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Mutual information (MI) is an important information theoretic concept which has many applications in telecommunications, in blind source separation, and in machine learning. More recently, it has been also employed for the instrumental assessment of speech intelligibility where traditionally correlation based measures are used. In this paper, we address the difference between MI and correlation from the viewpoint of discovering dependencies between variables in the context of speech signals. We perform our investigation by considering the linear predictive approximation and the extrapolation of speech signals as examples. We compare a parametric MI estimation approach based on a Gaussian mixture model (GMM) with the k-nearest neighbor (KNN) approach which is a well-known non-parametric method available to estimate the MI. We show that the GMM-based MI estimator leads to more consistent results. © 2020 Sprachkommunikation - 10. ITG-Fachtagung. All rights reserved., QC 20201202

Millimeter wave (mmWave) communication has attracted increasing attention as a promising technology for 5G networks. One of the key architectural features of mmWave is the use of massive antenna arrays at both the transmitter and the receiver sides. Therefore, by employing directional beamforming (BF), both mmWave base stations (MBSs) and mmWave users (MUEs) are capable of supporting multi-beam simultaneous transmissions. However, most researches have only considered a single beam, which means that they do not make full potential of mmWave. In this context, in order to improve the performance of short-range indoor mmWave networks with multiple reflections, we investigate the challenges and potential solutions of downlink multi-user multi-beam transmission, which can be described as a high-dimensional (i.e., beamspace) multi-user multiple-input multiple-output (MU-MIMO) technique, including multi-user BF training, simultaneous users' grouping, and multi-user multi-beam power allocation. Furthermore, we present the theoretical and numerical results to demonstrate that beamspace MU-MIMO compared with single beam transmission can largely improve the rate performance of mmWave systems., QC 20220927

Multi-beam concurrent transmission is one of promising solutions for a millimeter-wave (mmWave) network to provide seamless handover, robustness to blockage, and continuous connectivity. Nevertheless, one of the major obstacles in multi-beam concurrent transmissions is the optimization of beam pair selection, which is essential to improve the mmWave network performance. Therefore, in this paper, we propose a novel heterogeneous multi-beam cloud radio access network (HMBCRAN) architecture which provides seamless mobility and coverage for mmWave networks. We also design a novel acquirement method for candidate beam pair links (BPLs) in HMBCRANs architecture, which reduces user power consumption, signaling overhead, and overall time consumption. Based on HMBCRANs architecture and the resulted candidate BPLs for each user equipment, a beam pair selection optimization problem aiming at maximizing network sum rate is formulated. To find the solution efficiently, the considered problem is reformulated as a non-operative game with local interaction, which only needs local information exchanging among players. A decentralized algorithm based on HMBCRANs architecture and binary loglinear learning is proposed to obtain the optimal pure strategy Nash equilibrium of the proposed game, in which a concurrent multi-player selection scheme and an information exchanging protocol among players are developed to reduce the complexity and signal overheads. The stability, optimality, and complexity of the proposed algorithm are analyzed via theoretical and simulation method. The results prove that the proposed scheme has better convergence speed and sum rate against the state-ofthe- art schemes., QC 20181214

Wireless self-backhauling is a promising solution for dense millimeter wave (mmWave) small cell networks, the system efficiency of which, however, depends upon the balance of resources between the backhaul link and access links of each small cell. In this paper, we address the discrete power control and non-unified transmission duration allocation problem for self-backhauling mmWave cellular networks, in which each small cell is allowed to adopt individual transmission duration allocation ratio according to its own channel and load conditions. We first formulate the considered problem as a non-cooperative game G with a common utility function. We prove the feasibility and existence of the pure strategy Nash equilibrium (NE) of game G under some mild conditions. Then, we design a centralized resource allocation algorithm based on the best response dynamic and a decentralized resource allocation algorithm (DRA) based on control-plane/user-plane split architecture and loglinear learning to obtain a feasible pure strategy NE of game G. For speeding up convergence and reducing signaling overheads, we reformulate the considered problem as a non-cooperative game G' with local interaction, in which only local information exchange is required. Based on DRA, we design a concurrent DRA to obtain the best feasible pure strategy NE of game G'. Furthermore, we extend the proposed algorithms to the discrete power control and unified transmission duration allocation optimization problem. Extensive simulations are conducted with different system configurations to demonstrate the convergence and effectiveness of the proposed algorithms., QC 20180130

Using higher frequency bands (e.g., millimeter waves) to provide higher data rate is an effective way to eliminate performance bottleneck for future wireless networks, particularly for cellular networks based high-speed railway (HSR) wireless communication systems. However, higher frequency bands suffer from significant path loss and narrow-beam coverage, which pose serious challenges in cellular networks, especially under the HSR scenario. Meanwhile, as one of the key performance indexes of ultrareliable and low-latency communications in 5G systems, network reliability should be guaranteed to provide steady reliable data transmission along the railway, especially when safety-critical railway signaling information is delivered. In this paper, we propose a novel beamforming scheme, namely, optimal nonuniform steady mmWave beamforming, to guarantee the network reliability under an interleaved redundant coverage architecture for future HSR wireless systems. Moreover, we develop a bisection-based beam boundary determination (BBBD) method to determine the service area of each predefined RF beam. Finally, we demonstrate that the proposed optimal nonuniform steady mmWave beamforming can provide steady reliable data transmissions along the railway, and the network reliability requirements can be guaranteed when the proposed BBBD method is used. We expect that our optimal nonuniform steady mmWave beamforming provides a promising solution for future HSR wireless systems., QC 20180531

This paper studies beam training design for a codebook-based beamforming millimeter wave (mmwave) system where multiple antenna arrays are employed and each array is capable of beamforming independently. To reduce the training overhead and the complexity of subsequent beam direction search, we propose a cooperative multi-subarray beam training method. Specifically, from the perspective of excluding non-effective beam direction combinations and thus reducing search space, method and criterion of beam superposition are proposed to construct a wide beam from multiple narrow beams corresponding to multiple subarrays. Then, a cooperative multi-subarray beam training scheme is proposed based on the proposed criterion. Finally, simulation results show that the proposed scheme achieves a spectral efficiency close to that of the optimal exhaustive search scheme, while has greatly reduced training overhead and computational complexity., QC 20180507

This paper presents a coding theorem for linear coding over finite rings, in the setting of the Slepian-Wolf source coding problem. This theorem covers corresponding achievability theorems of Elias (IRE Conv. Rec. 1955, 3, 37-46) and Csiszár (IEEE Trans. Inf. Theory 1982, 28, 585-592) for linear coding over finite fields as special cases. In addition, it is shown that, for any set of finite correlated discrete memoryless sources, there always exists a sequence of linear encoders over some finite non-field rings which achieves the data compression limit, the Slepian-Wolf region. Hence, the optimality problem regarding linear coding over finite non-field rings for data compression is closed with positive confirmation with respect to existence. For application, we address the problem of source coding for computing, where the decoder is interested in recovering a discrete function of the data generated and independently encoded by several correlated i.i.d. random sources. We propose linear coding over finite rings as an alternative solution to this problem. Results in Körner-Marton (IEEE Trans. Inf. Theory 1979, 25, 219-221) and Ahlswede-Han (IEEE Trans. Inf. Theory 1983, 29, 396-411, Theorem 10) are generalized to cases for encoding (pseudo) nomographic functions (over rings). Since a discrete function with a finite domain always admits a nomographic presentation, we conclude that both generalizations universally apply for encoding all discrete functions of finite domains. Based on these, we demonstrate that linear coding over finite rings strictly outperforms its field counterpart in terms of achieving better coding rates and reducing the required alphabet sizes of the encoders for encoding infinitely many discrete functions., QC 20171201

In this paper, we investigate the performance of the wiretap Rayleigh fading channel in the presence of statistical delay constraints. We invoke tools from stochastic network calculus to derive probabilistic bounds on the delay. This method requires a statistical characterization of the wiretap fading service process, which we derive in closed form. We then validate these analytical bounds via simulations. Interestingly, the analysis of the wiretap fading channel reveals close structural similarities with the interference channel in terms of service process characterization, which is derived in our prior work. In our numerical evaluations, we show that the delay performance of the wiretap fading channel is in particular sensitive to bursty arrival processes due to the high variance of the service process., QC 20170519

In this paper, we investigate the performance of the wiretap Rayleigh fading channel in the presence of statistical delay constraints. We invoke tools from stochastic network calculus to derive probabilistic bounds on the delay. This method requires a statistical characterization of the wiretap fading service process, which we derive in closed form. We then validate these analytical bounds via simulations. Interestingly, the analysis of the wiretap fading channel reveals close structural similarities with the interference channel in terms of service process characterization, which is derived in our prior work. In our numerical evaluations, we show that the delay performance of the wiretap fading channel is in particular sensitive to bursty arrival processes due to the high variance of the service process., QC 20170519

In this paper, we investigate the performance of the wiretap Rayleigh fading channel in the presence of statistical delay constraints. We invoke tools from stochastic network calculus to derive probabilistic bounds on the delay. This method requires a statistical characterization of the wiretap fading service process, which we derive in closed form. We then validate these analytical bounds via simulations. Interestingly, the analysis of the wiretap fading channel reveals close structural similarities with the interference channel in terms of service process characterization, which is derived in our prior work. In our numerical evaluations, we show that the delay performance of the wiretap fading channel is in particular sensitive to bursty arrival processes due to the high variance of the service process., QC 20170519

In this paper, we investigate the performance of the wiretap Rayleigh fading channel in the presence of statistical delay constraints. We invoke tools from stochastic network calculus to derive probabilistic bounds on the delay. This method requires a statistical characterization of the wiretap fading service process, which we derive in closed form. We then validate these analytical bounds via simulations. Interestingly, the analysis of the wiretap fading channel reveals close structural similarities with the interference channel in terms of service process characterization, which is derived in our prior work. In our numerical evaluations, we show that the delay performance of the wiretap fading channel is in particular sensitive to bursty arrival processes due to the high variance of the service process., QC 20170519

In this paper, we investigate the performance of the wiretap Rayleigh fading channel in the presence of statistical delay constraints. We invoke tools from stochastic network calculus to derive probabilistic bounds on the delay. This method requires a statistical characterization of the wiretap fading service process, which we derive in closed form. We then validate these analytical bounds via simulations. Interestingly, the analysis of the wiretap fading channel reveals close structural similarities with the interference channel in terms of service process characterization, which is derived in our prior work. In our numerical evaluations, we show that the delay performance of the wiretap fading channel is in particular sensitive to bursty arrival processes due to the high variance of the service process., QC 20170519

The lifetime of wireless sensor networks (WSNs) can be substantially extended by transferring energy wirelessly to the sensor nodes. In this poster, a wireless energy transfer (WET) enabled WSN is presented, where a base station transfers energy wirelessly to the sensor nodes that are deployed in several regions of interest, to supply them with energy to sense and to upload data. The WSN lifetime can be extended by deploying redundant sensor nodes, which allows the implementation of duty-cycling mechanisms to reduce nodes' energy consumption. In this context, a problem on sensor node deployment naturally arises, where one needs to determine how many sensor nodes to deploy in each region such that the total number of nodes is minimized, and the WSN is immortal. The problem is formulated as an integer optimization, whose solution is challenging due to the binary decision variables and a non-linear constraint. A greedy-based algorithm is proposed to achieve the optimal solution of such deployment problem. It is argued that such scheme can be used in monitoring systems in smart cities, such as smart buildings and water lines., QC 20170116

The lifetime of wireless sensor networks (WSNs) can be substantially extended by transferring energy wirelessly to the sensor nodes. In this poster, a wireless energy transfer (WET) enabled WSN is presented, where a base station transfers energy wirelessly to the sensor nodes that are deployed in several regions of interest, to supply them with energy to sense and to upload data. The WSN lifetime can be extended by deploying redundant sensor nodes, which allows the implementation of duty-cycling mechanisms to reduce nodes' energy consumption. In this context, a problem on sensor node deployment naturally arises, where one needs to determine how many sensor nodes to deploy in each region such that the total number of nodes is minimized, and the WSN is immortal. The problem is formulated as an integer optimization, whose solution is challenging due to the binary decision variables and a non-linear constraint. A greedy-based algorithm is proposed to achieve the optimal solution of such deployment problem. It is argued that such scheme can be used in monitoring systems in smart cities, such as smart buildings and water lines., QC 20170116

The lifetime of wireless sensor networks (WSNs) can be substantially extended by transferring energy wirelessly to the sensor nodes. In this poster, a wireless energy transfer (WET) enabled WSN is presented, where a base station transfers energy wirelessly to the sensor nodes that are deployed in several regions of interest, to supply them with energy to sense and to upload data. The WSN lifetime can be extended by deploying redundant sensor nodes, which allows the implementation of duty-cycling mechanisms to reduce nodes' energy consumption. In this context, a problem on sensor node deployment naturally arises, where one needs to determine how many sensor nodes to deploy in each region such that the total number of nodes is minimized, and the WSN is immortal. The problem is formulated as an integer optimization, whose solution is challenging due to the binary decision variables and a non-linear constraint. A greedy-based algorithm is proposed to achieve the optimal solution of such deployment problem. It is argued that such scheme can be used in monitoring systems in smart cities, such as smart buildings and water lines., QC 20170116

The lifetime of wireless sensor networks (WSNs) can be substantially extended by transferring energy wirelessly to the sensor nodes. In this poster, a wireless energy transfer (WET) enabled WSN is presented, where a base station transfers energy wirelessly to the sensor nodes that are deployed in several regions of interest, to supply them with energy to sense and to upload data. The WSN lifetime can be extended by deploying redundant sensor nodes, which allows the implementation of duty-cycling mechanisms to reduce nodes' energy consumption. In this context, a problem on sensor node deployment naturally arises, where one needs to determine how many sensor nodes to deploy in each region such that the total number of nodes is minimized, and the WSN is immortal. The problem is formulated as an integer optimization, whose solution is challenging due to the binary decision variables and a non-linear constraint. A greedy-based algorithm is proposed to achieve the optimal solution of such deployment problem. It is argued that such scheme can be used in monitoring systems in smart cities, such as smart buildings and water lines., QC 20170116

The lifetime of wireless sensor networks (WSNs) can be substantially extended by transferring energy wirelessly to the sensor nodes. In this poster, a wireless energy transfer (WET) enabled WSN is presented, where a base station transfers energy wirelessly to the sensor nodes that are deployed in several regions of interest, to supply them with energy to sense and to upload data. The WSN lifetime can be extended by deploying redundant sensor nodes, which allows the implementation of duty-cycling mechanisms to reduce nodes' energy consumption. In this context, a problem on sensor node deployment naturally arises, where one needs to determine how many sensor nodes to deploy in each region such that the total number of nodes is minimized, and the WSN is immortal. The problem is formulated as an integer optimization, whose solution is challenging due to the binary decision variables and a non-linear constraint. A greedy-based algorithm is proposed to achieve the optimal solution of such deployment problem. It is argued that such scheme can be used in monitoring systems in smart cities, such as smart buildings and water lines., QC 20170116

In this paper, the privacy leakage problem in an eavesdropped parallel distributed binary hypothesis test network is considered. A novel Neyman–Pearson test-operational privacy leakage measure is proposed and a privacy-constrained distributed Neyman–Pearson test problem is formulated. Such privacy-constrained distributed Neyman–Pearson test network is designed to optimize the Neyman–Pearson test performance and meanwhile to satisfy a desired suppression constraint on the privacy leakage. This study characterizes the privacy-constrained distributed Neyman–Pearson test network design and particularly identifies the sufficiency of deterministic likelihood-ratio test for optimality. These results help to simplify the optimal design problem of a privacy-constrained distributed Neyman–Pearson test network. Numerical results are presented to show the trade-off between the test performance and privacy leakage in privacy-constrained distributed Neyman–Pearson test networks., QC 20170308

In this paper, the privacy leakage problem in an eavesdropped parallel distributed binary hypothesis test network is considered. A novel Neyman–Pearson test-operational privacy leakage measure is proposed and a privacy-constrained distributed Neyman–Pearson test problem is formulated. Such privacy-constrained distributed Neyman–Pearson test network is designed to optimize the Neyman–Pearson test performance and meanwhile to satisfy a desired suppression constraint on the privacy leakage. This study characterizes the privacy-constrained distributed Neyman–Pearson test network design and particularly identifies the sufficiency of deterministic likelihood-ratio test for optimality. These results help to simplify the optimal design problem of a privacy-constrained distributed Neyman–Pearson test network. Numerical results are presented to show the trade-off between the test performance and privacy leakage in privacy-constrained distributed Neyman–Pearson test networks., QC 20170308

In this paper, the privacy leakage problem in an eavesdropped parallel distributed binary hypothesis test network is considered. A novel Neyman–Pearson test-operational privacy leakage measure is proposed and a privacy-constrained distributed Neyman–Pearson test problem is formulated. Such privacy-constrained distributed Neyman–Pearson test network is designed to optimize the Neyman–Pearson test performance and meanwhile to satisfy a desired suppression constraint on the privacy leakage. This study characterizes the privacy-constrained distributed Neyman–Pearson test network design and particularly identifies the sufficiency of deterministic likelihood-ratio test for optimality. These results help to simplify the optimal design problem of a privacy-constrained distributed Neyman–Pearson test network. Numerical results are presented to show the trade-off between the test performance and privacy leakage in privacy-constrained distributed Neyman–Pearson test networks., QC 20170308

In this paper, the privacy leakage problem in an eavesdropped parallel distributed binary hypothesis test network is considered. A novel Neyman–Pearson test-operational privacy leakage measure is proposed and a privacy-constrained distributed Neyman–Pearson test problem is formulated. Such privacy-constrained distributed Neyman–Pearson test network is designed to optimize the Neyman–Pearson test performance and meanwhile to satisfy a desired suppression constraint on the privacy leakage. This study characterizes the privacy-constrained distributed Neyman–Pearson test network design and particularly identifies the sufficiency of deterministic likelihood-ratio test for optimality. These results help to simplify the optimal design problem of a privacy-constrained distributed Neyman–Pearson test network. Numerical results are presented to show the trade-off between the test performance and privacy leakage in privacy-constrained distributed Neyman–Pearson test networks., QC 20170308

In this paper, the privacy leakage problem in an eavesdropped parallel distributed binary hypothesis test network is considered. A novel Neyman–Pearson test-operational privacy leakage measure is proposed and a privacy-constrained distributed Neyman–Pearson test problem is formulated. Such privacy-constrained distributed Neyman–Pearson test network is designed to optimize the Neyman–Pearson test performance and meanwhile to satisfy a desired suppression constraint on the privacy leakage. This study characterizes the privacy-constrained distributed Neyman–Pearson test network design and particularly identifies the sufficiency of deterministic likelihood-ratio test for optimality. These results help to simplify the optimal design problem of a privacy-constrained distributed Neyman–Pearson test network. Numerical results are presented to show the trade-off between the test performance and privacy leakage in privacy-constrained distributed Neyman–Pearson test networks., QC 20170308

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., QC 2017-06-08

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., QC 20170329

In cyberphysical systems, a relevant problem is assigning agents to slots by distributed decisions capable to preserve agent's privacy. For example, in future intelligent transportation systems, city-level coordinators may optimally assign cars (the agents) to parking slots depending on the cars' distance to final destinations so to ensuring social fairness and without disclosing or even using the car's destination information. Unfortunately, these assignment problems are combinatorial, whereas traditional solvers are exponentially complex, are not scalable, and do not ensure privacy of the agents' intended destinations. Moreover, no emphasis is placed to optimise the agents' social benefit. In this paper, the aggregate social benefit of the agents is considered by an agent-slot assignment optimization problem whose objective function is the fairness among the agents. Due to the problem's complexity, the problem is solved by an approximate approach based on Lagrange duality theory that allows to develop an iterative semi-distributed algorithm. It is shown that the proposed algorithm is gracefully scalable compared to centralised methods, and that it preserves privacy in the sense that an eavesdropper will not be able to discover the destination of any agent during the algorithm iterations. Numerical results illustrate the performance and trade-off of the proposed algorithm compared to the ideal optimal assignment and a greedy method., QC 20170217

This paper focuses on the problem of reconstructing low-rank matrices from underdetermined measurements using alternating optimization strategies. We endeavour to combine an alternating least-squares based estimation strategy with ideas from the alternating direction method of multipliers (ADMM) to recover low-rank matrices with linear parameterized structures, such as Hankel matrices. The use of ADMM helps to improve the estimate in each iteration due to its capability of incorporating information about the direction of estimates achieved in previous iterations. We show that merging these two alternating strategies leads to a better performance and less consumed time than the existing alternating least squares (ALS) strategy. The improved performance is verified via numerical simulations with varying sampling rates and real applications., QC 20160202. QC 20160304

Wireless capsule endoscopy (WCE) is an emerging technique to enhance Gastroenterologists information about the patient's gastrointestinal (G.I.) tract. Localization of capsule inside human body in this case is an active area of research. This can be thought of as a sub-domain of micro and bio-robotics fields. If capsule and micro-robot localization problem in human body is solved, then it may potentially lead to less invasive treatments for G.I. diseases and other micro-robot assisted medical procedures. Several approaches have been investigated by the researchers to estimate capsule location. The proposed solutions are mainly static and thus prone to the changes in the propagation medium. We propose an adaptive algorithm based on expectation maximization technique for capsule localization. The proposed algorithm adaptively updates the estimated location based on the received radio frequency (RF) signal measurements., QC 20161207

This paper studies the asymptotic performance of multi-hop amplify-and-forward relay multiple-antenna communication channels. Each multi-antenna relay terminal in the considered network amplifies the received signal, sent by a source, and retransmits it upstream toward a destination. Achievable ergodic rates of the relay channel with both jointly optimal detection and decoding and practical separate-decoding receiver architectures for arbitrary signaling schemes, along with average bit error rates for various types of detectors are derived in the regime where the number of antennas at each terminal grows large without a bound. To overcome the difficulty of averaging over channel realizations, we apply a large-system analysis based on the replica method from statistical physics. The validity of the large-system analysis is further verified through Monte Carlo simulations of realistic finite-sized systems., QC 20160905

Low-rank matrix reconstruction (LRMR) problem considersestimation (or reconstruction) of an underlying low-rank matrixfrom under-sampled linear measurements. A low-rank matrix can be represented using a factorized model. In thisarticle, we derive Bayesian Cramer-Rao bounds for LRMR where a factorized model is used. We first show a general informative bound, and then derive several Bayesian Cramer-Rao bounds for different scenarios. We always considered the low-rank matrix to be reconstructed as a random matrix, but its model hyper-parameters for three cases - deterministic known, deterministic unknown and random. Finally we compare the bounds with existing practical algorithms through numerical simulations., QC 20160810

Low-rank matrix reconstruction (LRMR) problem considersestimation (or reconstruction) of an underlying low-rank matrixfrom under-sampled linear measurements. A low-rank matrix can be represented using a factorized model. In thisarticle, we derive Bayesian Cramer-Rao bounds for LRMR where a factorized model is used. We first show a general informative bound, and then derive several Bayesian Cramer-Rao bounds for different scenarios. We always considered the low-rank matrix to be reconstructed as a random matrix, but its model hyper-parameters for three cases - deterministic known, deterministic unknown and random. Finally we compare the bounds with existing practical algorithms through numerical simulations., QC 20160810

Low-rank matrix reconstruction (LRMR) problem considersestimation (or reconstruction) of an underlying low-rank matrixfrom under-sampled linear measurements. A low-rank matrix can be represented using a factorized model. In thisarticle, we derive Bayesian Cramer-Rao bounds for LRMR where a factorized model is used. We first show a general informative bound, and then derive several Bayesian Cramer-Rao bounds for different scenarios. We always considered the low-rank matrix to be reconstructed as a random matrix, but its model hyper-parameters for three cases - deterministic known, deterministic unknown and random. Finally we compare the bounds with existing practical algorithms through numerical simulations., QC 20160810

Low-rank matrix reconstruction (LRMR) problem considersestimation (or reconstruction) of an underlying low-rank matrixfrom under-sampled linear measurements. A low-rank matrix can be represented using a factorized model. In thisarticle, we derive Bayesian Cramer-Rao bounds for LRMR where a factorized model is used. We first show a general informative bound, and then derive several Bayesian Cramer-Rao bounds for different scenarios. We always considered the low-rank matrix to be reconstructed as a random matrix, but its model hyper-parameters for three cases - deterministic known, deterministic unknown and random. Finally we compare the bounds with existing practical algorithms through numerical simulations., QC 20160810

Low-rank matrix reconstruction (LRMR) problem considersestimation (or reconstruction) of an underlying low-rank matrixfrom under-sampled linear measurements. A low-rank matrix can be represented using a factorized model. In thisarticle, we derive Bayesian Cramer-Rao bounds for LRMR where a factorized model is used. We first show a general informative bound, and then derive several Bayesian Cramer-Rao bounds for different scenarios. We always considered the low-rank matrix to be reconstructed as a random matrix, but its model hyper-parameters for three cases - deterministic known, deterministic unknown and random. Finally we compare the bounds with existing practical algorithms through numerical simulations., QC 20160810

In this paper, the blocklength-limited performance of a relaying system is studied, where channels are assumed to experience quasi-static Rayleigh fading while at the same time only the average channel state information (CSI) is available at the source. Both the physical-layer performance (blocklength-limited throughput) and the link-layer performance (effective capacity) of the relaying system are investigated. We propose a simple system operation by introducing a factor based on which we weight the average CSI and let the source determine the coding rate accordingly. In particular, we show that both the blocklengthlimited throughput and the effective capacity are quasi-concave in the weight factor. Through numerical analysis, we investigate the relaying performance with average CSI while considering perfect CSI scenario and direct transmission as contrasts. We observe that relaying is more efficient than direct transmission in the finite blocklength regime. Moreover, this performance advantage of relaying under the average CSI scenario is more significant than under the perfect CSI scenario. Finally, the speed of convergence (between the blocklength-limited performance and the performance with infinite blocklengths) in relaying system is faster in comparison to the direct transmission under both the average CSI scenario and the perfect CSI scenario., QC 20160408

In this paper, the blocklength-limited performance of a relaying system is studied, where channels are assumed to experience quasi-static Rayleigh fading while at the same time only the average channel state information (CSI) is available at the source. Both the physical-layer performance (blocklength-limited throughput) and the link-layer performance (effective capacity) of the relaying system are investigated. We propose a simple system operation by introducing a factor based on which we weight the average CSI and let the source determine the coding rate accordingly. In particular, we show that both the blocklengthlimited throughput and the effective capacity are quasi-concave in the weight factor. Through numerical analysis, we investigate the relaying performance with average CSI while considering perfect CSI scenario and direct transmission as contrasts. We observe that relaying is more efficient than direct transmission in the finite blocklength regime. Moreover, this performance advantage of relaying under the average CSI scenario is more significant than under the perfect CSI scenario. Finally, the speed of convergence (between the blocklength-limited performance and the performance with infinite blocklengths) in relaying system is faster in comparison to the direct transmission under both the average CSI scenario and the perfect CSI scenario., QC 20160408

In this paper, the blocklength-limited performance of a relaying system is studied, where channels are assumed to experience quasi-static Rayleigh fading while at the same time only the average channel state information (CSI) is available at the source. Both the physical-layer performance (blocklength-limited throughput) and the link-layer performance (effective capacity) of the relaying system are investigated. We propose a simple system operation by introducing a factor based on which we weight the average CSI and let the source determine the coding rate accordingly. In particular, we show that both the blocklengthlimited throughput and the effective capacity are quasi-concave in the weight factor. Through numerical analysis, we investigate the relaying performance with average CSI while considering perfect CSI scenario and direct transmission as contrasts. We observe that relaying is more efficient than direct transmission in the finite blocklength regime. Moreover, this performance advantage of relaying under the average CSI scenario is more significant than under the perfect CSI scenario. Finally, the speed of convergence (between the blocklength-limited performance and the performance with infinite blocklengths) in relaying system is faster in comparison to the direct transmission under both the average CSI scenario and the perfect CSI scenario., QC 20160408

In this paper, the blocklength-limited performance of a relaying system is studied, where channels are assumed to experience quasi-static Rayleigh fading while at the same time only the average channel state information (CSI) is available at the source. Both the physical-layer performance (blocklength-limited throughput) and the link-layer performance (effective capacity) of the relaying system are investigated. We propose a simple system operation by introducing a factor based on which we weight the average CSI and let the source determine the coding rate accordingly. In particular, we show that both the blocklengthlimited throughput and the effective capacity are quasi-concave in the weight factor. Through numerical analysis, we investigate the relaying performance with average CSI while considering perfect CSI scenario and direct transmission as contrasts. We observe that relaying is more efficient than direct transmission in the finite blocklength regime. Moreover, this performance advantage of relaying under the average CSI scenario is more significant than under the perfect CSI scenario. Finally, the speed of convergence (between the blocklength-limited performance and the performance with infinite blocklengths) in relaying system is faster in comparison to the direct transmission under both the average CSI scenario and the perfect CSI scenario., QC 20160408

In this paper, the blocklength-limited performance of a relaying system is studied, where channels are assumed to experience quasi-static Rayleigh fading while at the same time only the average channel state information (CSI) is available at the source. Both the physical-layer performance (blocklength-limited throughput) and the link-layer performance (effective capacity) of the relaying system are investigated. We propose a simple system operation by introducing a factor based on which we weight the average CSI and let the source determine the coding rate accordingly. In particular, we show that both the blocklengthlimited throughput and the effective capacity are quasi-concave in the weight factor. Through numerical analysis, we investigate the relaying performance with average CSI while considering perfect CSI scenario and direct transmission as contrasts. We observe that relaying is more efficient than direct transmission in the finite blocklength regime. Moreover, this performance advantage of relaying under the average CSI scenario is more significant than under the perfect CSI scenario. Finally, the speed of convergence (between the blocklength-limited performance and the performance with infinite blocklengths) in relaying system is faster in comparison to the direct transmission under both the average CSI scenario and the perfect CSI scenario., QC 20160408

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)., QC 20160407

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)., QC 20160407

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)., QC 20160407

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)., QC 20160407

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)., QC 20160407

Low-power wireless devices suffer notoriously from Cross- Technology Interference (CTI). To enable co-existence, researchers have proposed a variety of interference mitigation strategies. Existing solutions, however, are designed to work with the limitations of currently available radio chips. In this paper, we investigate how to exploit physical layer properties of 802.15.4 signals to better address CTI. We present CrossZig, a cross-layer solution that takes advantage of physical layer information and processing to improve low-power communication under CTI. To this end, CrossZig utilizes physical layer information to detect presence of CTI in a corrupted packet and to apply an adaptive packet recovery which incorporates a novel cross-layer based packet merging and an adaptive FEC coding. We implement a prototype of CrossZig for the low-power IEEE 802.15.4 in a software-defined radio platform. We show the adaptability and the performance gain of CrossZig through experimental evaluation considering both micro-benchmarking and system performance under various interference patterns. Our results demonstrate that CrossZig can achieve a high accuracy in error localization (94.3% accuracy) and interference type identification (less than 5% error rate for SINR ranges below 3 dB). Moreover, our system shows consistent performance improvements under interference from various interfering technologies., QC 20160408

Low-power wireless devices suffer notoriously from Cross- Technology Interference (CTI). To enable co-existence, researchers have proposed a variety of interference mitigation strategies. Existing solutions, however, are designed to work with the limitations of currently available radio chips. In this paper, we investigate how to exploit physical layer properties of 802.15.4 signals to better address CTI. We present CrossZig, a cross-layer solution that takes advantage of physical layer information and processing to improve low-power communication under CTI. To this end, CrossZig utilizes physical layer information to detect presence of CTI in a corrupted packet and to apply an adaptive packet recovery which incorporates a novel cross-layer based packet merging and an adaptive FEC coding. We implement a prototype of CrossZig for the low-power IEEE 802.15.4 in a software-defined radio platform. We show the adaptability and the performance gain of CrossZig through experimental evaluation considering both micro-benchmarking and system performance under various interference patterns. Our results demonstrate that CrossZig can achieve a high accuracy in error localization (94.3% accuracy) and interference type identification (less than 5% error rate for SINR ranges below 3 dB). Moreover, our system shows consistent performance improvements under interference from various interfering technologies., QC 20160408

Low-power wireless devices suffer notoriously from Cross- Technology Interference (CTI). To enable co-existence, researchers have proposed a variety of interference mitigation strategies. Existing solutions, however, are designed to work with the limitations of currently available radio chips. In this paper, we investigate how to exploit physical layer properties of 802.15.4 signals to better address CTI. We present CrossZig, a cross-layer solution that takes advantage of physical layer information and processing to improve low-power communication under CTI. To this end, CrossZig utilizes physical layer information to detect presence of CTI in a corrupted packet and to apply an adaptive packet recovery which incorporates a novel cross-layer based packet merging and an adaptive FEC coding. We implement a prototype of CrossZig for the low-power IEEE 802.15.4 in a software-defined radio platform. We show the adaptability and the performance gain of CrossZig through experimental evaluation considering both micro-benchmarking and system performance under various interference patterns. Our results demonstrate that CrossZig can achieve a high accuracy in error localization (94.3% accuracy) and interference type identification (less than 5% error rate for SINR ranges below 3 dB). Moreover, our system shows consistent performance improvements under interference from various interfering technologies., QC 20160408

Low-power wireless devices suffer notoriously from Cross- Technology Interference (CTI). To enable co-existence, researchers have proposed a variety of interference mitigation strategies. Existing solutions, however, are designed to work with the limitations of currently available radio chips. In this paper, we investigate how to exploit physical layer properties of 802.15.4 signals to better address CTI. We present CrossZig, a cross-layer solution that takes advantage of physical layer information and processing to improve low-power communication under CTI. To this end, CrossZig utilizes physical layer information to detect presence of CTI in a corrupted packet and to apply an adaptive packet recovery which incorporates a novel cross-layer based packet merging and an adaptive FEC coding. We implement a prototype of CrossZig for the low-power IEEE 802.15.4 in a software-defined radio platform. We show the adaptability and the performance gain of CrossZig through experimental evaluation considering both micro-benchmarking and system performance under various interference patterns. Our results demonstrate that CrossZig can achieve a high accuracy in error localization (94.3% accuracy) and interference type identification (less than 5% error rate for SINR ranges below 3 dB). Moreover, our system shows consistent performance improvements under interference from various interfering technologies., QC 20160408

Low-power wireless devices suffer notoriously from Cross- Technology Interference (CTI). To enable co-existence, researchers have proposed a variety of interference mitigation strategies. Existing solutions, however, are designed to work with the limitations of currently available radio chips. In this paper, we investigate how to exploit physical layer properties of 802.15.4 signals to better address CTI. We present CrossZig, a cross-layer solution that takes advantage of physical layer information and processing to improve low-power communication under CTI. To this end, CrossZig utilizes physical layer information to detect presence of CTI in a corrupted packet and to apply an adaptive packet recovery which incorporates a novel cross-layer based packet merging and an adaptive FEC coding. We implement a prototype of CrossZig for the low-power IEEE 802.15.4 in a software-defined radio platform. We show the adaptability and the performance gain of CrossZig through experimental evaluation considering both micro-benchmarking and system performance under various interference patterns. Our results demonstrate that CrossZig can achieve a high accuracy in error localization (94.3% accuracy) and interference type identification (less than 5% error rate for SINR ranges below 3 dB). Moreover, our system shows consistent performance improvements under interference from various interfering technologies., QC 20160408