Search

The recent development of low cost wireless sensors enables novel internet-of-things (IoT) applications, such as the monitoring of water distribution networks. In such scenarios, the lifetime of the wireless sensor network (WSN) is a major concern, given that sensor node replacement is generally inconvenient and costly. In this paper, a compressive sensing-based scheduling scheme is proposed that conserves energy by activating only a small subset of sensor nodes in each timeslot to sense and transmit. Compressive sensing introduces a cardinality constraint that makes the scheduling optimization problem particularly challenging. Taking advantage of the network topology imposed by the IoT water monitoring scenario, the scheduling problem is decomposed into simpler subproblems, and a dynamic-programming-based solution method is proposed. Based on the proposed method, a solution algorithm is derived, whose complexity and energy-wise performance are investigated. The complexity of the proposed algorithm is characterized and its performance is evaluated numerically via an IoT emulator of water distribution networks. The analytical and numerical results show that the proposed algorithm outperforms state-of-the-art approaches in terms of energy consumption, network lifetime, and robustness to sensor node failures. It is argued that the derived solution approach is general and it can be potentially applied to more IoT scenarios such as WSN scheduling in smart cities and intelligent transport systems., QC 20151215

The recent development of low cost wireless sensors enables novel internet-of-things (IoT) applications, such as the monitoring of water distribution networks. In such scenarios, the lifetime of the wireless sensor network (WSN) is a major concern, given that sensor node replacement is generally inconvenient and costly. In this paper, a compressive sensing-based scheduling scheme is proposed that conserves energy by activating only a small subset of sensor nodes in each timeslot to sense and transmit. Compressive sensing introduces a cardinality constraint that makes the scheduling optimization problem particularly challenging. Taking advantage of the network topology imposed by the IoT water monitoring scenario, the scheduling problem is decomposed into simpler subproblems, and a dynamic-programming-based solution method is proposed. Based on the proposed method, a solution algorithm is derived, whose complexity and energy-wise performance are investigated. The complexity of the proposed algorithm is characterized and its performance is evaluated numerically via an IoT emulator of water distribution networks. The analytical and numerical results show that the proposed algorithm outperforms state-of-the-art approaches in terms of energy consumption, network lifetime, and robustness to sensor node failures. It is argued that the derived solution approach is general and it can be potentially applied to more IoT scenarios such as WSN scheduling in smart cities and intelligent transport systems., QC 20151215

The recent development of low cost wireless sensors enables novel internet-of-things (IoT) applications, such as the monitoring of water distribution networks. In such scenarios, the lifetime of the wireless sensor network (WSN) is a major concern, given that sensor node replacement is generally inconvenient and costly. In this paper, a compressive sensing-based scheduling scheme is proposed that conserves energy by activating only a small subset of sensor nodes in each timeslot to sense and transmit. Compressive sensing introduces a cardinality constraint that makes the scheduling optimization problem particularly challenging. Taking advantage of the network topology imposed by the IoT water monitoring scenario, the scheduling problem is decomposed into simpler subproblems, and a dynamic-programming-based solution method is proposed. Based on the proposed method, a solution algorithm is derived, whose complexity and energy-wise performance are investigated. The complexity of the proposed algorithm is characterized and its performance is evaluated numerically via an IoT emulator of water distribution networks. The analytical and numerical results show that the proposed algorithm outperforms state-of-the-art approaches in terms of energy consumption, network lifetime, and robustness to sensor node failures. It is argued that the derived solution approach is general and it can be potentially applied to more IoT scenarios such as WSN scheduling in smart cities and intelligent transport systems., QC 20151215

In this paper, we investigate the performance of a wireless relay network with multiple transmission sessions, in which multiple groups of source nodes communicate with their respective destination nodes via a shared wireless relay network. A multiple transmission session model with network code division multiplexing (NCDM) scheme is proposed to remove the intersession interference at each destination. The fundamental idea of the NCDM scheme takes advantage of the property of G circle dot H-T = 0 of the low-density generator matrix (LDGM) codes. Based on the analysis of the NCDM scheme, we investigate the relationship among the equivalent received signal vector, the number of sessions and the column weight of the generator matrix. New code design criteria for the construction of the generator matrix is proposed. We further evaluate the multiple transmission session model with the proposed NCDM scheme in terms of throughput and complexity. Our evaluation demonstrates that the proposed scheme not only has a linear computational complexity, but also shows a similar error performance in the AWGN case and a considerable throughput improvement compared with its counterpart, which is referred to as a serial session scheme, where groups of source nodes communicate with their respective destinations in a time division manner., QC 20151216

The recent development of low cost wireless sensors enables novel internet-of-things (IoT) applications, such as the monitoring of water distribution networks. In such scenarios, the lifetime of the wireless sensor network (WSN) is a major concern, given that sensor node replacement is generally inconvenient and costly. In this paper, a compressive sensing-based scheduling scheme is proposed that conserves energy by activating only a small subset of sensor nodes in each timeslot to sense and transmit. Compressive sensing introduces a cardinality constraint that makes the scheduling optimization problem particularly challenging. Taking advantage of the network topology imposed by the IoT water monitoring scenario, the scheduling problem is decomposed into simpler subproblems, and a dynamic-programming-based solution method is proposed. Based on the proposed method, a solution algorithm is derived, whose complexity and energy-wise performance are investigated. The complexity of the proposed algorithm is characterized and its performance is evaluated numerically via an IoT emulator of water distribution networks. The analytical and numerical results show that the proposed algorithm outperforms state-of-the-art approaches in terms of energy consumption, network lifetime, and robustness to sensor node failures. It is argued that the derived solution approach is general and it can be potentially applied to more IoT scenarios such as WSN scheduling in smart cities and intelligent transport systems., QC 20151215

The recent development of low cost wireless sensors enables novel internet-of-things (IoT) applications, such as the monitoring of water distribution networks. In such scenarios, the lifetime of the wireless sensor network (WSN) is a major concern, given that sensor node replacement is generally inconvenient and costly. In this paper, a compressive sensing-based scheduling scheme is proposed that conserves energy by activating only a small subset of sensor nodes in each timeslot to sense and transmit. Compressive sensing introduces a cardinality constraint that makes the scheduling optimization problem particularly challenging. Taking advantage of the network topology imposed by the IoT water monitoring scenario, the scheduling problem is decomposed into simpler subproblems, and a dynamic-programming-based solution method is proposed. Based on the proposed method, a solution algorithm is derived, whose complexity and energy-wise performance are investigated. The complexity of the proposed algorithm is characterized and its performance is evaluated numerically via an IoT emulator of water distribution networks. The analytical and numerical results show that the proposed algorithm outperforms state-of-the-art approaches in terms of energy consumption, network lifetime, and robustness to sensor node failures. It is argued that the derived solution approach is general and it can be potentially applied to more IoT scenarios such as WSN scheduling in smart cities and intelligent transport systems., QC 20151215

In this paper, we improve iterative greedy search algorithms in which atoms are selected serially over iterations, i.e., one-by-one over iterations. For serial atom selection, we devise two new schemes to select an atom from a set of potential atoms in each iteration. The two new schemes lead to two new algorithms. For both the algorithms, in each iteration, the set of potential atoms is found using a standard matched filter. In case of the first scheme, we propose an orthogonal projection strategy that selects an atom from the set of potential atoms. Then, for the second scheme, we propose a look-ahead strategy such that the selection of an atom in the current iteration has an effect on the future iterations. The use of look-ahead strategy requires a higher computational resource. To achieve a tradeoff between performance and complexity, we use the two new schemes in cascade and develop a third new algorithm. Through experimental evaluations, we compare the proposed algorithms with existing greedy search and convex relaxation algorithms., QC 20120227

In this paper, we improve iterative greedy search algorithms in which atoms are selected serially over iterations, i.e., one-by-one over iterations. For serial atom selection, we devise two new schemes to select an atom from a set of potential atoms in each iteration. The two new schemes lead to two new algorithms. For both the algorithms, in each iteration, the set of potential atoms is found using a standard matched filter. In case of the first scheme, we propose an orthogonal projection strategy that selects an atom from the set of potential atoms. Then, for the second scheme, we propose a look-ahead strategy such that the selection of an atom in the current iteration has an effect on the future iterations. The use of look-ahead strategy requires a higher computational resource. To achieve a tradeoff between performance and complexity, we use the two new schemes in cascade and develop a third new algorithm. Through experimental evaluations, we compare the proposed algorithms with existing greedy search and convex relaxation algorithms., QC 20120227

In this paper, we improve iterative greedy search algorithms in which atoms are selected serially over iterations, i.e., one-by-one over iterations. For serial atom selection, we devise two new schemes to select an atom from a set of potential atoms in each iteration. The two new schemes lead to two new algorithms. For both the algorithms, in each iteration, the set of potential atoms is found using a standard matched filter. In case of the first scheme, we propose an orthogonal projection strategy that selects an atom from the set of potential atoms. Then, for the second scheme, we propose a look-ahead strategy such that the selection of an atom in the current iteration has an effect on the future iterations. The use of look-ahead strategy requires a higher computational resource. To achieve a tradeoff between performance and complexity, we use the two new schemes in cascade and develop a third new algorithm. Through experimental evaluations, we compare the proposed algorithms with existing greedy search and convex relaxation algorithms., QC 20120227

In this paper, we improve iterative greedy search algorithms in which atoms are selected serially over iterations, i.e., one-by-one over iterations. For serial atom selection, we devise two new schemes to select an atom from a set of potential atoms in each iteration. The two new schemes lead to two new algorithms. For both the algorithms, in each iteration, the set of potential atoms is found using a standard matched filter. In case of the first scheme, we propose an orthogonal projection strategy that selects an atom from the set of potential atoms. Then, for the second scheme, we propose a look-ahead strategy such that the selection of an atom in the current iteration has an effect on the future iterations. The use of look-ahead strategy requires a higher computational resource. To achieve a tradeoff between performance and complexity, we use the two new schemes in cascade and develop a third new algorithm. Through experimental evaluations, we compare the proposed algorithms with existing greedy search and convex relaxation algorithms., QC 20120227

In this paper, we improve iterative greedy search algorithms in which atoms are selected serially over iterations, i.e., one-by-one over iterations. For serial atom selection, we devise two new schemes to select an atom from a set of potential atoms in each iteration. The two new schemes lead to two new algorithms. For both the algorithms, in each iteration, the set of potential atoms is found using a standard matched filter. In case of the first scheme, we propose an orthogonal projection strategy that selects an atom from the set of potential atoms. Then, for the second scheme, we propose a look-ahead strategy such that the selection of an atom in the current iteration has an effect on the future iterations. The use of look-ahead strategy requires a higher computational resource. To achieve a tradeoff between performance and complexity, we use the two new schemes in cascade and develop a third new algorithm. Through experimental evaluations, we compare the proposed algorithms with existing greedy search and convex relaxation algorithms., QC 20120227

A Gaussian multiple-input multiple-output broadcast channel (MIMO GBC) is considered. Throughout the paper it is assumed that 1) input signals are Gaussian and 2) perfect channel state information is available at the transmitter and at the receivers. By considering each data stream as a single user, the uplink-downlink signal-to-interference-plus-noise (SINR) duality is generalized to the MIMO case with general cross-talk matrix. The duality is subsequently applied to finding the solution for the SINR-balancing problem. The result serves as a tool for characterizing achievable rate regions of different coding strategies. Next, we investigate a superposition coding scheme proposed by Cover-van der Meulen-Hajek and Pursley (nicknamed CMHP [1]), where there is a common message to both users. We consider a MIMO broadcast channel with two users, each user has two antennas and the transmitter has four antennas. Assuming one common stream is sent by CMHP coding and successive decoding, a lower bound to the CMHP rate region is found. Behaviors of the CMHP rate region and sumrate are analyzed. We find the sumrate gaps between DPC, CMHP, and MMSE at high SNR for general 2-user multiple-input single-output (MISO) Gaussian broadcast channel. The result suggests when CMHP is beneficial for sumrate., QC 20110411

We investigate sink decoding approaches and performance analysis for a network with intermediate node encoding (coded network). The network consists of statistically independent noisy channels. The sink bit error probability (BEP) is the performance measure. First, we investigate soft-decision decoding without statistical information on the upstream channels (the channels not directly connected to the sink). Numerical results show that the decoder cannot significantly improve the performance from a hard-decision decoder. We develop union bounds for analysis. The bounds show the asymptotic (regarding SNR: signal-to-noise ratio) performance of the decoder. Using statistical information about the upstream channels, we can find the error patterns of final hop channels (channels directly connected to sinks).With the error patterns, maximum-likelihood (ML) decoding can be performed, and a significant improvement in the BEP is obtained. To evaluate the union bound for the ML decoder, we use an equivalent point procedure. It is reduced to the least-squares problem with a linear constraint in the medium-to-high SNR region. With deterministic knowledge of the errors in the upstream channels, a genie-aided decoder can further improve the performance. We give the union bound for the genie decoder, which is straightforward to evaluate. By analyzing these decoders, we find that knowledge about the upstream channels is essential for good sink decoding., © 2009 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 20111101. Updated from conference paper to article, VR Project

We investigate sink decoding approaches and performance analysis for a network with intermediate node encoding (coded network). The network consists of statistically independent noisy channels. The sink bit error probability (BEP) is the performance measure. First, we investigate soft-decision decoding without statistical information on the upstream channels (the channels not directly connected to the sink). Numerical results show that the decoder cannot significantly improve the performance from a hard-decision decoder. We develop union bounds for analysis. The bounds show the asymptotic (regarding SNR: signal-to-noise ratio) performance of the decoder. Using statistical information about the upstream channels, we can find the error patterns of final hop channels (channels directly connected to sinks).With the error patterns, maximum-likelihood (ML) decoding can be performed, and a significant improvement in the BEP is obtained. To evaluate the union bound for the ML decoder, we use an equivalent point procedure. It is reduced to the least-squares problem with a linear constraint in the medium-to-high SNR region. With deterministic knowledge of the errors in the upstream channels, a genie-aided decoder can further improve the performance. We give the union bound for the genie decoder, which is straightforward to evaluate. By analyzing these decoders, we find that knowledge about the upstream channels is essential for good sink decoding., © 2009 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 20111101. Updated from conference paper to article, VR Project

We investigate sink decoding approaches and performance analysis for a network with intermediate node encoding (coded network). The network consists of statistically independent noisy channels. The sink bit error probability (BEP) is the performance measure. First, we investigate soft-decision decoding without statistical information on the upstream channels (the channels not directly connected to the sink). Numerical results show that the decoder cannot significantly improve the performance from a hard-decision decoder. We develop union bounds for analysis. The bounds show the asymptotic (regarding SNR: signal-to-noise ratio) performance of the decoder. Using statistical information about the upstream channels, we can find the error patterns of final hop channels (channels directly connected to sinks).With the error patterns, maximum-likelihood (ML) decoding can be performed, and a significant improvement in the BEP is obtained. To evaluate the union bound for the ML decoder, we use an equivalent point procedure. It is reduced to the least-squares problem with a linear constraint in the medium-to-high SNR region. With deterministic knowledge of the errors in the upstream channels, a genie-aided decoder can further improve the performance. We give the union bound for the genie decoder, which is straightforward to evaluate. By analyzing these decoders, we find that knowledge about the upstream channels is essential for good sink decoding., © 2009 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 20111101. Updated from conference paper to article, VR Project

We investigate sink decoding approaches and performance analysis for a network with intermediate node encoding (coded network). The network consists of statistically independent noisy channels. The sink bit error probability (BEP) is the performance measure. First, we investigate soft-decision decoding without statistical information on the upstream channels (the channels not directly connected to the sink). Numerical results show that the decoder cannot significantly improve the performance from a hard-decision decoder. We develop union bounds for analysis. The bounds show the asymptotic (regarding SNR: signal-to-noise ratio) performance of the decoder. Using statistical information about the upstream channels, we can find the error patterns of final hop channels (channels directly connected to sinks).With the error patterns, maximum-likelihood (ML) decoding can be performed, and a significant improvement in the BEP is obtained. To evaluate the union bound for the ML decoder, we use an equivalent point procedure. It is reduced to the least-squares problem with a linear constraint in the medium-to-high SNR region. With deterministic knowledge of the errors in the upstream channels, a genie-aided decoder can further improve the performance. We give the union bound for the genie decoder, which is straightforward to evaluate. By analyzing these decoders, we find that knowledge about the upstream channels is essential for good sink decoding., © 2009 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 20111101. Updated from conference paper to article, VR Project

We investigate sink decoding approaches and performance analysis for a network with intermediate node encoding (coded network). The network consists of statistically independent noisy channels. The sink bit error probability (BEP) is the performance measure. First, we investigate soft-decision decoding without statistical information on the upstream channels (the channels not directly connected to the sink). Numerical results show that the decoder cannot significantly improve the performance from a hard-decision decoder. We develop union bounds for analysis. The bounds show the asymptotic (regarding SNR: signal-to-noise ratio) performance of the decoder. Using statistical information about the upstream channels, we can find the error patterns of final hop channels (channels directly connected to sinks).With the error patterns, maximum-likelihood (ML) decoding can be performed, and a significant improvement in the BEP is obtained. To evaluate the union bound for the ML decoder, we use an equivalent point procedure. It is reduced to the least-squares problem with a linear constraint in the medium-to-high SNR region. With deterministic knowledge of the errors in the upstream channels, a genie-aided decoder can further improve the performance. We give the union bound for the genie decoder, which is straightforward to evaluate. By analyzing these decoders, we find that knowledge about the upstream channels is essential for good sink decoding., © 2009 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 20111101. Updated from conference paper to article, VR Project

We consider downlink transmission in multi-cell wireless networks where in each cell one base station is serving multiple mobile terminals. There is no a priori channel state information (CSI) available at base stations and mobile terminals. We propose a low-complexity pilot-assisted opportunistic user scheduling (PAOUS) scheme. The proposed scheme operates in four subsequent phases: channel training; feedback transmission; user scheduling; and data transmission. We deploy an orthogonal pilot-assisted channel training scheme for acquiring CST at mobile terminals. Consequently, each mobile terminal obtains a noisy estimation of the corresponding local CST (i.e. channel gains from base stations to the mobile terminal). Then, it makes a local decision based on the estimated channel gains of the interfering links (i.e. the links between base stations in neighboring cells and the mobile terminal) and sends a one-bit feedback signal to the base station of the corresponding cell. Each base station schedules one mobile terminal for communication. We compute the achievable rate region and the achievable degrees of freedom (DoF) of the proposed transmission scheme. Our results show that in a multi-cell network with K base stations and coherence time T, the total DoF K-opt (1 - K-opt/T) is achievable given that the number of mobile terminals in each cell scales proportional to signal-to-noise-ratio. Since limited radio resources are available, only a subset of base stations should be activated, where the optimum number of active base stations is K-opt = min {K, T/2}. This recommends that in large networks (K > T/2), select only a subset of the base stations to be active and perform the PAOUS scheme within the cells associated to these base stations. Our results reveal that, even with single antenna at base stations and no a priori CSI at terminals, a non-trivial DoF gain can be achieved. We also investigate the power allocation between channel training and data transmission, QC 20160923

In this work, we address the problem of channel estimation and precoding / combining for the so-called hybrid millimeter wave (mmWave) MIMO architecture. Our proposed channel estimation scheme exploits channel reciprocity in TDD MIMO systems, by using echoing, thereby allowing us to implement Krylov subspace methods in a fully distributed way. The latter results in estimating the right (resp. left) singular subspace of the channel at the transmitter (resp. receiver). Moreover, we also tackle the problem of subspace decomposition whereby the estimated right (resp. left) singular subspaces are approximated by a cascade of analog and digital precoder (resp. combiner), using an iterative method. Finally we compare our scheme with an equivalent fully digital case and conclude that a relatively similar performance can be achieved, however, with a drastically reduced number of RF chains - 4 ~ 8 times less (i.e., massive savings in cost and power consumption)., QC 20151217

In this work, we address the problem of channel estimation and precoding / combining for the so-called hybrid millimeter wave (mmWave) MIMO architecture. Our proposed channel estimation scheme exploits channel reciprocity in TDD MIMO systems, by using echoing, thereby allowing us to implement Krylov subspace methods in a fully distributed way. The latter results in estimating the right (resp. left) singular subspace of the channel at the transmitter (resp. receiver). Moreover, we also tackle the problem of subspace decomposition whereby the estimated right (resp. left) singular subspaces are approximated by a cascade of analog and digital precoder (resp. combiner), using an iterative method. Finally we compare our scheme with an equivalent fully digital case and conclude that a relatively similar performance can be achieved, however, with a drastically reduced number of RF chains - 4 ~ 8 times less (i.e., massive savings in cost and power consumption)., QC 20151217

In this work, we address the problem of channel estimation and precoding / combining for the so-called hybrid millimeter wave (mmWave) MIMO architecture. Our proposed channel estimation scheme exploits channel reciprocity in TDD MIMO systems, by using echoing, thereby allowing us to implement Krylov subspace methods in a fully distributed way. The latter results in estimating the right (resp. left) singular subspace of the channel at the transmitter (resp. receiver). Moreover, we also tackle the problem of subspace decomposition whereby the estimated right (resp. left) singular subspaces are approximated by a cascade of analog and digital precoder (resp. combiner), using an iterative method. Finally we compare our scheme with an equivalent fully digital case and conclude that a relatively similar performance can be achieved, however, with a drastically reduced number of RF chains - 4 ~ 8 times less (i.e., massive savings in cost and power consumption)., QC 20151217

In this work, we address the problem of channel estimation and precoding / combining for the so-called hybrid millimeter wave (mmWave) MIMO architecture. Our proposed channel estimation scheme exploits channel reciprocity in TDD MIMO systems, by using echoing, thereby allowing us to implement Krylov subspace methods in a fully distributed way. The latter results in estimating the right (resp. left) singular subspace of the channel at the transmitter (resp. receiver). Moreover, we also tackle the problem of subspace decomposition whereby the estimated right (resp. left) singular subspaces are approximated by a cascade of analog and digital precoder (resp. combiner), using an iterative method. Finally we compare our scheme with an equivalent fully digital case and conclude that a relatively similar performance can be achieved, however, with a drastically reduced number of RF chains - 4 ~ 8 times less (i.e., massive savings in cost and power consumption)., QC 20151217

In this work, we address the problem of channel estimation and precoding / combining for the so-called hybrid millimeter wave (mmWave) MIMO architecture. Our proposed channel estimation scheme exploits channel reciprocity in TDD MIMO systems, by using echoing, thereby allowing us to implement Krylov subspace methods in a fully distributed way. The latter results in estimating the right (resp. left) singular subspace of the channel at the transmitter (resp. receiver). Moreover, we also tackle the problem of subspace decomposition whereby the estimated right (resp. left) singular subspaces are approximated by a cascade of analog and digital precoder (resp. combiner), using an iterative method. Finally we compare our scheme with an equivalent fully digital case and conclude that a relatively similar performance can be achieved, however, with a drastically reduced number of RF chains - 4 ~ 8 times less (i.e., massive savings in cost and power consumption)., QC 20151217

We construct motion-adaptive transforms for image sequences by using the eigenvectors of Laplacian matrices defined on vertex-weighted graphs, where the weights of the vertices are defined by scale factors. The vertex weights determine only the first basis vector of the linear transform uniquely. Therefore, we use these weights to define two Laplacians of vertex-weighted graphs. The eigenvectors of each Laplacian share the first basis vector as defined by the scale factors only. As the first basis vector is common for all considered Laplacians, we refer to it as subspace constraint. The first Laplacian uses the inverse scale factors, whereas the second utilizes the scale factors directly. The scale factors result from the assumption of ideal motion. Hence, the ideal unscaled pixels are equally connected and we are free to form arbitrary graphs, such as complete graphs, ring graphs, or motion-inherited graphs. Experimental results on energy compaction show that the Laplacian which is based on the inverse scale factors outperforms the one which is based on the direct scale factors. Moreover, Laplacians of motion-inherited graphs are superior than that of complete or ring graphs, when assessing the energy compaction of the resulting motion-adaptive transforms., QC 20150410

We consider a scenario consisting of a primary and asecondary system, each represented by a pair of a transmitter anda receiver. The secondary transmitter assists in the retransmission of the primary message, which prevents the primary performance from being degraded by allowing the secondary system to access the transmission resources. Two network coding schemes applied in retransmission phase are investigated, the stationary network coding (SNC) scheme and the adaptive network coding(ANC) scheme. For each scheme we derive analytical results on packet throughput and infer that the ANC scheme outperforms the SNC scheme. We then provide a numerical performance comparison and a numerical optimization of the secondary packet throughput. Our main result shows cooperation can provide a significant performance improvement through effective network coding., QC 20150410

A lossy source coding problem with secrecy constraints is considered where a remote information source should be transmitted to a single destination via multiple agents in the presence of an eavesdropper. The agents observe noisy versions of the source and independently encode and transmit their observations to the destination via noiseless rate-limited links. Unbeknownst to the agents, an eavesdropper intercepts one of the links from the agents to the destination to learn as much as possible about the source. The destination should estimate the remote source subject to a mean distortion threshold. This problem can be viewed as the CEO problem with addition of secrecy constraints. We establish inner and outer bounds on the rate-distortion-equivocation region. In addition, we provide the optimal rate-distortion-equivocation region for the quadratic Gaussian case when the eavesdropper has no side information., QC 20150227

Luby transform (LT) codes are a class of rateless codes that automatically adapt their rate to the quality of the communication channel. In the original LT codes, fixed check-node degree distributions are used to combine variable nodes uniformly at random to extend the code graph and produce code bits. Here we propose a different approach: we design a sequence of rate-compatible degree distributions, and develop an algorithm that produces code bits in a manner such that the resulting degree distributions follow the designed sequence. Using this new design framework, we develop low-complexity LT codes suitable for time-varying noisy channels. Performance and complexity of the proposed LT codes are measured in terms of bit error rate and average number of edges per information and coded bit, respectively. Numerical examples illustrate the resulting trade-off between performance and complexity of the designed LT codes., QC 20150227

We consider the problem of lossy source coding with side information under a privacy constraint that the reconstruction sequence at a decoder should be kept secret to a certain extent from another terminal such as an eavesdropper, a sender, or a helper. We are interested in how the reconstruction privacy constraint at a particular terminal affects the rate-distortion tradeoff. In this work, we allow the decoder to use a random mapping, and give inner and outer bounds to the rate-distortion-equivocation region for different cases. In the case where each reconstruction symbol depends only on the source description and current side information symbol, the complete rate-distortion-equivocation region is provided. A binary example illustrating a new tradeoff due to the new privacy constraint, and a gain from the use of randomized decoder is given., QC 20150227

In this work, we study the so-called leakage minimization problem, within the context of interference alignment (IA). For that purpose, we propose a novel approach based on controlled perturbations of the leakage function, and show how the latter can be used as a mechanism to control the algorithm's convergence (and thus tradeoff convergence speed for reliability). Although the proposed scheme falls under the broad category of stochastic optimization, we show through simulations that it has a quasi-deterministic convergence that we exploit to improve on the worst case performance of its predecessor, resulting in significantly better sum-rate capacity and average cost function value., QC 20140602, METIS

We consider a downlink two-hop MISO broadcast network with a 2-antenna source communicating to 2 single-antenna destinations, via 2 single-antenna relays. We investigate spectrally efficient transmission and the associated achievable sum degrees of freedom (DoF) with mixed channel state information at the transmitter (CSIT), which consists of perfect delayed CSIT and imperfect instantaneous CSIT. When the variance of the estimation error of the instantaneous CSIT lies on level of O(P-α) for the transmission power P and some α [0, 1], we show that the sum DoF 4-2α over 3-2α [4 over 3, 2] can be achieved by a novel interference alignment (IA) scheme. The result shows that rather than exploiting only delayed or imperfect instantaneous CSIT, the transmission design taking advantages of both can achieve higher sum DoF., QC 20140912

We consider a secure source coding problem with side informations at the decoder and the eavesdropper. The encoder has a source that it wishes to describe with limited distortion through a rate-limited link to a legitimate decoder. The message sent is also observed by the eavesdropper. The encoder aims to minimize both the distortion incurred by the legitimate decoder; and the information leakage rate at the eavesdropper. When the encoder has access to the side information (S.I.) at the decoder, we characterize the rate-distortion-information leakage rate (R.D.I.) region under a Markov chain assumption and when S.I. at the encoder does not improve the rate-distortion region as compared to the case when S.I. is absent. We then extend our setting to consider the case where the encoder and decoder obtain coded S.I. through a rate-limited helper, and characterize the R.D.I. region for several special cases under logarithmic loss distortion (log-loss). Finally, we consider the case of list or entropy constraints at the decoder and show that the R.D.I. region coincides with R.D.I. region under log-loss., QC 20140312

This note proves that, for any finite set of correlated discrete i.i.d. 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. Based on this, we address a variation of the data compression problem which considers recovering some discrete function of the data. It is demonstrated that linear encoder over non-field ring strictly outperforms its field counterpart for encoding some function in terms of achieving strictly larger achievable region with strictly smaller alphabet size., QC 20140311

In this work, we study the so-called leakage minimization problem, within the context of interference alignment (IA). For that purpose, we propose a novel approach based on controlled perturbations of the leakage function, and show how the latter can be used as a mechanism to control the algorithm's convergence (and thus tradeoff convergence speed for reliability). Although the proposed scheme falls under the broad category of stochastic optimization, we show through simulations that it has a quasi-deterministic convergence that we exploit to improve on the worst case performance of its predecessor, resulting in significantly better sum-rate capacity and average cost function value., QC 20140602, METIS

We study the achievable sum degrees of freedom(DoF) in a class of wireless single-antenna multi-hop half-duplexrelay networks. The networks contain Ms information sources, Md information destinations, and arbitrary layers of relays, each with 2K (K ≥ max{Ms,Md}) half-duplex relays, in between. A cluster successive relaying transmission scheme is applied to conduct the communication: We divide the relays in each layer into two equal-size clusters and activate them successively to efficiently use the available channel. It is shown that in a time-varying fading environment this scheme asymptotically achieves the sum DoF min{MsK/(Ms+K) , MdK/(Md+K−1)}, which is irrelevant to the number of hops the source messages have to pass through. This result also implies that, when the number of relays in each layeris infinitely large, the available DoF (i.e. the optimally achievable sum DoF) of the considered networks is min{Ms,Md}. Neither distributed signal processing nor multiple layers of half-duplex relay operation negatively affects the system DoF performance., QC 20130903

Operation-intensive exhaustive checking is the unique optimal method for performing joint maximum-likelihood (JML) data and channel estimation. Thus, several suboptimal methods incorporating conventional maximum-likelihood sequence estimation (MLSE) or its extensions have been proposed in order to reduce complexity. However, most of them suffer from either long convergence delay or unsatisfactory performance. Therefore, based on a two-phase MLSE algorithm designed for partially coherent receivers, we propose a novel low-complexity and low-latency batch algorithm for JML process. The proposed algorithm first searches for several of the best candidate training sequences using priority-first search, and then applies the training sequences to the two-phase MLSEs to obtain the maximum-likelihood data sequence. Simulation results show that when the candidate training sequence length is adequate, the proposed algorithm can achieve the performance of optimal exhaustive checking., QC 20140121

The problem of feedback stabilization of LTI plants over a Gaussian interference channel is considered. Two plants with arbitrary distributed initial states are monitored by two separate sensors which communicate their measurements to two separate controllers over a Gaussian interference channel under average transmit power constraints. The necessary conditions for mean square-stabilization over a memoryless symmetric Gaussian interference channel are derived. These conditions are shown to be tight for some system parameters. Further it is shown that linear memoryless sensing and control schemes are optimal for stabilization in some special cases., QC 20140128

We study the low SNR scaling of the non-coherent secret-key agreement capacity over a reciprocal, block-fading channel. For the restricted class of strategies, where one of the nodes is constrained to transmit pilot-only symbols, we show that the secret-key capacity scales as SNR ·log T if T ≤ 1/SNR, where T denotes the coherence period, and as SNR·log(1/SNR) otherwise. Our upper bound is inspired by the genie-aided argument of Borade and Zheng (IT-Trans 2010). Our lower bound is based on bursty communication, channel training, and secret message transmission., QC 20140121

A transmission scheme for mean square stabilization of two linear systems over a Gaussian interference relay channel is studied. A delay-free linear sensing and control strategy is proposed and an achievable stability region is derived. It shows that the stability region can be significantly enlarged by deploying a relay node in such a multi-user Gaussian channels. Furthermore we observe that the separation structure between estimation and control is inadequate in high interference regime., QC 20140121

We consider a distributed detection problem where two nodes, or decision makers, observe a common source and aim to decide on one of several hypotheses. Before making their individual decisions, the nodes are allowed to communicate over rate-constrained links, through a bidirectional relay. We show that if the rate of the common relay-to-node link is greater than or equal to the rate of the individual node-to-relay links, and the individual decisions are not coupled by the cost metric, then network coding at the relay allows the overall problem to decouple into two separate two-node distributed detection problems over serial networks; and the two serial networks can be designed independently. However, if the rate of the relay-to-node link is strictly less than the node-to-relay links, no such decoupling can be assumed in general, and the overall detection network needs to be jointly designed., QC 20131210

Bidirectional wireless communication between two multi-antenna terminals that operate in a full-duplex mode is considered. The effects of loopback self-interference and transmit-side noise due to radio front-end imperfections are analyzed in the asymptotic regime where the number of antennas at both terminals grows large. The study compares two transparent mitigation schemes by which conventional spatial multiplexing can operate without being aware of the self-interference issue: spatial-domain null-space projection and time-domain subtractive cancellation. Both schemes eliminate the information-bearing part of the self-interference perfectly, but due to transmitter noise, the subtractive cancellation suffers from residual self-interference. The receive-side null-space projection, on the other hand, suppresses also the transmit-side noise, but at the cost of reduction in the degrees of freedom available for data reception. The results indicate that suppression becomes preferred when the level of transmit-side noise increases and the receiver does not have instantaneous knowledge about the spatial structure of the self-interference. Exhaustive Monte Carlo simulations also show that the asymptotic analytical results are useful for evaluating the performance of practical not-so-large systems., QC 20140217

Reconstruction fidelity of sparse signals contaminated by sparse noise is considered. Statistical mechanics inspired tools are used to show that the l(1)-norm based convex optimization algorithm exhibits a phase transition between the possibility of perfect and imperfect reconstruction. Conditions characterizing this threshold are derived and the mean square error of the estimate is obtained for the case when perfect reconstruction is not possible. Detailed calculations are provided to expose the mathematical tools to a wide audience., QC 20140625

In this work, we study the so-called leakage minimization problem, within the context of interference alignment (IA). For that purpose, we propose a novel approach based on controlled perturbations of the leakage function, and show how the latter can be used as a mechanism to control the algorithm's convergence (and thus tradeoff convergence speed for reliability). Although the proposed scheme falls under the broad category of stochastic optimization, we show through simulations that it has a quasi-deterministic convergence that we exploit to improve on the worst case performance of its predecessor, resulting in significantly better sum-rate capacity and average cost function value., QC 20140602, METIS

In this work, we study the so-called leakage minimization problem, within the context of interference alignment (IA). For that purpose, we propose a novel approach based on controlled perturbations of the leakage function, and show how the latter can be used as a mechanism to control the algorithm's convergence (and thus tradeoff convergence speed for reliability). Although the proposed scheme falls under the broad category of stochastic optimization, we show through simulations that it has a quasi-deterministic convergence that we exploit to improve on the worst case performance of its predecessor, resulting in significantly better sum-rate capacity and average cost function value., QC 20140602, METIS

In this work, we study the so-called leakage minimization problem, within the context of interference alignment (IA). For that purpose, we propose a novel approach based on controlled perturbations of the leakage function, and show how the latter can be used as a mechanism to control the algorithm's convergence (and thus tradeoff convergence speed for reliability). Although the proposed scheme falls under the broad category of stochastic optimization, we show through simulations that it has a quasi-deterministic convergence that we exploit to improve on the worst case performance of its predecessor, resulting in significantly better sum-rate capacity and average cost function value., QC 20140602, METIS

We analyze the traffic carrying capacity of a single-input single-output system for a given delay guarantee at the flow level. A continuous time ON/OFF data source is considered as the traffic model while the wireless channel is modeled using the Gilbert-Elliot model. We study the impact of the delay bound violation probability, traffic burstiness, channel memory and the delay requirements at the network layer on the throughput under different signal strength., QC 20130111

We consider the problem of detecting a sparse random signal from the compressive measurements without reconstructing the signal. Using a subspace model for the sparse signal where the signal parameters are drawn according to Gaussian law, we obtain the detector based on Neyman-Pearson criterion and analytically determine its operating characteristics when the signal covariance is known. These results are extended to situations where the covariance cannot be estimated. The results can be used to determine the number of measurements needed for a particular detector performance and also illustrate the presence of an optimal support for a given number of measurements., QC 20121119

We develop a greedy pursuit algorithm for solving the distributed compressed sensing problem in a connected network. This algorithm is based on subspace pursuit and uses the mixed support-setsignal model. Through experimental evaluation, we show that the distributed algorithm performs significantly better than the standalone (disconnected) solution and close to a centralized (fully connected to a central point) solution., QC 20121114

We propose Gaussian process dynamical models (GPDMs) as a new, nonparametric paradigm in acoustic models of speech. These use multidimensional, continuous state-spaces to overcome familiar issues with discrete-state, HMM-based speech models. The added dimensions allow the state to represent and describe more than just temporal structure as systematic differences in mean, rather than as mere correlations in a residual (which dynamic features or AR-HMMs do). Being based on Gaussian processes, the models avoid restrictive parametric or linearity assumptions on signal structure. We outline GPDM theory, and describe model setup and initialization schemes relevant to speech applications. Experiments demonstrate subjectively better quality of synthesized speech than from comparable HMMs. In addition, there is evidence for unsupervised discovery of salient speech structure., QC 20120308, LISTA

A pair of secondary (cognitive, unlicensed) users is communicating in the presence of multiple primary (licensed) user pairs. The cognitive transceiver is implementing beamforming and orthogonal space-time block coding in the presence of external interference, induced by the primary system transmission, that has to be properly handled. Moreover, the cognitive link nodes, both the transmitter and the receiver, are supplied with different levels of network side information (NSI), i.e. primary messages and partial channel side information (CSI). We investigate how this side information can be taken into account in the cognitive system design and how interference affects the behaviour of the beamforming solution. Through numerical simulations, we illustrate the impact of partial CSI on system performance and discuss its implications on the feasibility of cognitive systems., QC 20121218

A secret key agreement setup between three users is considered in which each pair of them wishes to agree on a secret key hidden from the remaining user. The three users observe i.i.d. outputs of correlated sources and there is a noiseless public channel from each user for communication to the others. In this setup, inner and outer bounds of the secret key capacity region is derived. Moreover, some special cases are obtained in which the inner bound coincides the explicit outer bound. Also a binary-erasure example is presented through which the results are examined., QC 20130115