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  • 1.
    B. da Silva Jr., Jose Mairton
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Network and Systems engineering.
    Ghauch, Hadi
    KTH, School of Electrical Engineering and Computer Science (EECS), Network and Systems engineering.
    Fodor, Gabor
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
    Fischione, Carlo
    KTH, School of Electrical Engineering and Computer Science (EECS), Network and Systems engineering.
    How to Split UL/DL Antennas in Full-DuplexCellular Networks2018In: IEEE International Conference on Communication (ICC’18): ThirdWorkshop on Full-Duplex Communications for Future Wireless Networks, Kansas City, MO, USA: IEEE Communications Society, 2018Conference paper (Refereed)
    Abstract [en]

    To further improve the potential of full-duplex com-munications, networks may employ multiple antennas at thebase station or user equipment. To this end, networks thatemploy current radios usually deal with self-interference andmulti-user interference by beamforming techniques. Althoughprevious works investigated beamforming design to improvespectral efficiency, the fundamental question of how to split theantennas at a base station between uplink and downlink infull-duplex networks has not been investigated rigorously. Thispaper addresses this question by posing antenna splitting as abinary nonlinear optimization problem to minimize the sum meansquared error of the received data symbols. It is shown that thisis an NP-hard problem. This combinatorial problem is dealt withby equivalent formulations, iterative convex approximations, anda binary relaxation. The proposed algorithm is guaranteed toconverge to a stationary solution of the relaxed problem with muchsmaller complexity than exhaustive search. Numerical resultsindicate that the proposed solution is close to the optimal in bothhigh and low self-interference capable scenarios, while the usuallyassumed antenna splitting is far from optimal. For large numberof antennas, a simple antenna splitting is close to the proposedsolution. This reveals that the importance of antenna splittingdiminishes with the number of antennas.

  • 2.
    B. da Silva Jr., Jose Mairton
    et al.
    KTH, School of Electrical Engineering (EES), Network and Systems engineering.
    Ghauch, Hadi
    Fodor, Gabor
    Skoglund, Mikael
    KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.
    Fischione, Carlo
    KTH, School of Electrical Engineering and Computer Science (EECS), Network and Systems engineering.
    Smart Antenna Assignment is Essential in Full-Duplex Communications2019In: IEEE Transactions on Communications, ISSN 0090-6778, E-ISSN 1558-0857Article in journal (Refereed)
    Abstract [en]

    Full-duplex communications have the potential to almost double the spectralefficiency. To realize such a potentiality, the signal separation at base station’s antennasplays an essential role. This paper addresses the fundamentals of such separationby proposing a new smart antenna architecture that allows every antenna to beeither shared or separated between uplink and downlink transmissions. The benefitsof such architecture are investigated by an assignment problem to optimally assignantennas, beamforming and power to maximize the weighted sum spectral efficiency.We propose a near-to-optimal solution using block coordinate descent that divides theproblem into assignment problems, which are NP-hard, a beamforming and powerallocation problems. The optimal solutions for the beamforming and power allocationare established while near-to-optimal solutions to the assignment problems are derivedby semidefinite relaxation. Numerical results indicate that the proposed solution isclose to the optimum, and it maintains a similar performance for high and low residualself-interference powers. With respect to the usually assumed antenna separationtechnique and half-duplex transmission, the sum spectral efficiency gains increase withthe number of antennas. We conclude that our proposed smart antenna assignment forsignal separation is essential to realize the benefits of multiple antenna full-duplexcommunications.

  • 3.
    Barros da Silva Jr., José Mairton
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Network and Systems engineering. Royal Inst Technol, KTH, Stockholm, Sweden..
    Ghauch, Hadi
    KTH, School of Electrical Engineering and Computer Science (EECS), Network and Systems engineering.
    Fodor, Gabor
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
    Fischione, Carlo
    KTH, School of Electrical Engineering and Computer Science (EECS), Network and Systems engineering.
    How to Split UL/DL Antennas in Full-Duplex Cellular Networks2018In: 2018 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS WORKSHOPS (ICC WORKSHOPS), IEEE, 2018Conference paper (Refereed)
    Abstract [en]

    To further improve the potential of full-duplex communications, networks may employ multiple antennas at the base station or user equipment. To this end, networks that employ current radios usually deal with self-interference and multi-user interference by beamforming techniques. Although previous works investigated beamforming design to improve spectral efficiency, the fundamental question of how to split the antennas at a base station between uplink and downlink in full-duplex networks has not been investigated rigorously. This paper addresses this question by posing antenna splitting as a binary nonlinear optimization problem to minimize the sum mean squared error of the received data symbols. It is shown that this is an NP-hard problem. This combinatorial problem is dealt with by equivalent formulations, iterative convex approximations, and a binary relaxation. The proposed algorithm is guaranteed to converge to a stationary solution of the relaxed problem with much smaller complexity than exhaustive search. Numerical results indicate that the proposed solution is close to the optimal in both high and low self-interference capable scenarios, while the usually assumed antenna splitting is far from optimal. For large number of antennas, a simple antenna splitting is close to the proposed solution. This reveals that the importance of antenna splitting diminishes with the number of antennas.

  • 4. Chan, Wai Ming
    et al.
    Kim, Taejoon
    Ghauch, Hadi
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Bengtsson, Mats
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Subspace Estimation and Hybrid Precoding for Wideband Millimeter-Wave MIMO Systems2016In: 2016 50th Asilomar Conference on Signals, Systems and Computers, IEEE Computer Society, 2016, p. 286-290, article id 7869043Conference paper (Refereed)
    Abstract [en]

    There has been growing interest in millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems, which would likely employ hybrid analog-digital precoding with large-scale analog arrays deployed at wide bandwidths. Primary challenges here are how to efficiently estimate the large-dimensional frequency-selective channels and customize the wideband hybrid analog-digital precoders and combiners. To address these challenges, we propose a low-overhead channel subspace estimation technique for the wideband hybrid analog-digital MIMO precoding systems. We first show that the Gram matrix of the frequency-selective channel can be decomposed into frequency-flat and frequency-selective components. Based on this, the Arnoldi approach, leveraging channel reciprocity and time-reversed echoing, is employed to estimate a frequency-flat approximation of the frequency-selective mmWave channels, which is used to design the analog parts. After the analog precoder and combiner design, the low-dimensional frequency-selective channels are estimated using conventional pilot-based channel sounding. Numerical results show that considerable improvement in data-rate performance is possible.

  • 5.
    Farhadi, Hamed
    et al.
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Ghauch, Hadi
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Skoglund, Mikael
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Pilot-assisted opportunistic user scheduling for wireless multi-cell networks2015In: IEEE International Conference on Communications, IEEE , 2015Conference paper (Refereed)
    Abstract [en]

    We consider downlink transmission in multi-cell wireless networks where in each cell one base station is serving multiple mobile terminals. There is no a priori channel state information (CSI) available at base stations and mobile terminals. We propose a low-complexity pilot-assisted opportunistic user scheduling (PAOUS) scheme. The proposed scheme operates in four subsequent phases: channel training; feedback transmission; user scheduling; and data transmission. We deploy an orthogonal pilot-assisted channel training scheme for acquiring CST at mobile terminals. Consequently, each mobile terminal obtains a noisy estimation of the corresponding local CST (i.e. channel gains from base stations to the mobile terminal). Then, it makes a local decision based on the estimated channel gains of the interfering links (i.e. the links between base stations in neighboring cells and the mobile terminal) and sends a one-bit feedback signal to the base station of the corresponding cell. Each base station schedules one mobile terminal for communication. We compute the achievable rate region and the achievable degrees of freedom (DoF) of the proposed transmission scheme. Our results show that in a multi-cell network with K base stations and coherence time T, the total DoF K-opt (1 - K-opt/T) is achievable given that the number of mobile terminals in each cell scales proportional to signal-to-noise-ratio. Since limited radio resources are available, only a subset of base stations should be activated, where the optimum number of active base stations is K-opt = min {K, T/2}. This recommends that in large networks (K > T/2), select only a subset of the base stations to be active and perform the PAOUS scheme within the cells associated to these base stations. Our results reveal that, even with single antenna at base stations and no a priori CSI at terminals, a non-trivial DoF gain can be achieved. We also investigate the power allocation between channel training and data transmission phases. Our study shows that in large networks (many base stations) more power should be allocated to channel training while in dense networks (many mobile terminals in each cell) more power should be allocated for data transmission.

  • 6.
    Ghauch, Hadi
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Optimization techniques for future cellular systems: Harnessing the gains from higher frequencies, increased spectral efficiency, and densification2016Doctoral thesis, monograph (Other academic)
  • 7.
    Ghauch, Hadi
    et al.
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Bengtsson, Mats
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Kim, Taejoon
    City University of Hong Kong.
    Skoglund, Mikael
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Subspace Estimation and Decomposition for Hybrid Analog-Digital Millimetre-Wave MIMO systems2015In: 2015 IEEE 16th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), IEEE , 2015, p. 395-399Conference paper (Refereed)
    Abstract [en]

    In this work, we address the problem of channel estimation and precoding / combining for the so-called hybrid millimeter wave (mmWave) MIMO architecture. Our proposed channel estimation scheme exploits channel reciprocity in TDD MIMO systems, by using echoing, thereby allowing us to implement Krylov subspace methods in a fully distributed way. The latter results in estimating the right (resp. left) singular subspace of the channel at the transmitter (resp. receiver). Moreover, we also tackle the problem of subspace decomposition whereby the estimated right (resp. left) singular subspaces are approximated by a cascade of analog and digital precoder (resp. combiner), using an iterative method. Finally we compare our scheme with an equivalent fully digital case and conclude that a relatively similar performance can be achieved, however, with a drastically reduced number of RF chains - 4 ~ 8 times less (i.e., massive savings in cost and power consumption).

  • 8.
    Ghauch, Hadi
    et al.
    KTH, School of Electrical Engineering (EES), Information Science and Engineering. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Imtiaz, Sahar
    KTH, School of Electrical Engineering (EES), Information Science and Engineering. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Skoglund, Mikael
    KTH, School of Electrical Engineering (EES), Information Science and Engineering. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Koudouridis, George
    Gross, James
    KTH, School of Electrical Engineering (EES), Information Science and Engineering. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Fairness and User Assignment in Cloud-RAN2017In: 2017 IEEE 86TH VEHICULAR TECHNOLOGY CONFERENCE (VTC-FALL), 2017Conference paper (Refereed)
    Abstract [en]

    In this paper, we extend our previous work on user assignment in Cloud-RAN, where we proposed an algorithm for user assignment (UA). We motivate the inherent fairness issue that is present in the latter UA scheme, since some users in the system will never get served. To improve the fairness, we propose that the UA scheme is preceded by a user scheduling step which aims at selecting at any time the users that should be considered by the UA algorithm for scheduling (in the next time slot). Two user scheduling approaches have been studied. The first scheme improves the minimum throughput (MT), by selecting at any time the users with the lowest throughput. The second scheme is based on round-robin (RR) scheduling, where the set of potentially scheduled users for the next slot, is done by excluding all the previously served users, in that round. Moreover, the subset of actual users to be served, is determined using the UA algorithm. We evaluate their fairness and sumrate performance, via extensive simulations. While one might have expected a tradeoff between the sum-rate performance and fairness, our results show that MT improves both metrics, when compared to the original UA algorithm (without fairness), for some choice of parameter values. This implies that both fairness and aggregate system performance can be improved, by a careful choice of the number of assigned and served users.

  • 9.
    Ghauch, Hadi
    et al.
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Communication Theory.
    Kim, Taejoon
    City University of Hong Kong.
    Bengtsson, Mats
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Skoglund, Mikael
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Distributed Low-Overhead Schemes for Multi-stream MIMO Interference Channels2015In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 63, no 7, p. 1737-1749Article in journal (Refereed)
    Abstract [en]

    Our aim in this work is to propose fully distributed schemes for transmit and receive filter optimization. The novelty of the proposed schemes is that they only require a few forward-backward iterations, thus causing minimal communication overhead. For that purpose, we relax the well-known leakage minimization problem, and then propose two different filter update structures to solve the resulting non-convex problem: though one leads to conventional full-rank filters, the other results in rank-deficient filters, that we exploit to gradually reduce the transmit and receive filter rank, and greatly speed up the convergence. Furthermore, inspired from the decoding of turbo codes, we propose a turbo-like structure to the algorithms, where a separate inner optimization loop is run at each receiver (in addition to the main forward-backward iteration). In that sense, the introduction of this turbo-like structure converts the communication overhead required by conventional methods to computational overhead at each receiver (a cheap resource), allowing us to achieve the desired performance, under a minimal overhead constraint. Finally, we show through comprehensive simulations that both proposed schemes hugely outperform the relevant benchmarks, especially for large system dimensions.

  • 10.
    Ghauch, Hadi
    et al.
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Kim, Taejoon
    Bengtsson, Mats
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Skoglund, Mikael
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Interference Alignment via Controlled Perturbations2013In: 2013 IEEE Global Communications Conference (GLOBECOM), IEEE , 2013, p. 3996-4001Conference paper (Refereed)
    Abstract [en]

    In this work, we study the so-called leakage minimization problem, within the context of interference alignment (IA). For that purpose, we propose a novel approach based on controlled perturbations of the leakage function, and show how the latter can be used as a mechanism to control the algorithm's convergence (and thus tradeoff convergence speed for reliability). Although the proposed scheme falls under the broad category of stochastic optimization, we show through simulations that it has a quasi-deterministic convergence that we exploit to improve on the worst case performance of its predecessor, resulting in significantly better sum-rate capacity and average cost function value.

  • 11.
    Ghauch, Hadi
    et al.
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Kim, Taejoon
    Bengtsson, Mats
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Skoglund, Mikael
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Subspace Estimation and Decomposition for Large Millimeter-Wave MIMO Systems2016In: IEEE Journal on Selected Topics in Signal Processing, ISSN 1932-4553, E-ISSN 1941-0484, Vol. 10, no 3, p. 528-542Article in journal (Refereed)
    Abstract [en]

    Channel estimation and precoding in hybrid analog-digital millimeter-wave (mmWave) MIMO systems is a fundamental problem that has yet to be addressed, before any of the promised gains can be harnessed. For that matter, we propose a method (based on the well-known Arnoldi iteration) exploiting channel reciprocity in TDD systems and the sparsity of the channel's eigenmodes, to estimate the right (resp. left) singular subspaces of the channel, at the BS (resp. MS). We first describe the algorithm in the context of conventional MIMO systems, and derive bounds on the estimation error in the presence of distortions at both BS and MS. We later identify obstacles that hinder the application of such an algorithm to the hybrid analog-digital architecture, and address them individually. In view of fulfilling the constraints imposed by the hybrid analog-digital architecture, we further propose an iterative algorithm for subspace decomposition, whereby the above estimated subspaces, are approximated by a cascade of analog and digital precoder/combiner. Finally, we evaluate the performance of our scheme against the perfect CSI, fully digital case (i.e., an equivalent conventional MIMO system), and conclude that similar performance can be achieved, especially at medium-to-high SNR (where the performance gap is less than 5%), however, with a drastically lower number of RF chains (similar to 4 to 8 times less).

  • 12.
    Ghauch, Hadi
    et al.
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. KTH, School of Electrical Engineering (EES), Network and Systems engineering.
    Kim, Taejoon
    Bengtsson, Mats
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. KTH, School of Electrical Engineering (EES), Information Science and Engineering.
    Skoglund, Mikael
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. KTH, School of Electrical Engineering (EES), Information Science and Engineering.
    Sum-Rate Maximization in Sub-28-GHz Millimeter-Wave MIMO Interfering Networks2017In: IEEE Journal on Selected Areas in Communications, ISSN 0733-8716, E-ISSN 1558-0008, Vol. 35, no 7, p. 1649-1662Article in journal (Refereed)
    Abstract [en]

    MIMO systems in the lower part of the millimetre-wave (mmWave) spectrum band (i.e., below 28 GHz) do not exhibit enough directivity and selectively, as compared to their counterparts in higher bands of the spectrum (i.e., above 60 GHz), and thus still suffer from the detrimental effect of interference, on the system sum rate. As such systems exhibit large numbers of antennas and short coherence times for the channel, traditional methods of distributed coordination are ill-suited, and the resulting communication overhead would offset the gains of coordination. In this paper, we propose algorithms for tackling the sum-rate maximization problem that are designed to address the above-mentioned limitations. We derive a lower bound on the sum rate, a so-called difference of log and trace (DLT) bound, shed light on its tightness, and highlight its decoupled nature at both the transmitters and receivers. Moreover, we derive the solution to each of the subproblems that we dub non-homogeneous waterfilling (a variation on the MIMO waterfilling solution), and underline an inherent desirable feature: its ability to turn-OFF streams exhibiting low SINR, and contribute to greatly speeding up the convergence of the proposed algorithm. We then show the convergence of the resulting algorithm, max-DLT, to a stationary point of the DLT bound. Finally, we rely on extensive simulations of various network configurations, to establish the fast-converging nature of our proposed schemes, and thus their suitability for addressing the short coherence interval, as well as the increased system dimensions, arising when managing interference in lower bands of the mmWave spectrum. Moreover, our results suggest that interference management still brings about significant performance gains, especially in dense deployments.

  • 13.
    Ghauch, Hadi
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Network and Systems Engineering.
    Kim, Taejoon
    Univ Kansas, Dept EECS, Lawrence, KS 66045 USA..
    Fischione, Carlo
    KTH, School of Electrical Engineering and Computer Science (EECS), Network and Systems Engineering.
    Skoglund, Mikael
    KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.
    Compressive Sensing with Applications to Millimeter-wave Architectures2019In: 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), IEEE , 2019, p. 7834-7838Conference paper (Refereed)
    Abstract [en]

    To make the system available at low-cost, millimeter-ave (mmWave) multiple-input multiple-output (MIMO) architectures employ analog arrays, which are driven by a limited number of radio frequency (RF) chains. One primary challenge of using large hybrid analog-digital arrays is that the digital baseband cannot directly access the signal to/from each antenna. To address this limitation, recent research has focused on retransmissions, iterative precoding, and subspace decomposition methods. Unlike these approaches that exploited the channel's low-rank, in this work we exploit the sparsity of the received signal at both the transmit/receive antennas. While the signal itself is de facto dense, it is well-known that most signals are sparse under an appropriate choice of basis. By delving into the structured compressive sensing (CS) framework and adapting them to variants of the mmWave hybrid architectures, we provide methodologies to recover the analog signal at each antenna from the (low-dimensional) digital signal. Moreover, we characterizes the minimal numbers of measurement and RF chains to provide this recovery, with high probability. We discuss their applications to common variants of the hybrid architecture. By leveraging the inherent sparsity of the received signal, our analysis reveals that a hybrid MIMO system can be " turned into" a fully digital one: the number of needed RF chains increases logarithmically with the number of antennas.

  • 14.
    Ghauch, Hadi
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Network and Systems Engineering.
    Kim, Taejoon
    KTH, School of Electrical Engineering and Computer Science (EECS). City University of Hong Kong.
    Skoglund, Mikael
    KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.
    Fischione, Carlo
    KTH, School of Electrical Engineering and Computer Science (EECS), Network and Systems Engineering.
    Low-Overhead Coordination in Sub-28 Millimeter-Wave Networks2018In: 2018 IEEE International Conference on Communications (ICC), 2018Conference paper (Refereed)
  • 15.
    Ghauch, Hadi
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.
    Mahboob Ur Rahman, Muhammad
    KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.
    Imtiaz, Sahar
    KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.
    Qvarfordt, Christer
    Huawei.
    Skoglund, Mikael
    KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.
    Gross, James
    KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.
    User Assignment in C-RAN Systems: Algorithms and BoundsIn: Article in journal (Refereed)
  • 16.
    Ghauch, Hadi
    et al.
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Mochaourab, Rami
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Bengtsson, Mats
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Skoglund, Mikael
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Distributed precoding and user selection in MIMO interfering networks2015In: 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015, IEEE conference proceedings, 2015, p. 461-464Conference paper (Refereed)
    Abstract [en]

    In this work we shed light on the problem of precoding and user selection in MIMO networks. We formulate the problem using the framework of stable matching, whereby a set of users wish to be matched to a set of serving base stations, such as to maximize the sum-rate performance of the system. Though the problem is NP-hard, we propose a suboptimal heuristic that tackles the problem in a distributed fashion: we apply a many-to-one stable matching algorithm to generate a sequence of matchings, and the Weighted MMSE algorithm to perform the precoding. We benchmark our algorithm againt the recently proposed Weighted MMSE with User Assignment algorithm [1].

  • 17.
    Ghauch, Hadi
    et al.
    Information Networking Institute, Carnegie Mellon University.
    Papadias, Constantinos
    Broadband Wireless & Network Sensors Lab, Athens Information Technology.
    Interference Alignment: A one-sided approach2011Conference paper (Refereed)
    Abstract [en]

    Interference Alignment (IA) is the process of designing signals in such a way that they cast overlapping shadows at their unintended receivers, while remaining distinguishable at the intended ones [1]. Our goal in this paper is to come up with an algorithm for IA that runs at the transmitters only (and is transparent to the receivers), that doesn’t require channel reciprocity, and thus alleviates the need to alternate between the forward and reverse network as is the case in [2], thereby saving significant overhead in certain environments where the channel changes frequently. Most importantly, our effort is focused on ensuring that this one-sided approach does not degrade the performance of the system w.r.t. [2]. As a first step, we mathematically express the interference in each receiver’s desired signal as a function of the transmitters’ beamforming vectors. We then propose a simple steepest descent (SD) algorithm and use it to minimize the interference in each receiver’s desired signal space. We mathematically establish equivalences between our approach and the Distributed IA algorithm presented in [2] and show that our algorithm also converges to an alignment solution (when the solution is feasible).

  • 18.
    Ghauch, Hadi
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Network and Systems Engineering.
    Rahman, Muhammad Mahboob Ur
    KTH. Informat Technol Univ, Dept Elect Engn, Lahore 54000, Pakistan..
    Imtiaz, Sahar
    KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.
    Qvarfordt, Christer
    Huawei Technol, S-16440 Kista, Sweden..
    Skoglund, Mikael
    KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.
    Gross, James
    KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.
    User Assignment in C-RAN Systems: Algorithms and Bounds2018In: IEEE Transactions on Wireless Communications, ISSN 1536-1276, E-ISSN 1558-2248, Vol. 17, no 6, p. 3889-3902Article in journal (Refereed)
    Abstract [en]

    In this paper, we investigate the problem of mitigating interference between so-called antenna domains of a cloud radio access network (C-RAN). In contrast to previous work, we turn to an approach utilizing primarily the optimal assignment of users to central processors in a C-RAN deployment. We formulate this user assignment problem as an integer optimization problem and propose an iterative algorithm for obtaining a solution. Motivated by the lack of optimality guarantees on such solutions, we opt to find lower bounds on the problem and the resulting interference leakage in the network. We thus derive the corresponding Dantzig-Wolfe decomposition, formulate the dual problem, and show that the former offers a tighter bound than the latter. We highlight the fact that the bounds in question consist of linear problems with an exponential number of variables and adapt the column generation method for solving them. In addition to shedding light on the tightness of the bounds in question, our numerical results show significant sum-rate gains over several comparison schemes. Moreover, the proposed scheme delivers similar performance as weighted minimum mean squared-error (MMSE) with a significantly lower complexity (around 10 times less).

  • 19.
    Ghauch, Hadi
    et al.
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Ur Rahman, Mahboob
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Imtiaz, Sahar
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Gross, James
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Coordination and Antenna Domain Formation in Cloud-RAN Systems2016In: 2016 IEEE International Conference on Communications, ICC 2016, Institute of Electrical and Electronics Engineers (IEEE), 2016, article id 7511264Conference paper (Refereed)
    Abstract [en]

    We study here the problem of Antenna Domain Formation (ADF) in cloud RAN systems, whereby multiple remote radio-heads (RRHs) are each to be assigned to a set of antenna domains (ADs), such that the total interference between the ADs is minimized. We formulate the corresponding optimization problem, by introducing the concept of interference coupling coefficients among pairs of radio-heads. We then propose a low-overhead algorithm that allows the problem to be solved in a distributed fashion, among the aggregation nodes (ANs), and establish basic convergence results. Moreover, we also propose a simple relaxation to the problem, thus enabling us to characterize its maximum performance. We follow a layered coordination structure: after the ADs are formed, radio-heads are clustered to perform coordinated beamforming using the well known Weighted-MMSE algorithm. Finally, our simulations show that using the proposed ADF mechanism would significantly increase the sum-rate of the system (with respect to random assignment of radio-heads).

  • 20. He, Jiguang
    et al.
    Kim, Taejoon
    Ghauch, Hadi
    Liu, Kunpeng
    Wang, Guangjian
    Millimeter Wave MIMO Channel Tracking Systems2014In: Globecom Workshops, 2014, IEEE conference proceedings, 2014Conference paper (Refereed)
    Abstract [en]

    We consider channel/subspace tracking systems for temporally correlated millimeter wave (e.g., E-band) multiple-input multiple-output (MIMO) channels. Our focus is given to the tracking algorithm in the non-line-of-sight (NLoS) environment, where the transmitter and the receiver are equipped with hybrid analog/digital precoder and combiner, respectively. In the absence of straightforward time-correlated channel model in the millimeter wave MIMO literature, we present a temporal MIMO channel evolution model for NLoS millimeter wave scenarios. Considering that conventional MIMO channel tracking algorithms in microwave bands are not directly applicable, we propose a new channel tracking technique based on sequentially updating the precoder and combiner. Numerical results demonstrate the superior channel tracking ability of the proposed technique over independent sounding approach in the presented channel model and the spatial channel model (SCM) adopted in 3GPP specification.

  • 21.
    Imtiaz, Sahar
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.
    Ghauch, Hadi
    KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.
    Koudouridis, George
    Gross, James
    KTH, School of Electrical Engineering (EES), Information Science and Engineering.
    Random Forests Resource Allocation for 5G Systems: Performance and Robustness Study2018Conference paper (Refereed)
  • 22.
    Imtiaz, Sahar
    et al.
    KTH, School of Electrical Engineering (EES), Information Science and Engineering.
    Ghauch, Hadi
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Ur Rahman, M. M.
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Koudouridis, Georgios
    KTH, School of Electrical Engineering (EES), Communication Networks. Huawei Technologies Sweden R&D Center.
    Gross, James
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Learning-based resource allocation scheme for TDD-based 5G CRAN system2016In: MSWiM 2016 - Proceedings of the 19th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, ACM Press, 2016, p. 176-185Conference paper (Refereed)
    Abstract [en]

    Provision of high data rates with always-onconnectivity to high mobility users is one of the motivations for design of fifth generation (5G) systems. High system capacity can be achieved by coordination between large number of antennas, which is done using the cloud radio access network (CRAN) design in 5G systems. In terms of baseband processing, allocation of appropriate resources to the users is necessary to achieve high system capacity, for which the state of the art uses the users' channel state information (CSI); however, they do not take into account the associated overhead, which poses a major bottleneck for the effective system performance. In contrast to this approach, this paper proposes the use of machine learning for allocating resources to high mobility users using only their position estimates. Specifically, the 'random forest' algorithm, a supervised machine learning technique, is used to design a learning-based resource allocation scheme by exploiting the relationships between the system parameters and the users' position estimates. In this way, the overhead for CSI acquisition is avoided by using the position estimates instead, with better spectrum utilization. While the initial numerical investigations, with minimum number of users in the system, show that the proposed learning-based scheme achieves 86% of the efficiency achieved by the perfect CSI-based scheme, if the effect of overhead is factored in, the proposed scheme performs better than the CSI-based approach. In a realistic scenario, with multiple users in the system, the significant increase in overhead for the CSI-based scheme leads to a performance gain of 100%, or more, by using the proposed scheme, and thus proving the proposed scheme to be more efficient in terms of system performance.

  • 23.
    Imtiaz, Sahar
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.
    Koudouridis, Georgios P.
    Ghauch, Hadi
    KTH, School of Electrical Engineering and Computer Science (EECS), Network and Systems Engineering.
    Gross, James
    KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.
    Random forests for resource allocation in 5G cloud radio access networks based on position information2018In: EURASIP Journal on Wireless Communications and Networking, ISSN 1687-1472, E-ISSN 1687-1499, Vol. 2018, no 1, article id 142Article in journal (Refereed)
    Abstract [en]

    Next generation 5G cellular networks are envisioned to accommodate an unprecedented massive amount of Internet of things (IoT) and user devices while providing high aggregate multi-user sum rates and low latencies. To this end, cloud radio access networks (CRAN), which operate at short radio frames and coordinate dense sets of spatially distributed radio heads, have been proposed. However, coordination of spatially and temporally denser resources for larger sets of user population implies considerable resource allocation complexity and significant system signalling overhead when associated with channel state information (CSI)-based resource allocation (RA) schemes. In this paper, we propose a novel solution that utilizes random forests as supervised machine learning approach to determine the resource allocation in multi-antenna CRAN systems based primarily on the position information of user terminals. Our simulation studies show that the proposed learning based RA scheme performs comparably to a CSI-based scheme in terms of spectral efficiency and is a promising approach to master the complexity in future cellular networks. When taking the system overhead into account, the proposed learning-based RA scheme, which utilizes position information, outperforms legacy CSI-based scheme by up to 100%. The most important factor influencing the performance of the proposed learning-based RA scheme is antenna orientation randomness and position inaccuracies. While the proposed random forests scheme is robust against position inaccuracies and changes in the propagation scenario, we complement our scheme with three approaches that restore most of the original performance when facing random antenna orientations of the user terminal.

  • 24.
    Mochaourab, Rami
    et al.
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Brandt, Rasmus
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Ghauch, Hadi
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Bengtsson, Mats
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Overhead-Aware Distributed CSI Selection in the MIMO Interference Channel2015In: 2015 23rd European Signal Processing Conference, EUSIPCO 2015, Institute of Electrical and Electronics Engineers (IEEE), 2015, p. 1038-1042, article id 7362541Conference paper (Refereed)
    Abstract [en]

    We consider a MEMO interference channel in which the transmitters and receivers operate in frequency-division duplex mode. In this setting, interference management through coordinated transceiver design necessitates channel state information at the transmitters (CSI-T). The acquisition of CSI T is done through feedback from the receivers, which entitles a loss in degrees of freedom, due to training and feedback. This loss increases with the amount of CSI-T. In this work, after formulating an overhead model for CSI acquisition at the transmitters, we propose a distributed mechanism to find for each transmitter a subset of the complete CSI, which is used to perform interference management. The mechanism is based on many-to-many stable matching. We prove the existence of a stable matching and exploit an algorithm to reach it. Simulation results show performance improvement compared to full and minimal CSI-T.

  • 25.
    Rahman, Muhammad Mahboob Ur
    et al.
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Ghauch, Hadi
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Imtiaz, Sahar
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Gross, James
    KTH, School of Electrical Engineering (EES), Communication Theory.
    RRH Clustering and Transmit Precoding for Interference-limited 5G CRAN Downlink2015Conference paper (Refereed)
  • 26.
    Shokri-Ghadikolaei, Hossein
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Network and Systems Engineering.
    Ghauch, Hadi
    KTH, School of Electrical Engineering and Computer Science (EECS), Network and Systems Engineering.
    Fischione, Carlo
    KTH, School of Electrical Engineering and Computer Science (EECS), Network and Systems Engineering.
    Learning-based tracking of AoAs and AoDs in mmWave networks2018In: Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM, Association for Computing Machinery , 2018, p. 45-50Conference paper (Refereed)
    Abstract [en]

    This paper considers a millimeter-wave communication system and proposes an efficient channel estimation scheme with a minimum number of pilots. We model the dynamics of the channel’s second-order statistics by a Markov process and develop a learning framework to obtain these dynamics from an unlabeled set of measured angles of arrival and departure. We then find the optimal precoding and combining vectors for pilot signals. Using these vectors, the transmitter and receiver will sequentially estimate the corresponding angles of departure and arrival, and then refine the pilot precoding and combining vectors to minimize the error of estimating the channel gains.

  • 27.
    Shokri-Ghadikolaei, Hossein
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Network and Systems Engineering.
    Ghauch, Hadi
    KTH, School of Electrical Engineering and Computer Science (EECS), Network and Systems Engineering. COMELEC Department, Telecom ParisTech, Paris, France.
    Fischione, Carlo
    KTH, School of Electrical Engineering and Computer Science (EECS), Network and Systems Engineering.
    Skoglund, Mikael
    KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.
    Learning and Data Selection in Big Datasets2019In: Proceedings of the 36th International Conference on MachineLearning, Long Beach, California, PMLR 97, 2019., 2019Conference paper (Refereed)
    Abstract [en]

    Finding a dataset of minimal cardinality to characterize the optimal parameters of a model is of paramount importance in machine learning and distributed optimization over a network. This paper investigates the compressibility of large datasets. More specifically, we propose a framework that jointly learns the input-output mapping as well as the most representative samples of the dataset (sufficient dataset). Our analytical results show that the cardinality of the sufficient dataset increases sub-linearly with respect to the original dataset size. Numerical evaluations of real datasets reveal a large compressibility, up to 95%, without a noticeable drop in the learnability performance, measured by the generalization error.

  • 28.
    Tolli, Antti
    et al.
    Univ Oulu, Ctr Wireless Commun, Oulu, Finland..
    Ghauch, Hadi
    KTH, School of Electrical Engineering and Computer Science (EECS), Network and Systems engineering.
    Kaleva, Jarkko
    Univ Oulu, Ctr Wireless Commun, Oulu, Finland..
    Komulainen, Petri
    Mediatek Wireless, Oulu, Finland..
    Bengtsson, Mats
    KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.
    Skoglund, Mikael
    KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.
    Honig, Michael
    Northwestern Univ, EECS Dept, Evanston, IL 60208 USA..
    Lahetkangas, Eeva
    Nokia Bell Labs, Murray Hill, NJ USA..
    Tiirola, Esa
    Nokia Bell Labs, Murray Hill, NJ USA..
    Pajukoski, Kari
    Nokia Bell Labs, Murray Hill, NJ USA..
    Distributed Coordinated Transmission with Forward-Backward Training for 5G Radio Access2019In: IEEE Communications Magazine, ISSN 0163-6804, E-ISSN 1558-1896, Vol. 57, no 1, p. 58-64Article in journal (Refereed)
    Abstract [en]

    CoMP transmission and reception have been considered in cellular networks for enabling larger coverage, improved rates, and interference mitigation. To harness the gains of coordinated beamforming, fast information exchange over a backhaul connecting the cooperating BSs is required. In practice, the bandwidth and delay limitations of the backhaul may not be able to meet such stringent demands. These impairments motivate the study of cooperative approaches based only on local CSI that require minimal or no information exchange between the BSs. To this end, several distributed approaches are introduced for CB-CoMP. The proposed methods rely on the channel reciprocity and iterative spatially precoded over-the-air pilot signaling. We elaborate how F-B training facilitates distributed CB by allowing BSs and UEs to iteratively optimize their respective transmitters/receivers based on only locally measured CSI. The trade-off due to the overhead from the F-B iterations is discussed. We also consider the challenge of dynamic TDD where the UE-UE channel knowledge cannot be acquired at the BSs by exploiting channel reciprocity. Finally, standardization activities and practical requirements for enabling the proposed F-B training schemes in 5G radio access are discussed.

1 - 28 of 28
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