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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

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

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 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

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 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

Collecting sensory data at access point (AP) from large number of sensor nodes with low latency is a critical issue. In Wi-Fi, prior to uplink data delivery, AP typically needs to poll large number of sensor nodes sequentially and allocate channel resources to individual node resulting in large latency. An efficient method to reduce the latency and power consumption in wireless sensor networks is to parallelize the polling operation so that multiple nodes can concurrently respond to the poll request of an AP by sending orthogonal sequences with uplink power control. In this paper, we present a conceptually simple uplink power control scheme for the parallel polling operation between AP and low power sensor nodes. We formulate the uplink power control problem as a sequence design problem and show that uplink channel state information (CSI) required to achieve a given target receive SNR can be significantly reduced by carefully designing sequences. We further develop a low complexity instantaneous (fast) power control scheme in order to reduce the number of computations required by the low power sensor node. We also analyze and compare the detection performance of the instantaneous (fast) and average (slow) power control schemes in terms of diversity gain., QC 20140317

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

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 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

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

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

In this paper, we investigate the problem of successive refinement with side information (SI) under secrecy constraint. In particular, under classical successive refinement coding scheme, there are degraded SI sequences Y-n and Z(n) at two decoders and E-n at the eavesdropper. Based on the status of two switches, three different cases are investigated. In case 1 and 3, the eavesdropper only observes output of encoder 1 and 2, respectively, while in case 2, the eavesdropper observes outputs of both encoder 1 and 2. The Markov chain X - Y - (Z, E) holds in all cases. The equivocation is measured by the normalized entropy of source sequence conditioned on the observation of eavesdropper. We completely characterize the rate-distortion-equivocation regions for all three cases, and show that layered coding is optimal. Finally, a binary source example is given., QC 20150227

This paper briefly presents and evaluates recent advances in statistical methods for improving inter-view inconsistency in multiview depth imagery. View synthesis is vital in free-viewpoint television in order to allow viewers to move freely in a dynamic scene. Here, depth image-based rendering plays a pivotal role by synthesizing an arbitrary number of novel views by using a subset of captured views and corresponding depth maps only. Usually, each depth map is estimated individually at different viewpoints by stereo matching and, hence, shows lack of inter-view consistency. This lack of consistency affects the quality of view synthesis negatively. This paper discusses two different approaches to enhance the inter-view depth consistency. The first one uses generative models based on multiview color and depth classification to assign a probabilistic weight to each depth pixel. The weighted depth pixels are utilized to enhance depth maps. The second one performs inter-view consistency testing in depth difference space to enhance the depth maps at multiple viewpoints. We comparatively evaluate these two methods and discuss their pros and cons for future work., QC 20150109

This paper investigates a new type of typicality for sequences, termed Supremus typical sequences, in both the strong and the weak senses. It is seen that Supremus typicality is a condition stronger than classic typicality in both the strong and the weak senses. Even though Supremus typical sequences form a (often strictly smaller) subset of classic typical sequences, the Asymptotic Equipartion Property is still valid for Supremus typical sequences. Furthermore, Supremus typicality leads to a generalized typicality lemma that is more accessible and easier to analyze than its classic counterpart., QC 20150227

This paper presents for the first time the testbed implementation of an iterative interference alignment and power control algorithm for downlink transmission in a multiple-input multiple-output (MIMO) cellular network. The network is composed of three cells where within each cell one base station (BS) communicates with one mobile station (MS). Each terminal is equipped with two antennas. All the BSs transmit at the same time and the same frequency band. Transmitter beamforming vectors and receiver filtering vectors are computed according to the interference alignment concept, and power control is performed to guarantee successful communication of each BS-MS pair at a desired fixed rate. The indoor measurements performed on an universal software radio peripheral (USRP) based test-bed, show that the power can be reduced by at least 4 dB, 90% of the time, while at the same time reducing the bit-error-rate (BER)., QC 20150227

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

This paper considers the capacity limit of an uplink wideband CDMA (WCDMA) system assuming imperfect channel state information at the receiver (CSIR). In order to make the studied results useful for the performance assessment of real cellular networks, various realistic assumptions are included in the problem. A discrete-time channel model is derived based on the mismatched filtering at the receiver. Capacity inner bounds are then characterized based on the discrete-time channel model considering different assumptions on decoding strategy. Numerical results are also provided to show the effect of imperfect CSIR on the capacity., QC 20151211

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

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

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 paper, we study the problem of privacy information leakage in a smart grid. The privacy risk is assumed to be caused by an unauthorized binary hypothesis testing of the consumer's behaviour based on the smart meter readings of energy supplies from the energy provider. Another energy supplies are produced by an alternative energy source. A controller equipped with an energy storage device manages the energy inflows to satisfy the energy demand of the consumer. We study the optimal energy control strategy which minimizes the asymptotic exponential decay rate of the minimum Type II error probability in the unauthorized hypothesis testing to suppress the privacy risk. Our study shows that the cardinality of the energy supplies from the energy provider for the optimal control strategy is no more than two. This result implies a simple objective of the optimal energy control strategy. When additional side information is available for the adversary, the optimal control strategy and privacy risk are compared with the case of leaking smart meter readings to the adversary only., QC 20160121

In this paper, we study the problem of privacy information leakage in a smart grid. The privacy risk is assumed to be caused by an unauthorized binary hypothesis testing of the consumer's behaviour based on the smart meter readings of energy supplies from the energy provider. Another energy supplies are produced by an alternative energy source. A controller equipped with an energy storage device manages the energy inflows to satisfy the energy demand of the consumer. We study the optimal energy control strategy which minimizes the asymptotic exponential decay rate of the minimum Type II error probability in the unauthorized hypothesis testing to suppress the privacy risk. Our study shows that the cardinality of the energy supplies from the energy provider for the optimal control strategy is no more than two. This result implies a simple objective of the optimal energy control strategy. When additional side information is available for the adversary, the optimal control strategy and privacy risk are compared with the case of leaking smart meter readings to the adversary only., QC 20160121

In this paper, we study the problem of privacy information leakage in a smart grid. The privacy risk is assumed to be caused by an unauthorized binary hypothesis testing of the consumer's behaviour based on the smart meter readings of energy supplies from the energy provider. Another energy supplies are produced by an alternative energy source. A controller equipped with an energy storage device manages the energy inflows to satisfy the energy demand of the consumer. We study the optimal energy control strategy which minimizes the asymptotic exponential decay rate of the minimum Type II error probability in the unauthorized hypothesis testing to suppress the privacy risk. Our study shows that the cardinality of the energy supplies from the energy provider for the optimal control strategy is no more than two. This result implies a simple objective of the optimal energy control strategy. When additional side information is available for the adversary, the optimal control strategy and privacy risk are compared with the case of leaking smart meter readings to the adversary only., QC 20160121

In this paper, we study the problem of privacy information leakage in a smart grid. The privacy risk is assumed to be caused by an unauthorized binary hypothesis testing of the consumer's behaviour based on the smart meter readings of energy supplies from the energy provider. Another energy supplies are produced by an alternative energy source. A controller equipped with an energy storage device manages the energy inflows to satisfy the energy demand of the consumer. We study the optimal energy control strategy which minimizes the asymptotic exponential decay rate of the minimum Type II error probability in the unauthorized hypothesis testing to suppress the privacy risk. Our study shows that the cardinality of the energy supplies from the energy provider for the optimal control strategy is no more than two. This result implies a simple objective of the optimal energy control strategy. When additional side information is available for the adversary, the optimal control strategy and privacy risk are compared with the case of leaking smart meter readings to the adversary only., QC 20160121

In this paper, we study the problem of privacy information leakage in a smart grid. The privacy risk is assumed to be caused by an unauthorized binary hypothesis testing of the consumer's behaviour based on the smart meter readings of energy supplies from the energy provider. Another energy supplies are produced by an alternative energy source. A controller equipped with an energy storage device manages the energy inflows to satisfy the energy demand of the consumer. We study the optimal energy control strategy which minimizes the asymptotic exponential decay rate of the minimum Type II error probability in the unauthorized hypothesis testing to suppress the privacy risk. Our study shows that the cardinality of the energy supplies from the energy provider for the optimal control strategy is no more than two. This result implies a simple objective of the optimal energy control strategy. When additional side information is available for the adversary, the optimal control strategy and privacy risk are compared with the case of leaking smart meter readings to the adversary only., QC 20160121

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

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

In this paper, we study an optimal transmit strategy for multiple-input single-output (MISO) Gaussian channels with joint sum and per-antenna power constraints. We study in detail the interesting case where the sum of the per-antenna power constraints is larger than sum power constraint. A closed-form characterization of an optimal beamforming strategy is derived.It is shown that we can always find an optimal beamforming transmit strategy that allocates the maximal sum power with phases matched to the complex channel coefficients. The main result is a simple recursive algorithm to compute the optimal power allocation. Whenever the optimal power allocation of the corresponding problem with sum power constraint only exceeds per-antenna power constraints, it is optimal to allocate maximal per-antenna power to those antennas to satisfy the per-antenna power constraints. The remaining power is divided amongst the other antennas whose optimal allocation follows from a reduced joint sum and per-antenna power constraints problem of smaller channel coefficient dimension and reduced sum power constraint. Finally, the theoretical results are illustrated by numerical examples., QC 20160607

In this paper, we study an optimal transmit strategy for multiple-input single-output (MISO) Gaussian channels with joint sum and per-antenna power constraints. We study in detail the interesting case where the sum of the per-antenna power constraints is larger than sum power constraint. A closed-form characterization of an optimal beamforming strategy is derived.It is shown that we can always find an optimal beamforming transmit strategy that allocates the maximal sum power with phases matched to the complex channel coefficients. The main result is a simple recursive algorithm to compute the optimal power allocation. Whenever the optimal power allocation of the corresponding problem with sum power constraint only exceeds per-antenna power constraints, it is optimal to allocate maximal per-antenna power to those antennas to satisfy the per-antenna power constraints. The remaining power is divided amongst the other antennas whose optimal allocation follows from a reduced joint sum and per-antenna power constraints problem of smaller channel coefficient dimension and reduced sum power constraint. Finally, the theoretical results are illustrated by numerical examples., QC 20160607

In this paper, we study an optimal transmit strategy for multiple-input single-output (MISO) Gaussian channels with joint sum and per-antenna power constraints. We study in detail the interesting case where the sum of the per-antenna power constraints is larger than sum power constraint. A closed-form characterization of an optimal beamforming strategy is derived.It is shown that we can always find an optimal beamforming transmit strategy that allocates the maximal sum power with phases matched to the complex channel coefficients. The main result is a simple recursive algorithm to compute the optimal power allocation. Whenever the optimal power allocation of the corresponding problem with sum power constraint only exceeds per-antenna power constraints, it is optimal to allocate maximal per-antenna power to those antennas to satisfy the per-antenna power constraints. The remaining power is divided amongst the other antennas whose optimal allocation follows from a reduced joint sum and per-antenna power constraints problem of smaller channel coefficient dimension and reduced sum power constraint. Finally, the theoretical results are illustrated by numerical examples., QC 20160607

In this paper, we study an optimal transmit strategy for multiple-input single-output (MISO) Gaussian channels with joint sum and per-antenna power constraints. We study in detail the interesting case where the sum of the per-antenna power constraints is larger than sum power constraint. A closed-form characterization of an optimal beamforming strategy is derived.It is shown that we can always find an optimal beamforming transmit strategy that allocates the maximal sum power with phases matched to the complex channel coefficients. The main result is a simple recursive algorithm to compute the optimal power allocation. Whenever the optimal power allocation of the corresponding problem with sum power constraint only exceeds per-antenna power constraints, it is optimal to allocate maximal per-antenna power to those antennas to satisfy the per-antenna power constraints. The remaining power is divided amongst the other antennas whose optimal allocation follows from a reduced joint sum and per-antenna power constraints problem of smaller channel coefficient dimension and reduced sum power constraint. Finally, the theoretical results are illustrated by numerical examples., QC 20160607

In this paper, we study an optimal transmit strategy for multiple-input single-output (MISO) Gaussian channels with joint sum and per-antenna power constraints. We study in detail the interesting case where the sum of the per-antenna power constraints is larger than sum power constraint. A closed-form characterization of an optimal beamforming strategy is derived.It is shown that we can always find an optimal beamforming transmit strategy that allocates the maximal sum power with phases matched to the complex channel coefficients. The main result is a simple recursive algorithm to compute the optimal power allocation. Whenever the optimal power allocation of the corresponding problem with sum power constraint only exceeds per-antenna power constraints, it is optimal to allocate maximal per-antenna power to those antennas to satisfy the per-antenna power constraints. The remaining power is divided amongst the other antennas whose optimal allocation follows from a reduced joint sum and per-antenna power constraints problem of smaller channel coefficient dimension and reduced sum power constraint. Finally, the theoretical results are illustrated by numerical examples., QC 20160607

In this paper, an energy flow control strategy to reduce the smart meter privacy leakage is studied. The considered smart grid is equipped with an energy storage device. The privacy leakage is modeled as optimal Bayesian detections on the behaviors of the consumer made by an authorized adversary. To evaluate the privacy risk, a Bayesian detection-operational privacy leakage metric is proposed. The design of an optimal privacy-preserving energy control strategy can be formulated as a belief state MDP problem. Therefore, standard methods and algorithms can be utilized to obtain or to approximate the optimal control strategy. A simplified problem to design an instantaneous optimal privacy-preserving control strategy is also considered. It is shown that the problem of the instantaneous optimal control strategy design can be formulated as a set of linear programmings., QC 20160401

In this paper, an energy flow control strategy to reduce the smart meter privacy leakage is studied. The considered smart grid is equipped with an energy storage device. The privacy leakage is modeled as optimal Bayesian detections on the behaviors of the consumer made by an authorized adversary. To evaluate the privacy risk, a Bayesian detection-operational privacy leakage metric is proposed. The design of an optimal privacy-preserving energy control strategy can be formulated as a belief state MDP problem. Therefore, standard methods and algorithms can be utilized to obtain or to approximate the optimal control strategy. A simplified problem to design an instantaneous optimal privacy-preserving control strategy is also considered. It is shown that the problem of the instantaneous optimal control strategy design can be formulated as a set of linear programmings., QC 20160401

In this paper, an energy flow control strategy to reduce the smart meter privacy leakage is studied. The considered smart grid is equipped with an energy storage device. The privacy leakage is modeled as optimal Bayesian detections on the behaviors of the consumer made by an authorized adversary. To evaluate the privacy risk, a Bayesian detection-operational privacy leakage metric is proposed. The design of an optimal privacy-preserving energy control strategy can be formulated as a belief state MDP problem. Therefore, standard methods and algorithms can be utilized to obtain or to approximate the optimal control strategy. A simplified problem to design an instantaneous optimal privacy-preserving control strategy is also considered. It is shown that the problem of the instantaneous optimal control strategy design can be formulated as a set of linear programmings., QC 20160401

In this paper, an energy flow control strategy to reduce the smart meter privacy leakage is studied. The considered smart grid is equipped with an energy storage device. The privacy leakage is modeled as optimal Bayesian detections on the behaviors of the consumer made by an authorized adversary. To evaluate the privacy risk, a Bayesian detection-operational privacy leakage metric is proposed. The design of an optimal privacy-preserving energy control strategy can be formulated as a belief state MDP problem. Therefore, standard methods and algorithms can be utilized to obtain or to approximate the optimal control strategy. A simplified problem to design an instantaneous optimal privacy-preserving control strategy is also considered. It is shown that the problem of the instantaneous optimal control strategy design can be formulated as a set of linear programmings., QC 20160401

In this paper, an energy flow control strategy to reduce the smart meter privacy leakage is studied. The considered smart grid is equipped with an energy storage device. The privacy leakage is modeled as optimal Bayesian detections on the behaviors of the consumer made by an authorized adversary. To evaluate the privacy risk, a Bayesian detection-operational privacy leakage metric is proposed. The design of an optimal privacy-preserving energy control strategy can be formulated as a belief state MDP problem. Therefore, standard methods and algorithms can be utilized to obtain or to approximate the optimal control strategy. A simplified problem to design an instantaneous optimal privacy-preserving control strategy is also considered. It is shown that the problem of the instantaneous optimal control strategy design can be formulated as a set of linear programmings., QC 20160401