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In sparse recovery from measurement data a common approach is to use greedy pursuit reconstruction algorithms. Most of these algorithms have a correlation filter for detecting active components in the sparse data. In this paper, we show how modifications can be made for the greedy pursuit algorithms so that they use beamformers insteadof the standard correlation filter. Using these beamformers, improved performance in the algorithms is obtained. In particular, we discuss beamformers for the average and worst case scenario and give methods for constructing them., QC 20131022

In sparse recovery from measurement data a common approach is to use greedy pursuit reconstruction algorithms. Most of these algorithms have a correlation filter for detecting active components in the sparse data. In this paper, we show how modifications can be made for the greedy pursuit algorithms so that they use beamformers insteadof the standard correlation filter. Using these beamformers, improved performance in the algorithms is obtained. In particular, we discuss beamformers for the average and worst case scenario and give methods for constructing them., QC 20131022

In sparse recovery from measurement data a common approach is to use greedy pursuit reconstruction algorithms. Most of these algorithms have a correlation filter for detecting active components in the sparse data. In this paper, we show how modifications can be made for the greedy pursuit algorithms so that they use beamformers insteadof the standard correlation filter. Using these beamformers, improved performance in the algorithms is obtained. In particular, we discuss beamformers for the average and worst case scenario and give methods for constructing them., QC 20131022

In sparse recovery from measurement data a common approach is to use greedy pursuit reconstruction algorithms. Most of these algorithms have a correlation filter for detecting active components in the sparse data. In this paper, we show how modifications can be made for the greedy pursuit algorithms so that they use beamformers insteadof the standard correlation filter. Using these beamformers, improved performance in the algorithms is obtained. In particular, we discuss beamformers for the average and worst case scenario and give methods for constructing them., QC 20131022

In sparse recovery from measurement data a common approach is to use greedy pursuit reconstruction algorithms. Most of these algorithms have a correlation filter for detecting active components in the sparse data. In this paper, we show how modifications can be made for the greedy pursuit algorithms so that they use beamformers insteadof the standard correlation filter. Using these beamformers, improved performance in the algorithms is obtained. In particular, we discuss beamformers for the average and worst case scenario and give methods for constructing them., QC 20131022

In this paper we consider a scenario, where several mobile multi-antenna terminals communicate with the multi- antenna base station within a cellular communication system over the flat Rayleigh fading channel. In addition, several terminals from the neighboring cell cause interference. For such a scenario, we derive, using the replica method, the asymptotic sum-rate of the communication in the large-system limit for arbitrary signal constellations. Moreover, we show via numerical results that when the interfering terminals use a QPSK constellation, the resulting interference becomes easier to handle. In effect, we may be able to accumulate more interfering transmitter-receiver pairs within the same area as compared to the case of Gaussian signals. Monte- Carlo simulations show that the derived asymptotic expression matches well with the simulated values even for small numbers of antennas., QC 20130108

In 1968, Witsenhausen introduced his famous counterexample where he showed that even in the simple linear quadratic static team decision problem, complex nonlinear decisions could outperform any given linear decision. This problem has served as a benchmark problem for decades where researchers try to achieve the optimal solution. This paper introduces a systematic iterative source-channel coding approach to solve problems of the Witsenhausen Counterexample-character. The advantage of the presented approach is its simplicity. Also, no assumptions are made about the shape of the space of policies. The minimal cost obtatined using the introduced method is 0.16692462, which is the lowest known thus far., © 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.QC 20111212

Cooperative communication has been proposed as a means to increase the capacity of a wireless link by mitigating the path-loss, fading and shadowing effects of radio propagation. In this paper, we evaluate the efficiency of cooperative communication in large scale wireless networks under interference from simultaneous transmissions. Specifically, we consider tunable spatial reuse time division multiplexing and half-duplex decode-and-forward cooperative relaying on a hop-by-hop basis. We show that hop-by-hop cooperation improves the reliability of the transmissions particularly in the low-SINR or in the low-coding-rate regimes. Moreover, hop-by-hop cooperative relaying gains 15 - 20% more throughput compared to simple multihopping in the interference-limited regime, if the relay location and the reuse distance are jointly optimized., QC 20120102, SRA TNG Network Layer Models

In 1968, Witsenhausen introduced his famous counterexample where he showed that even in the simple linear quadratic static team decision problem, complex nonlinear decisions could outperform any given linear decision. This problem has served as a benchmark problem for decades where researchers try to achieve the optimal solution. This paper introduces a systematic iterative source-channel coding approach to solve problems of the Witsenhausen Counterexample-character. The advantage of the presented approach is its simplicity. Also, no assumptions are made about the shape of the space of policies. The minimal cost obtatined using the introduced method is 0.16692462, which is the lowest known thus far., © 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.QC 20111212

In 1968, Witsenhausen introduced his famous counterexample where he showed that even in the simple linear quadratic static team decision problem, complex nonlinear decisions could outperform any given linear decision. This problem has served as a benchmark problem for decades where researchers try to achieve the optimal solution. This paper introduces a systematic iterative source-channel coding approach to solve problems of the Witsenhausen Counterexample-character. The advantage of the presented approach is its simplicity. Also, no assumptions are made about the shape of the space of policies. The minimal cost obtatined using the introduced method is 0.16692462, which is the lowest known thus far., © 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.QC 20111212

In 1968, Witsenhausen introduced his famous counterexample where he showed that even in the simple linear quadratic static team decision problem, complex nonlinear decisions could outperform any given linear decision. This problem has served as a benchmark problem for decades where researchers try to achieve the optimal solution. This paper introduces a systematic iterative source-channel coding approach to solve problems of the Witsenhausen Counterexample-character. The advantage of the presented approach is its simplicity. Also, no assumptions are made about the shape of the space of policies. The minimal cost obtatined using the introduced method is 0.16692462, which is the lowest known thus far., © 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.QC 20111212

In 1968, Witsenhausen introduced his famous counterexample where he showed that even in the simple linear quadratic static team decision problem, complex nonlinear decisions could outperform any given linear decision. This problem has served as a benchmark problem for decades where researchers try to achieve the optimal solution. This paper introduces a systematic iterative source-channel coding approach to solve problems of the Witsenhausen Counterexample-character. The advantage of the presented approach is its simplicity. Also, no assumptions are made about the shape of the space of policies. The minimal cost obtatined using the introduced method is 0.16692462, which is the lowest known thus far., © 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.QC 20111212

In this paper we extend the concept of check splitting to root-check LDPC codes, thus providing an incremental redundancy code construction specifically for the ARQ blockfading channel. By construction, the proposed coding scheme achieves a high level of diversity and effectively adapts the transmission rate to the instantaneous channel conditions., QC 20120123

This paper presents an achievable rate region for a 2-user Gaussian Z-interference channel with a noiseless and bidirectional digital communication link between the receivers. The region is achieved by utilizing the rate-splitting encoding technique, and the decode-and-forward and compress-and-forward strategies. In the very strong interference regime, the capacity region is achieved. In the weak interference regime, the asymptotic sum rate is characterized and shown to be possibly unbounded, which is in contrast to a recent result by Yu and Zhou for a similar scenario, however, with a unidirectional communication link between the receivers., QC 20110218

This paper presents a novel bandwidth-efficient joint network/channel coding approach for multi-user ARQ where network coding is employed to combine retransmissions to several users. It features trellis-coded modulation (TCM) and iterative network/channel decoding. We provide guidelines for optimizing the TCM encoder to enable a high throughput and match the network code to the TCM encoder such that convergence of the iterative decoder is achieved. Our results show that the proposed scheme significantly outperforms the conventional ones. At the same time the amount of feedback and the decoding complexity per user are greatly reduced., QC 20110223

In a wireless sensor network, it is likely that the measurements of the sensors are correlated. Distributed source coding can be used to reduce transmission rate or mitigate the effects of the channel noise in the case of analog transmission. In this paper, we propose a novel scheme for implementing distributed source-channel coding based on analog mappings. We assume that an analog source is to be transmitted to a receiver that has access to correlated side information, as in the Wyner-Ziv problem. From the Cramer-Rao lower bound, we observe general properties of analog distributed source-channel mappings. It is especially clear how the stretch factor influences the performance. From this observation we propose two different mappings based on sinusoidal waveforms. The proposed transmission scheme is numerically evaluated and shown to perform well, particularly in the low-SNR regime. Furthermore, it requires no encoding or decoding delay, making it suitable for delay-critical applications in wireless sensor networks., QC 20110221

We consider the multiple-access two-hop channel where two source nodes transmit to a destination node via a relay node. The relaying function is memoryless, in contrast to the conventional schemes based on coding with long codewords. That is, we model the operation of the relay as a two-to-one deterministic mapping, which combines the two received analog signals from the sources. This procedure resembles the concept of network coding where information combining is applied in the intermediate nodes. However, as our mapping directly combines the received analog signals without decoding, we coin the term (memoryless) analog network coding mapping. In this paper, both linear and non-linear mappings are studied. In particular, the Archimedean spiral is used for the non-linear 2:1 mapping, inspired by similar work in the context of joint source-channel coding. We discuss both the achievable rate regions and sum rates and demonstrate significant gains of applying the proposed analog mappings in the relay., QC 20110221

Optimal rate allocation in a networked control system with limited communication resources is instrumental to achieve satisfactory overall performance. In this paper, a practical rate allocation technique for state estimation in linear dynamic systems over a noisy channel is proposed. The method consists of two steps: (i) the overall distortion is expressed as a function of rates at all time instants by means of high-rate quantization theory, and (ii) a constrained optimization problem to minimize the overall distortion is solved by using Lagrange duality. Monte Carlo simulations illustrate the proposed scheme, which is shown to have good performance when compared to arbitrarily selected rate allocations., QC 20110113

In this paper, we investigate the performance of a new class of distributed codes for the 3-node relay channel, distributed serially concatenated codes (DSCCs). We provide a snap-shot analysis of the iterative DSCC decoder based on extrinsic information transfer characteristics and discuss new design aspects resulting from the extended code structure. Our simulation results show that DSCCs lead to an improved error-floor performance and an increased robustness against decoding errors at the relay compared to conventional distributed parallel concatenated coding schemes., QC 20110222

This paper proposes Utility Based Adaptive Frequency Hopping (UBAFH), an adaptive hopping technique implementing a new paradigm for frequency hopping systems. Traditional adaptive algorithms aim at identifying bad channels that are consequently removed from the hopset. UBAFH instead utilizes all the available frequencies but assigns different usage probabilities to different channels according to the experienced channel conditions. Opportune upper and lower limits are used in order to bound these usage probabilities and achieve a desired level of frequency diversity. We simulate the behavior of UBAFH over frequency selective fading channels and compare the achieved packet error rate and throughput with the ones of IEEE 802.15.1 and with the adaptive frequency hopping implementation proposed by IEEE 802.15.2 showing that UBAFH outperforms both approaches., QC 20130506

Optimal rate allocation in a networked control system with limited communication resources is instrumental to achieve satisfactory overall performance. In this paper, a practical rate allocation technique for state estimation in linear dynamic systems over a noisy channel is proposed. The method consists of two steps: (i) the overall distortion is expressed as a function of rates at all time instants by means of high-rate quantization theory, and (ii) a constrained optimization problem to minimize the overall distortion is solved by using Lagrange duality. Monte Carlo simulations illustrate the proposed scheme, which is shown to have good performance when compared to arbitrarily selected rate allocations., QC 20110113

Optimal rate allocation in a networked control system with limited communication resources is instrumental to achieve satisfactory overall performance. In this paper, a practical rate allocation technique for state estimation in linear dynamic systems over a noisy channel is proposed. The method consists of two steps: (i) the overall distortion is expressed as a function of rates at all time instants by means of high-rate quantization theory, and (ii) a constrained optimization problem to minimize the overall distortion is solved by using Lagrange duality. Monte Carlo simulations illustrate the proposed scheme, which is shown to have good performance when compared to arbitrarily selected rate allocations., QC 20110113

Optimal rate allocation in a networked control system with limited communication resources is instrumental to achieve satisfactory overall performance. In this paper, a practical rate allocation technique for state estimation in linear dynamic systems over a noisy channel is proposed. The method consists of two steps: (i) the overall distortion is expressed as a function of rates at all time instants by means of high-rate quantization theory, and (ii) a constrained optimization problem to minimize the overall distortion is solved by using Lagrange duality. Monte Carlo simulations illustrate the proposed scheme, which is shown to have good performance when compared to arbitrarily selected rate allocations., QC 20110113

Optimal rate allocation in a networked control system with limited communication resources is instrumental to achieve satisfactory overall performance. In this paper, a practical rate allocation technique for state estimation in linear dynamic systems over a noisy channel is proposed. The method consists of two steps: (i) the overall distortion is expressed as a function of rates at all time instants by means of high-rate quantization theory, and (ii) a constrained optimization problem to minimize the overall distortion is solved by using Lagrange duality. Monte Carlo simulations illustrate the proposed scheme, which is shown to have good performance when compared to arbitrarily selected rate allocations., QC 20110113

In this paper we propose continuous time Markov chain models that capture the dynamics of content spreading in a disruption tolerant wireless content distribution system. We use our models to study the effect of cooperation among the mobile nodes and how limited node resources, such as battery lifetime and confined storage, affect the content distribution process. Based on our models and numerical results we deduce that limiting the number of times a node shares each content entry is a good method to conserve energy while at the same time only slightly reducing system performance. Our study also suggests that the effect of assisting nodes is greatest for content channels with few subscribers. For promoting fairness in distributing channels and giving new channels a chance to spread, assisting nodes should therefore solicit and help in spreading less popular channels., QC 20110706

This paper investigates the shaping of the extrinsic information transfer (EXIT) functions of parallel turbo codes, when the turbo code is used as an outer code in a serially concatenated scheme. As opposed to other work, we consider the overall EXIT function of the turbo decoder and not the EXIT chart for the component codes. Three methods for shaping are investigated: (i) individual puncturing of the systematic part and the two parity parts of the turbo codes, (ii) puncturing of the overall turbo code, and (iii) repetition of the overall turbo code. For each case, the EXIT function of the resulting turbo code is computed from the EXIT function of its component codes. Exact results are obtained for the binary erasure channel., QC 20110210

In this paper, an iterative decoder for LDPC codes, suitable for high-speed processing, is proposed and analyzed. The messages exchanged between the nodes are binary vectors, where the information is conveyed by the number of ones rather than their positions in the vector. Regarding this aspect, the approach exhibits some similarities to stochastic decoding techniques. The check node decoders perform simple bit-wise modulo-2 additions, whereas the variable node decoders perform more complex processing tasks, making the resulting structure attractive for high-speed hardware implementation. By selecting the length of the binary vector messages between one and infinity, the complexity and the performance of the decoder can be scaled between that of the original binary message passing algorithm and that of the sum-product algorithm. Density evolution is developed for the proposed decoding algorithm, and decoding thresholds are determined as functions of the length of the binary vector messages. Simulation results exemplify the performance for finite-length codes., QC 20110218

We address the optimization of joint source-channel coding schemes for iterative source-channel decoding (ISCD) of first-order Markov sources. The optimization requires source encoders, providing code words with a minimum Hamming distance d(min) >= 2. Usually, specifically designed variable-length codes (VLCs) are applied for this purpose, leading, however, to an increased complexity of the decoder. In this paper, we propose a novel concept: a single parity-check code is employed for mapping the quantized source symbols to a binary representation. The additional redundancy introduced by this mapping is afterwards compensated by an inner channel code with rate R > 1. Simulation results for the AWGN channel show that the proposed scheme is superior to VLC-based approaches, while leading to a significantly reduced complexity at the same time., QC 20110210

In this paper, the extrinsic information transfer (EXIT) functions of randomly punctured systematic codes (RP-SCs) are analyzed. We consider the systematic encoder to consist of a non-systematic code (NC) and a parallel path for the systematic bits; puncturing of the systematic and the parity bits is then carried out separately. We show that the decoding model for measuring the EXIT characteristics of a RP-SC can be transformed into a decoding model of the underlying NC. For the special case where the a priori/communication channels are modeled by binary erasure channels, it becomes obvious that the EXIT functions of any RP-SC can be analytically derived from the EXIT functions of the underlying NC. This theoretical result is verified by a comparison to simulated EXIT functions., QC 20110210

This paper deals with algorithms for determining well performing bandwidth expanding joint source-channel coding (JSCC) systems. The JSCC systems are realized as direct source-channel mappings. The algorithms presented are "Power Constrained Channel Optimized Vector Quantizers" (PCCOVQ). The PCCOVQ algorithm is a modified generalized Lloyd algorithm. Theory on PCCOVQ is presented, with the emphasis on bandwidth expansion. Examples and results concerning bandwidth expansion by a factor of 2 and 3 are given., QC 20140225

The interference channel (IC) is the basic underlying channel model for many practical multi-terminal communication scenarios. Here, we consider a modified three-user IC with dual-antenna transmitters and receivers where we allow one receiver to have a finite-rate unidirectional link to another receiver. This kind of receiver-to-receiver link may exist in cellular networks with base-station backhaul. For this scenario we propose a scheme that combines the interference-cancellation capability of Alamouti codes with the subspace-overlapping property of interference alignment to achieve a sum-rate of three symbols/channel use. The scheme does also simultaneously provide a diversity gain of two for every symbol which significantly improves the bit-error rate performance compared to zero-forcing techniques., QC 20131112

Remote stabilization of linear dynamical systems over Gaussian networks is studied. Two linear time invariant systems (plants) with arbitrary distributed initial states are monitored by two separate sensors. The sensors communicate their measurements to two remotely situated controllers over a Gaussian interference, possibly with the assistance from a relay node. The common goal of the sensors, relay, and controllers is to stabilize the plants in mean-square sense. An optimized linear delay-free sensing and control scheme is proposed and sufficient conditions for mean-square stability are derived. These conditions reveal the relationship between plants' stability and communication channel parameters. It is shown that the proposed linear scheme can significantly outperform the existing estimation based control scheme in multi-user Gaussian networks., QC 20130806

We consider vector-quantized (VQ) transmission of compressed sensing (CS) measurements over noisy channels. Adopting mean-square error (MSE) criterion to measure the distortion between a sparse vector and its reconstruction, we derive channel-optimized quantization principles for encoding CS measurement vector and reconstructing sparse source vector. The resulting necessary optimal conditions are used to develop an algorithm for training channel-optimized vector quantization (COVQ) of CS measurements by taking the end-to-end distortion measure into account., QC 20140225

The problem of reliable communication over the Gaussian two-way relay channel is considered and a new achievable rate region by combining structured lattice-and random Gaussian codes is presented. It is then demonstrated that the new bound can outperform previously known rate regions. In particular, having found the optimal time-sharing and power allocation policies, it is established that the proposed strategy achieves the symmetric capacity to within 1/4e approximate to 0.092 [nat] approximate to 0.133 [bit], which improves on earlier bounds., QC 20130806

In a compressed sensing setup with jointly sparse, correlated data,we develop a distributed greedy algorithm called distributed predic-tive subspace pursuit. Based on estimates from neighboring sensornodes, this algorithm operates iteratively in two steps: first forminga prediction of the signal and then solving the compressed sensingproblem with an iterative linear minimum mean squared estimator.Through simulations we show that the algorithm provides better per-formance than current state-of-the-art algorithms., QC 20130902

For compressive sensing, we derive achievable performance guarantees for recovering partial support sets of sparse vectors. The guarantees are determined in terms of the fraction of signal power to be detected and the measurement rate, defined as a relation between the dimensions of the measurement matrix. Based on this result we derive a tradeoff between the measurement rate and the mean square error, and illustrate it by a numerical example., QC 20130710

We consider a resource-constrained scenario where a compressed sensing- (CS) based sensor has a low number of measurements which are quantized at a low rate followed by transmission or storage. Applying this scenario, we develop a new quantizer design which aims to attain a high-quality reconstruction performance of a sparse source signal based on analysis-by-synthesis framework. Through simulations, we compare the performance of the proposed quantization algorithm vis-a-vis existing quantization methods., QC 20130902

We consider vector-quantized (VQ) transmission of compressed sensing (CS) measurements over noisy channels. Adopting mean-square error (MSE) criterion to measure the distortion between a sparse vector and its reconstruction, we derive channel-optimized quantization principles for encoding CS measurement vector and reconstructing sparse source vector. The resulting necessary optimal conditions are used to develop an algorithm for training channel-optimized vector quantization (COVQ) of CS measurements by taking the end-to-end distortion measure into account., QC 20140225

We consider vector-quantized (VQ) transmission of compressed sensing (CS) measurements over noisy channels. Adopting mean-square error (MSE) criterion to measure the distortion between a sparse vector and its reconstruction, we derive channel-optimized quantization principles for encoding CS measurement vector and reconstructing sparse source vector. The resulting necessary optimal conditions are used to develop an algorithm for training channel-optimized vector quantization (COVQ) of CS measurements by taking the end-to-end distortion measure into account., QC 20140225

We consider vector-quantized (VQ) transmission of compressed sensing (CS) measurements over noisy channels. Adopting mean-square error (MSE) criterion to measure the distortion between a sparse vector and its reconstruction, we derive channel-optimized quantization principles for encoding CS measurement vector and reconstructing sparse source vector. The resulting necessary optimal conditions are used to develop an algorithm for training channel-optimized vector quantization (COVQ) of CS measurements by taking the end-to-end distortion measure into account., QC 20140225

For compressive sensing, we derive achievable performance guarantees for recovering partial support sets of sparse vectors. The guarantees are determined in terms of the fraction of signal power to be detected and the measurement rate, defined as a relation between the dimensions of the measurement matrix. Based on this result we derive a tradeoff between the measurement rate and the mean square error, and illustrate it by a numerical example., QC 20130710

We consider a resource-constrained scenario where a compressed sensing- (CS) based sensor has a low number of measurements which are quantized at a low rate followed by transmission or storage. Applying this scenario, we develop a new quantizer design which aims to attain a high-quality reconstruction performance of a sparse source signal based on analysis-by-synthesis framework. Through simulations, we compare the performance of the proposed quantization algorithm vis-a-vis existing quantization methods., QC 20130902

For compressive sensing, we derive achievable performance guarantees for recovering partial support sets of sparse vectors. The guarantees are determined in terms of the fraction of signal power to be detected and the measurement rate, defined as a relation between the dimensions of the measurement matrix. Based on this result we derive a tradeoff between the measurement rate and the mean square error, and illustrate it by a numerical example., QC 20130710

We consider a resource-constrained scenario where a compressed sensing- (CS) based sensor has a low number of measurements which are quantized at a low rate followed by transmission or storage. Applying this scenario, we develop a new quantizer design which aims to attain a high-quality reconstruction performance of a sparse source signal based on analysis-by-synthesis framework. Through simulations, we compare the performance of the proposed quantization algorithm vis-a-vis existing quantization methods., QC 20130902

We consider a resource-constrained scenario where a compressed sensing- (CS) based sensor has a low number of measurements which are quantized at a low rate followed by transmission or storage. Applying this scenario, we develop a new quantizer design which aims to attain a high-quality reconstruction performance of a sparse source signal based on analysis-by-synthesis framework. Through simulations, we compare the performance of the proposed quantization algorithm vis-a-vis existing quantization methods., QC 20130902

For compressive sensing, we derive achievable performance guarantees for recovering partial support sets of sparse vectors. The guarantees are determined in terms of the fraction of signal power to be detected and the measurement rate, defined as a relation between the dimensions of the measurement matrix. Based on this result we derive a tradeoff between the measurement rate and the mean square error, and illustrate it by a numerical example., QC 20130710

We consider a resource-constrained scenario where a compressed sensing- (CS) based sensor has a low number of measurements which are quantized at a low rate followed by transmission or storage. Applying this scenario, we develop a new quantizer design which aims to attain a high-quality reconstruction performance of a sparse source signal based on analysis-by-synthesis framework. Through simulations, we compare the performance of the proposed quantization algorithm vis-a-vis existing quantization methods., QC 20130902

We consider vector-quantized (VQ) transmission of compressed sensing (CS) measurements over noisy channels. Adopting mean-square error (MSE) criterion to measure the distortion between a sparse vector and its reconstruction, we derive channel-optimized quantization principles for encoding CS measurement vector and reconstructing sparse source vector. The resulting necessary optimal conditions are used to develop an algorithm for training channel-optimized vector quantization (COVQ) of CS measurements by taking the end-to-end distortion measure into account., QC 20140225

For compressive sensing, we derive achievable performance guarantees for recovering partial support sets of sparse vectors. The guarantees are determined in terms of the fraction of signal power to be detected and the measurement rate, defined as a relation between the dimensions of the measurement matrix. Based on this result we derive a tradeoff between the measurement rate and the mean square error, and illustrate it by a numerical example., QC 20130710

A multi-antenna cognitive radio network, with a single pair of primary users and a secondary broadcast channel, is considered. Under perfect channel state information (CSI), the rate-optimal strategy for the primary link is waterfilling, resulting in possibly unused dimensions. The secondary base station, supplied with perfect and global CSI, employs block diagonalization for interference-free message precoding and opportunistic interference alignment to avoid disturbing primary communication. In the absence of sufficient resources to serve all secondary users, a low complexity adaptive antenna selection strategy is proposed. This scheme constructs, in each time-slot, the set of active antennas by opportunistically reusing information and resources from the preceding selection round. Numerical results indicate a complexity reduction at a small rate-loss penalty, in comparison with existing selection methods., QC 20130524, EU, FP7, Sjunde ramprogrammet, INFSO-ICT-248303 QUASAR