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Oechtering, Tobias J.ORCID iD iconorcid.org/0000-0002-0036-9049
Alternative names
Publications (10 of 119) Show all publications
Cao, P., Oechtering, T. J. & Skoglund, M. (2018). Precoding Design for Massive MIMO Systems with Sub-connected Architecture and Per-antenna Power Constraints. In: : . Paper presented at The 22nd International ITG Workshop on Smart Antennas.
Open this publication in new window or tab >>Precoding Design for Massive MIMO Systems with Sub-connected Architecture and Per-antenna Power Constraints
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

This paper provides the necessary conditions to design precoding matrices for massive MIMO systems with a sub-connected architecture, RF power constraints and per-antenna power constraints. The system is configured such that each RFchain serves a group of antennas. The necessary condition to design the digital precoder is established based on a generalized water-filling and joint sum and per-antenna optimal power allocation solution, while the analog precoder is based on a per-antenna power allocation solution only. We study the analytically most interesting case where the power constraint on the RF chain is smaller than the sum of the corresponding per-antenna power constraints. For this, the optimal power is allocated based on two properties: Each RF chain uses full power and if the optimal power allocation of the unconstraint problem violates a per-antenna power constraint then it is optimal to allocate the maximal power for that antenna.

National Category
Engineering and Technology
Identifiers
urn:nbn:se:kth:diva-225420 (URN)
Conference
The 22nd International ITG Workshop on Smart Antennas
Note

QCR 20180411

Available from: 2018-04-04 Created: 2018-04-04 Last updated: 2018-04-11Bibliographically approved
Mochaourab, R. & Oechtering, T. J. (2018). Private Filtering for Hidden Markov Models. IEEE Signal Processing Letters, 25(6), 888-892
Open this publication in new window or tab >>Private Filtering for Hidden Markov Models
2018 (English)In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 25, no 6, p. 888-892Article in journal (Refereed) Published
Abstract [en]

Consider a hidden Markov model describing a system with two types of states: a monitored state and a private state. The two types of states are dependent and evolve jointly according to a Markov process with a stationary transition probability. It is desired to reveal the monitored states to a receiver but hide the private states. For this purpose, a privacy filter is necessary which suitably perturbs the monitored states before communication with the receiver. Our objective is to design the privacy filter to optimize the tradeoff between the monitoring accuracy and privacy, measured through a time-invariant distortion measure and Shannon's equivocation, respectively. As the optimal privacy filter is difficult to compute using the dynamic programming, we adopt a suboptimal greedy approach through which the privacy filter can be computed efficiently. Here, the greedy approach has the additional advantage of not being restricted to the finite time horizon setups. Simulations show the superiority of the approach compared to a privacy filter which only adds independent noise to the observations.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018
Keywords
Hidden Markov models, privacy, dynamic programming, greedy algorithm
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-230492 (URN)10.1109/LSP.2018.2827878 (DOI)000432451800005 ()2-s2.0-85045612606 (Scopus ID)
Note

QC 20180614

Available from: 2018-06-14 Created: 2018-06-14 Last updated: 2018-06-14Bibliographically approved
Mochaourab, R. & Oechtering, T. J. (2018). Private Filtering for Hidden Markov Models. IEEE Signal Processing Letters, 25(6), 888-892
Open this publication in new window or tab >>Private Filtering for Hidden Markov Models
2018 (English)In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 25, no 6, p. 888-892Article in journal (Refereed) Published
Abstract [en]

Consider a hidden Markov model describing a system with two types of states: a monitored state and a private state. The two types of states are dependent and evolve jointly according to a Markov process with a stationary transition probability. It is desired to reveal the monitored states to a receiver but hide the private states. For this purpose, a privacy filter is necessary which suitably perturbs the monitored states before communication with the receiver. Our objective is to design the privacy filter to optimize the tradeoff between the monitoring accuracy and privacy, measured through a time-invariant distortion measure and Shannon's equivocation, respectively. As the optimal privacy filter is difficult to compute using the dynamic programming, we adopt a suboptimal greedy approach through which the privacy filter can be computed efficiently. Here, the greedy approach has the additional advantage of not being restricted to the finite time horizon setups. Simulations show the superiority of the approach compared to a privacy filter which only adds independent noise to the observations.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2018
Keywords
Hidden Markov models, privacy, dynamic programming, greedy algorithm
National Category
Signal Processing
Identifiers
urn:nbn:se:kth:diva-230424 (URN)10.1109/LSP.2018.2827878 (DOI)000432451800005 ()2-s2.0-85045612606 (Scopus ID)
Note

QC 20180618

Available from: 2018-06-18 Created: 2018-06-18 Last updated: 2018-06-18Bibliographically approved
Vu, M. T., Oechtering, T. J. & Skoglund, M. (2017). Hierarchical identification with pre-processing. In: 2017 IEEE International Symposium on Information Theory (ISIT): . Paper presented at 2017 IEEE International Symposium on Information Theory, ISIT 2017, Aachen, Germany, 25 June 2017 through 30 June 2017 (pp. 2746-2750). Institute of Electrical and Electronics Engineers (IEEE), Article ID 8007029.
Open this publication in new window or tab >>Hierarchical identification with pre-processing
2017 (English)In: 2017 IEEE International Symposium on Information Theory (ISIT), Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 2746-2750, article id 8007029Conference paper, Published paper (Refereed)
Abstract [en]

We study a two-stage identification problem with pre-processing to enable efficient data retrieval and reconstruction. The first stage outputs a list of compatible users to the second stage which uses it to return the exact user identity with a corresponding reconstruction sequence. The rate distortion region is characterized. A connection to a two observer identification problem is also studied.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017
Series
IEEE International Symposium on Information Theory - Proceedings, ISSN 2157-8095
National Category
Engineering and Technology
Identifiers
urn:nbn:se:kth:diva-223032 (URN)10.1109/ISIT.2017.8007029 (DOI)000430345202163 ()2-s2.0-85034019095 (Scopus ID)9781509040964 (ISBN)
Conference
2017 IEEE International Symposium on Information Theory, ISIT 2017, Aachen, Germany, 25 June 2017 through 30 June 2017
Funder
Swedish Research Council, 2016-03853
Note

QC 20180221

Available from: 2018-02-21 Created: 2018-02-21 Last updated: 2018-05-14Bibliographically approved
Do, T. T., Ngo, H. Q., Duong, T. Q., Oechtering, T. & Skoglund, M. (2017). Massive MIMO Pilot Retransmission Strategies for Robustification Against Jamming. IEEE Wireless Communications Letters, 6(1), 58-61
Open this publication in new window or tab >>Massive MIMO Pilot Retransmission Strategies for Robustification Against Jamming
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2017 (English)In: IEEE Wireless Communications Letters, ISSN 2162-2337, E-ISSN 2162-2345, Vol. 6, no 1, p. 58-61Article in journal (Refereed) Published
Abstract [en]

This letter proposes anti-jamming strategies based on pilot retransmission for a single user uplink massive MIMO under jamming attack. A jammer is assumed to attack the system both in the training and data transmission phases. We first derive an achievable rate which enables us to analyze the effect of jamming attacks on the system performance. Counter-attack strategies are then proposed to mitigate this effect under two different scenarios: random and deterministic jamming attacks. Numerical results illustrate our analysis and benefit of the proposed schemes.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2017
Keywords
Massive MIMO, jamming, pilot retransmission
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-205139 (URN)10.1109/LWC.2016.2631163 (DOI)000395868700015 ()2-s2.0-85013651383 (Scopus ID)
Note

QC 20170412

Available from: 2017-04-12 Created: 2017-04-12 Last updated: 2017-11-29Bibliographically approved
Wiese, M. & Oechtering, T. J. (2017). Secure distributed estimation of linear systems. In: 2016 IEEE Conference on Communications and Network Security, CNS 2016: . Paper presented at 2016 IEEE Conference on Communications and Network Security, CNS 2016, 17 October 2016 through 19 October 2016 (pp. 616-620). Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>Secure distributed estimation of linear systems
2017 (English)In: 2016 IEEE Conference on Communications and Network Security, CNS 2016, Institute of Electrical and Electronics Engineers Inc. , 2017, p. 616-620Conference paper, Published paper (Refereed)
Abstract [en]

A two-dimensional linear time-invariant system is considered. The two dimensions of its states are observed by one sensor each. Every sensor quantizes its observations into a finite number of messages, using also the other sensor's past decisions. The combined sensor outputs should allow for a bounded estimation error (reliability). For a natural quantizer, we identify the cases where a price in the quantizer sum rate has to be paid for the fact that observations are distributed. At the same time, an eavesdropper should not be able to track the system state (security). Using the same quantizer as before, security is shown to be possible if the eavesdropper has less information than the estimator in that it for each sensor message pair obtains a larger set of indistinguishable message pairs.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2017
Keywords
Estimation, Invariance, Linear systems, Time varying control systems, Bounded estimation error, Combined sensors, Distributed estimation, Finite number, Linear time invariant systems, Message pairs, System state, Two-dimension, Network security
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-208001 (URN)10.1109/CNS.2016.7860559 (DOI)000402623000095 ()2-s2.0-85016060207 (Scopus ID)9781509030651 (ISBN)
Conference
2016 IEEE Conference on Communications and Network Security, CNS 2016, 17 October 2016 through 19 October 2016
Note

QC 2017-06-08

Available from: 2017-06-08 Created: 2017-06-08 Last updated: 2017-06-30Bibliographically approved
Do, T. T., Oechtering, T. J., Kim, S. M., Skoglund, M. & Peters, G. (2017). Uplink Waveform Channel With Imperfect Channel State Information and Finite Constellation Input. IEEE Transactions on Wireless Communications, 16(2), 1107-1119
Open this publication in new window or tab >>Uplink Waveform Channel With Imperfect Channel State Information and Finite Constellation Input
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2017 (English)In: IEEE Transactions on Wireless Communications, ISSN 1536-1276, E-ISSN 1558-2248, Vol. 16, no 2, p. 1107-1119Article in journal (Refereed) Published
Abstract [en]

This paper investigates the capacity limit of an uplink waveform channel assuming imperfect channel state information at the receiver (CSIR). Various realistic assumptions are incorporated into the problem, which make the study valuable for performance assessment of real cellular networks to identify potentials for performance improvements in practical receiver designs. We assume that the continuous-time received signal is first discretized by mismatched filtering based on the imperfect CSIR. The resulting discrete-time signals are then decoded considering two different decoding strategies, i.e., an optimal decoding strategy based on specific statistics of channel estimation errors and a sub-optimal decoding strategy treating the estimation error signal as additive Gaussian noise. Motivated by the proposed decoding strategies, we study the performance of the decision feedback equalizer for finite constellation inputs, in which inter-stream interferences are treated either using their true statistics or as Gaussian noise. Numerical results are provided to exemplify the benefit of exploiting the knowledge on the statistics of the channel estimation errors and inter-stream interferences. Simulations also assess the effect of the CSI imperfectness on the achievable rate, which reveal that finite constellation inputs are less sensitive to the estimation accuracy than Gaussian input, especially in the high SNR regime.

Place, publisher, year, edition, pages
IEEE Press, 2017
Keywords
Finite constellation input, imperfect CSI, mismatched filtering, uplink waveform channel
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-204102 (URN)10.1109/TWC.2016.2638420 (DOI)000395825200034 ()2-s2.0-85014906082 (Scopus ID)
Note

QC 20170329

Available from: 2017-03-29 Created: 2017-03-29 Last updated: 2017-11-29Bibliographically approved
Zaidi, A. A., Yüksel, S., Oechtering, T. & Skoglund, M. (2016). On the tightness of linear policies for stabilization of linear systems over Gaussian networks. Systems & control letters (Print), 88, 32-38
Open this publication in new window or tab >>On the tightness of linear policies for stabilization of linear systems over Gaussian networks
2016 (English)In: Systems & control letters (Print), ISSN 0167-6911, E-ISSN 1872-7956, Vol. 88, p. 32-38Article in journal (Refereed) Published
Abstract [en]

In this paper, we consider stabilization of multi-dimensional linear systems driven by Gaussian noise controlled over parallel Gaussian channels. For such systems, it has been recognized that for stabilization in the sense of asymptotic stationarity or stability in probability, Shannon capacity of a channel is an appropriate measure on characterizing whether a system can be made stable when controlled over the channel. However, this is in general not the case for quadratic stabilization. On a related problem of joint-source channel coding, in the information theory literature, the source-channel matching principle has been shown to lead to optimality of uncoded or analog transmission and when such matching conditions occur, it has been shown that capacity is also a relevant figure of merit for quadratic stabilization. A special case of this result is applicable to a scalar LQG system controlled over a scalar Gaussian channel. In this paper, we show that even in the absence of source-channel matching, to achieve quadratic stability, it may suffice that information capacity (in Shannon’s sense) is greater than the sum of the logarithm of unstable eigenvalue magnitudes. In particular, we show that periodic linear time varying coding policies are optimal in the sense of obtaining a finite second moment for the state of the system with minimum transmit power requirements for a large class of vector Gaussian channels. Our findings also extend the literature which has considered noise-free systems.

Place, publisher, year, edition, pages
Elsevier, 2016
Keywords
Channel capacity, Gaussian channels, Networked control systems, Stabilization
National Category
Signal Processing
Identifiers
urn:nbn:se:kth:diva-180930 (URN)10.1016/j.sysconle.2015.09.013 (DOI)000370101400004 ()2-s2.0-84951037908 (Scopus ID)
Note

QC 20160126

Available from: 2016-01-26 Created: 2016-01-25 Last updated: 2017-11-30Bibliographically approved
Cao, P., Oechtering, T., Schaefer, R. & Mikael, S. (2016). Optimal Transmit Strategy for MISO Channels with Joint Sum and Per-antenna Power Constraints [Letter to the editor]. IEEE Transactions on Signal Processing
Open this publication in new window or tab >>Optimal Transmit Strategy for MISO Channels with Joint Sum and Per-antenna Power Constraints
2016 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476Article in journal, Letter (Refereed) Published
Abstract [en]

In this paper, we study an optimal transmit strategy for multiple-input single-output (MISO) Gaussian channels with joint sum and per-antenna power constraints. We study in detail the interesting case where the sum of the per-antenna power constraints is larger than sum power constraint. A closed-form characterization of an optimal beamforming strategy is derived.It is shown that we can always find an optimal beamforming transmit strategy that allocates the maximal sum power with phases matched to the complex channel coefficients. The main result is a simple recursive algorithm to compute the optimal power allocation. Whenever the optimal power allocation of the corresponding problem with sum power constraint only exceeds per-antenna power constraints, it is optimal to allocate maximal per-antenna power to those antennas to satisfy the per-antenna power constraints. The remaining power is divided amongst the other antennas whose optimal allocation follows from a reduced joint sum and per-antenna power constraints problem of smaller channel coefficient dimension and reduced sum power constraint. Finally, the theoretical results are illustrated by numerical examples.

Place, publisher, year, edition, pages
IEEE, 2016
Keywords
Sum power constraint, per-antenna power constraint, MISO, beamforming, transmit strategy, transmission rate.
National Category
Engineering and Technology
Identifiers
urn:nbn:se:kth:diva-187650 (URN)10.1109/TSP.2016.2563382 (DOI)000380117400016 ()2-s2.0-84980395633 (Scopus ID)
External cooperation:
Note

QC 20160607

Available from: 2016-05-25 Created: 2016-05-25 Last updated: 2017-11-30Bibliographically approved
Li, Z., Oechtering, T. & Skoglund, M. (2016). Privacy-Preserving Energy Flow Control in Smart Grids. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2016: . Paper presented at IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2016, Shanghai, P. R. China, Mar. 20-25, 2016. IEEE
Open this publication in new window or tab >>Privacy-Preserving Energy Flow Control in Smart Grids
2016 (English)In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2016, IEEE , 2016Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, an energy flow control strategy to reduce the smart meter privacy leakage is studied. The considered smart grid is equipped with an energy storage device. The privacy leakage is modeled as optimal Bayesian detections on the behaviors of the consumer made by an authorized adversary. To evaluate the privacy risk, a Bayesian detection-operational privacy leakage metric is proposed. The design of an optimal privacy-preserving energy control strategy can be formulated as a belief state MDP problem. Therefore, standard methods and algorithms can be utilized to obtain or to approximate the optimal control strategy. A simplified problem to design an instantaneous optimal privacy-preserving control strategy is also considered. It is shown that the problem of the instantaneous optimal control strategy design can be formulated as a set of linear programmings.

Place, publisher, year, edition, pages
IEEE, 2016
National Category
Communication Systems Control Engineering Signal Processing
Identifiers
urn:nbn:se:kth:diva-179716 (URN)10.1109/ICASSP.2016.7472066 (DOI)000388373402067 ()2-s2.0-84973382871 (Scopus ID)
Conference
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2016, Shanghai, P. R. China, Mar. 20-25, 2016
Funder
Swedish Research Council, 2015-06815
Note

QC 20160401

Available from: 2015-12-21 Created: 2015-12-21 Last updated: 2017-01-24Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0002-0036-9049

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