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  • 1.
    Azari, Amin
    et al.
    KTH, School of Information and Communication Technology (ICT), Communication Systems, CoS, Radio Systems Laboratory (RS Lab).
    Miao, Guowang
    KTH, School of Information and Communication Technology (ICT), Communication Systems, CoS.
    Fundamental Tradeoffs in Resource Provisioning forIoT Services over Cellular Networks2017In: Proceedings of the 2017 IEEE International Conference on Communications, Institute of Electrical and Electronics Engineers (IEEE), 2017, article id 7996885Conference paper (Refereed)
    Abstract [en]

    Performance tradeoffs in resource provisioningfor mixed internet-of-things (IoT) and human-orientedcommunications(HoC) services over cellular networks are investigated.First, we present a low-complexity model of cellularconnectivity in the uplink direction in which both accessreservation and scheduled data transmission procedures areincluded. This model is employed subsequently in derivinganalytical expressions for energy efficiency, spectral efficiency,and experienced delay in data transmission of connected devicesas well as energy consumption of base stations. The derivedexpressions indicate that the choice of uplink resource provisioningstrategy introduces tradeoffs between battery lifetime forIoT communications, quality of service (QoS) for HoC, spectralefficiency and energy consumption for the access network. Then,the impacts of system and traffic parameters on the introducedtradeoffs are investigated. Performance analysis illustrates thatimproper resource provisioning for IoT traffic not only degradesQoS of high-priority services and decreases battery lifetime ofIoT devices, but also increases energy consumption of the accessnetwork. The presented analytical and simulations results figureout the ways in which spectral/energy efficiency for the accessnetwork and QoS for high-priority services could be traded toprolong battery lifetimes of connected devices by compromisingon the level of provisioned radio resources.

  • 2.
    Azari, Amin
    et al.
    KTH, School of Information and Communication Technology (ICT), Communication Systems, CoS, Radio Systems Laboratory (RS Lab). KTH, School of Information and Communication Technology (ICT), Centres, Center for Wireless Systems, Wireless@kth.
    Miao, Guowang
    KTH, School of Information and Communication Technology (ICT), Communication Systems, CoS.
    Network Life time Maximization for Cellular-Based M2M Networks2017In: IEEE Access, E-ISSN 2169-3536, Vol. 5, p. 18927-18940, article id 8045999Article in journal (Refereed)
    Abstract [en]

    High energy efficiency is critical for enabling massive machine-type communications (MTC) over cellular networks. This paper is devoted to energy consumption modeling, battery lifetime analysis, lifetime-aware scheduling, and transmit power control for massive MTC over cellular networks. We consider a realistic energy consumption model for MTC and model network battery-lifetime. Analytic expressions are derived to demonstrate the impact of scheduling on both the individual and network battery lifetimes. The derived expressions are subsequently employed in the uplink scheduling and transmit power control for mixed-priority MTC traffic in order to maximize the network lifetime. Besides the main solutions, low complexity solutions with limited feedback requirement are investigated, and the results are extended to existing LIE networks. In addition, the energy efficiency, spectral efficiency, and network lifetime tradeoffs in resource provisioning and scheduling for MTC over cellular networks are investigated. The simulation results show that the proposed solutions can provide substantial network lifetime improvement and network maintenance cost reduction in comparison with the existing scheduling schemes.

  • 3.
    Azari, Amin
    et al.
    KTH, School of Information and Communication Technology (ICT), Communication Systems, CoS, Radio Systems Laboratory (RS Lab).
    Miao, Guowang
    KTH, School of Information and Communication Technology (ICT), Communication Systems, CoS.
    Network Lifetime Maximization for Cellular-Based M2M Networks2017In: IEEE Access, E-ISSN 2169-3536Article in journal (Refereed)
    Abstract [en]

    High energy efficiency is critical for enabling massivemachine-type communications (MTC) over cellular networks.This work is devoted to energy consumption modeling,battery lifetime analysis, lifetime-aware scheduling and transmitpower control for massive MTC over cellular networks. Weconsider a realistic energy consumption model for MTC andmodel network battery-lifetime. Analytic expressions are derivedto demonstrate the impact of scheduling on both the individualand network battery lifetimes. The derived expressions aresubsequently employed in uplink scheduling and transmit powercontrol for mixed-priority MTC traffic in order to maximizethe network lifetime. Besides the main solutions, low-complexitysolutions with limited feedback requirement are investigated,and the results are extended to existing LTE networks. Also,the energy efficiency, spectral efficiency, and network lifetimetradeoffs in resource provisioning and scheduling for MTC overcellular networks are investigated. The simulation results showthat the proposed solutions can provide substantial networklifetime improvement and network maintenance cost reductionin comparison with the existing scheduling schemes.

  • 4.
    Azari, Amin
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Communication Systems, CoS, Radio Systems Laboratory (RS Lab).
    Miao, Guowang
    KTH, School of Electrical Engineering and Computer Science (EECS), Communication Systems, CoS, Radio Systems Laboratory (RS Lab).
    Stefanovic, Cedomir
    Aalborg University.
    Popovski, Petar
    Aalborg University.
    Latency-Energy Tradeoff based on Channel Scheduling and Repetitions in NB-IoT Systems2018In: 2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings, Institute of Electrical and Electronics Engineers (IEEE), 2018, article id 8648024Conference paper (Refereed)
    Abstract [en]

    Narrowband Internet of Things (NB-IoT) is the latest IoT connectivity solution presented by the 3rd generation partnership project (3GPP). NB-IoT introduces coverage classes and offers a significant link budget improvement by allowing repeated transmissions by nodes that experience high path loss. However, those repetitions necessarily increase the energy consumption and the latency in the whole NB-IoT system. The extent to which the whole system is affected depends on the scheduling of the uplink and downlink channels. We address this question, not treated previously, by developing a tractable model of NB-IoT access protocol operation, comprising message exchanges in random-access, control, and data channels, both in the uplink and downlink The model is then used to analyze the impact of channel scheduling as well as the interaction of coexisting coverage classes, through derivation of the expected latency and battery lifetime for each coverage class. These results are subsequently employed in investigation of latency-energy tradeoff in NB-IoT channel scheduling as well as determining the optimized operation points. Simulations results show validity of the analysis and confirm that channel scheduling and coexistence of coverage classes significantly affect latency and battery lifetime performance of NB-IoT devices.

  • 5.
    Chang, Peiliang
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Communication Systems, CoS, Radio Systems Laboratory (RS Lab).
    Miao, Guowang
    KTH, School of Electrical Engineering and Computer Science (EECS), Communication Systems, CoS, Radio Systems Laboratory (RS Lab).
    Energy-Efficient Resource Allocation in Multi-Carrier NOMA SystemsManuscript (preprint) (Other academic)
    Abstract [en]

    5G cellular networks are expected to support heterogeneous services with the same level of energy dissipation as current cellular networks. As a key enabler of 5G [1], the energy efficiency performance of non-orthogonal multiple access (NOMA) is of paramount importance. In NOMA systems, the system performance, e.g., spectral efficiency and energy efficiency are largely affected by resource allocation, i.e., sub-carrier assignment and power allocation. This paper studies the joint sub-carrier assignment and power allocation for the downlink transmission of multi-carrier NOMA systems to maximize the system energy efficiency (SEE). We first formulate an energyefficiency maximization problem while assuring the connectivity requirements of all users. The original optimization problem is a mixed integer programming problem and is NP hard. In order to develop optimal solutions with low complexity, the formulated problem is decomposed into three sub-problems: sub-carrier assignment, power allocation across sub-carriers and power allocation among users sharing the same sub-carrier. Given subcarrier assignment, we first obtain the optimal power allocation among users on one sub-carrier and then the optimal power allocation across sub-carriers. To find the optimal sub-carrier assignment, a greedy search solution based on the intrinsic structure of the transmitted power is proposed to minimize the overall required power to support the connectivity requirements of all users. Numerical simulations are implemented to validate the analytical findings. The results show that our proposed algorithms achieve better system energy efficiency and lower user blocking rate than the state-of-the-art solutions in the literature.

  • 6.
    Chang, Peiliang
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Communication Systems, CoS, Radio Systems Laboratory (RS Lab).
    Miao, Guowang
    KTH, School of Electrical Engineering and Computer Science (EECS), Communication Systems, CoS, Radio Systems Laboratory (RS Lab).
    Interference-aware Distributed Control of Cell Discontinuous Transmission2018In: 2018 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE (WCNC), IEEE , 2018Conference paper (Refereed)
    Abstract [en]

    As a main enabler for the next generation (5G) cellular networks, network densification faces challenges in intercell interference and energy consumption. Cell discontinuous transmission (DTX) can be employed to reduce both energy consumption of base stations (BSs) and inter-cell interference. In this paper we study the control problem of cell DTX in dense small cell networks (DSCNs). We firstly formulate the network energy efficiency optimization problem. Then a centralized heuristic DTX control algorithm is presented. In order to address the issues of complexity and scalability of the centralized solution, an interference-aware distributed DTX control algorithm is proposed. Discussions on algorithm complexity and implementation are provided. The proposed algorithms are evaluated with numerical simulations. Results show that at high load region, the proposed algorithms can not only enhance network capacity by reducing inter-cell interference by up to 60% but also increase network energy efficiency by switching BSs into micro-sleep mode by 67%.

  • 7.
    Chang, Peiliang
    et al.
    KTH, School of Information and Communication Technology (ICT), Communication Systems, CoS.
    Miao, Guowang
    KTH, School of Information and Communication Technology (ICT), Communication Systems, CoS.
    Joint Optimization of Base Station Deep-Sleep and DTX Micro-Sleep2016In: 2016 IEEE Globecom Workshops, GC Wkshps 2016 - Proceedings, IEEE conference proceedings, 2016, article id 7848943Conference paper (Refereed)
    Abstract [en]

    When both base station (BS) deep-sleep and discontinuous transmission (DTX) are applied to improve network energy efficiency (EE), switching BS into deep-sleep mode would increase the load of remaining active BSs and thereby reduce their energy savings via DTX. This paper studies the optimal BS operation strategy when both deep-sleep and DTX are employed. Queuing theory and stochastic geometry are jointly applied to model network performance with consideration of both traffic dynamics and channel quality variations. Analytical expressions of average BS load and network EE are derived. Both analytical and simulation results show that there is a trade-off between deep-sleep energy saving and DTX energy saving when the energy saving capacity of DTX is considerable. Analytical expression of the optimal percentage of deep-sleep BSs is provided.

  • 8.
    Chang, Peiliang
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Communication Systems, CoS, Radio Systems Laboratory (RS Lab).
    Miao, Guowang
    KTH, School of Electrical Engineering and Computer Science (EECS), Communication Systems, CoS, Radio Systems Laboratory (RS Lab).
    Optimal Operation of Base Stations With Deep Sleep and Discontinuous TransmissionIn: IEEE Transactions on Vehicular Technology, ISSN 0018-9545, E-ISSN 1939-9359Article in journal (Refereed)
    Abstract [en]

    Traffic-aware base station (BS) sleeping is a promising approach to increase the energy efficiency (EE) of cellular networks. Both deep sleep and discontinuous transmission (DTX)can be applied to improve network EE. This paper studies the optimal BS operation when both deep sleep and DTX are employed. Queuing theory and stochastic geometry theory are jointly applied to model network performance considering both traffic dynamics and stochastic channel quality. We firstly propose a scaling law of transmit power that assures network coverage. Then, we characterize the resource utilization of active BSs when various percent-ages of BSs are switched into deep sleep, and analyze the overload probability of the remaining active BSs. Finally, we investigate the impact of BS deep sleep and DTX micro sleep on network EE. Both analytical and simulation results show that there is a trade-off between deep sleep and DTX micro sleep. Switching BSs into deep sleep would increase the load of the remaining active BSs and reduce their energy saving achieved with DTX. When the power consumption of BS in DTX micro-sleep mode is considerably low, switching BSs into deep sleep might increase the overall energy consumption, and it is not always the best practice to switch as many BSs into deep sleep as possible to maximize network EE.

  • 9.
    Chang, Peiliang
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Communication Systems, CoS, Radio Systems Laboratory (RS Lab).
    Miao, Guowang
    KTH, School of Electrical Engineering and Computer Science (EECS), Communication Systems, CoS, Radio Systems Laboratory (RS Lab).
    Resource Provision for Energy-Efficient Mobile Edge Computing2018In: 2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings, IEEE Communications Society, 2018, article id 8648008Conference paper (Refereed)
    Abstract [en]

    Mobile Edge Computing (a.k.a Fog computing) is recently proposed to provide computing service for delay-sensitive mobile applications. Despite various benefits, deploying edge servers in cellular networks would increase their energy consumption. In this paper, we investigate the provision of resources, including both communication and computation resources, of Mobile Edge Computing (MEC) systems to improve their energy efficiency (EE). In a MEC system, both the communication subsystem, which allows mobile users to access Internet and offload their computing tasks, and the computation subsystem, which accomplishes the offloaded computing tasks, affect the service latency and consume energy. Modelling the whole system as tandem queues, we study the trade-offs between these two subsystems on energy consumption and service latency. Based on the analysis results, we propose an algorithm to determine the optimal provision of both communication and computation resources to minimize the overall energy consumption without sacrificing the performance on service latency. Numerical results are provided to validate our analytical findings.

  • 10.
    Kwon, Younggap
    et al.
    Yonsei Univ, Sch Elect & Elect Engn, Seoul 120749, South Korea.;Agcy Def Dev, Daejeon 305600, South Korea..
    Park, Hyunsung
    Yonsei Univ, Sch Elect & Elect Engn, Seoul 120749, South Korea..
    Oh, Jintaek
    Yonsei Univ, Sch Elect & Elect Engn, Seoul 120749, South Korea..
    Miao, Guowang
    KTH, School of Electrical Engineering and Computer Science (EECS), Communication Systems, CoS, Radio Systems Laboratory (RS Lab).
    Hwang, Taewon
    Yonsei Univ, Sch Elect & Elect Engn, Seoul 120749, South Korea..
    Energy-Efficient Routing and Link Adaptation for 2D Wireless Relay Networks in the Wideband Regime2018In: IEEE Transactions on Wireless Communications, ISSN 1536-1276, E-ISSN 1558-2248, Vol. 17, no 11, p. 7325-7339Article in journal (Refereed)
    Abstract [en]

    We discuss the globally optimal energy-efficient design of a 2D relay network. Different from the existing routing protocols on energy saving, which finds the minimal energy route for a given data rate, the proposed algorithm jointly optimizes routing and data rate to maximize energy efficiency (EE) defined as the achievable data rate per power consumption. We propose a low-complexity algorithm to circumvent the huge complexity of the exhaustive search for the network EE maximization and prove its global optimality. Moreover, the proposed algorithm is implemented in a distributed fashion because each relay needs to send its routing information only to the relays in its adjacent tiers, which significantly reduces the signaling overhead of the centralized implementation. Our analysis on the worst-case complexity in a fading channel shows that the complexity of the proposed algorithm increases linearly while that of the exhaustive search increases exponentially as the tier index increases. Simulation results confirm that the proposed algorithm outperforms the existing routing protocols on energy saving and achieves the globally optimal network EE at a significantly lower complexity than the exhaustive search.

  • 11.
    Wang, Yuanshuang
    et al.
    KTH, School of Information and Communication Technology (ICT). China Elect Technol Grp Corp, Res Inst 28, Nanjing, Jiangsu, Peoples R China.;Roy.
    Liu, Junjun
    China Elect Technol Grp Corp, Res Inst 28, Nanjing, Jiangsu, Peoples R China..
    Miao, Guowang
    KTH, School of Information and Communication Technology (ICT).
    Decentralized Cross-Layer Optimization for Energy-Efficient Resource Allocation in HetNets2018In: 2018 INTERNATIONAL CONFERENCE ON CYBER-ENABLED DISTRIBUTED COMPUTING AND KNOWLEDGE DISCOVERY (CYBERC 2018), IEEE , 2018, p. 470-474Conference paper (Refereed)
    Abstract [en]

    In this paper, we develop a joint physical and medium access control (MAC) layer optimization (JPMO) scheme based on game theory for energy-efficient resource allocation and interference management in heterogeneous networks (HetNets). In HetNets, cross-tier interference and co-tier interference significantly limit the network performance. Moreover, when each user only has its own channel state information and chooses its transmission policy independently without any coordination mechanism, it will result in the network collapse or waste of channel resources. To maximize the network efficiency, we develop the JPMO scheme through pricing mechanism from the perspective of game theory. Then we transform this scheme into a two-stage Stackelberg game, in which macrocell determines the transmission policy in MAC layer first, and then SCs perform EE power allocation in physical layer. Simulation results validate the effectiveness of the proposed scheme.

  • 12. Wu, Jian
    et al.
    Bao, Yanan
    Miao, Guowang
    KTH, School of Information and Communication Technology (ICT), Communication Systems, CoS.
    Zhou, Sheng
    Niu, Zhisheng
    Base-Station Sleeping Control and Power Matching for Energy–Delay Tradeoffs With Bursty Traffic2016In: IEEE Transactions on Vehicular Technology, ISSN 0018-9545, E-ISSN 1939-9359Article in journal (Refereed)
    Abstract [en]

    In this paper, we study sleeping control (SC) and power matching (PM) for a single cell in cellular networks with bursty traffic. The base station (BS) sleeps whenever the system is empty and wakes up when N users are assembled during the sleep period. The service capacity of the BS in the active mode is controlled by adjusting its transmit power. The total power consumption and average delay are analyzed, and based on this, the impact of parameter N and transmit power on the energy-delay tradeoff is studied. It is shown that, given the average traffic load, the more bursty the traffic is, the less total power consumed, although the delay performance of more bursty traffic is better only under certain circumstances. The optimal energy-delay tradeoff is then obtained through joint SC and PM optimization. The relationship between the optimal control parameters and the asymptotic performance are also provided. Moreover, the influence of the traffic autocorrelation is explored, which shows less impact on the system performance compared with that of the burstiness. Numerical results show the energy saving gain of the joint SC and PM scheme, as well as the impact of burstiness on the optimal energy-delay tradeoff.

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