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In this paper we consider the coexistence of a single-input single-output (SISO) primary link with a multiple-input single-output (MISO) secondary user pair that has non-causal knowledge of the primary message. We study an achievable rate region that exploits this knowledge by combining selfless relaying to maintain the rate supported by the primary link with dirty paper coding to pre-cancel the interference at the secondary receiver. We find the optimal choice of power allocation between these operating modes at the secondary transmitter as well as the optimal beamforming vectors. Moreover, we address the robustness of the solution to uncertainties in the channel knowledge. Finally, we show by numerical evaluation the gains obtained due to the additional knowledge of the primary message. QC 20120905

Theoretic results have shown that the capacity of a channel does not decrease if the receiver observes the transmitted signal in the presence of interference, provided that the transmitter knows this interference non-causally. That is, if the transmitter has non-causal access to the interference, by using proper precoding this interference could be “avoided” (as if it were not present) under the same transmit power constraint. It indicates that lossless (in the sense of capacity) precoding is theoretically possible at any signal-to-noise-ratio (SNR). This is of special interest in digital watermarking, transmission for ISI channels as well as for MIMO broadcast channels. Recent research has elegantly demonstrated the (near) achievability of this “existence-type” result, while the complexity is notable. An interesting question is what one can do when very little extra complexity is permitted. This thesis treats such special cases of this problem in order to shed some light on this question. In the AWGN channel with additive interference, an optimum modulator is designed under the constraint of a binary signaling alphabet with binary interference.Tomlinson-Harashima precoding (THP), which is originally proposed for ISI channels, is improved by picking up optimized parameters and then taken as a benchmark. Simulation results show that the Optimum Modulator always outperforms the THP with optimized parameters. The difference in performance, in terms of mutual information between channel input and output as well as coded bit error rate with Turbo codes, is significant in many scenarios.

In this paper, we investigate receivers for vehicle-to- vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. Vehicular channels are characterized by multiple paths and time variations, which introduce challenges in the design of receivers. We propose an algorithm for IEEE 802.11p-compliant receivers, based on orthogonal frequency-division multiplexing (OFDM). We employ iterative structures in the receiver as a way to estimate the channel despite variations within a frame. The channel estimator is based on factor graphs (FGs), which allow the design of soft iterative receivers while keeping acceptable computational complexity. Throughout this paper, we focus on designing a receiver that offers a good complexity-performance tradeoff. Moreover, we propose a scalable algorithm to be able to tune the tradeoff, depending on the channel conditions. Our algorithm allows reliable communications while offering a considerable decrease in computational complexity. In particular, numerical results show the tradeoff between complexity and performance measured in computational time and bit error rate (BER), as well as frame error rate (FER) achieved by various interpolation lengths used by the estimator, which both outperform by decades the standard least squares (LS) solution. Furthermore, our adaptive algorithm shows a considerable improvement in terms of computational time and complexity against state-of-the-art and classical receptors while showing acceptable BER and FER performance. QC 20151021

Speech is the most important communication modality for human interaction. Automatic speech recognition and speech synthesis have extended further the relevance of speech to man-machine interaction. Environment noise and various distortions, such as reverberation and speech processing artifacts, reduce the mutual information between the message modulated inthe clean speech and the message decoded from the observed signal. This degrades intelligibility and perceived quality, which are the two attributes associated with quality of service. An estimate of the state of these attributes provides important diagnostic information about the communication equipment and the environment. When the adverse effects occur at the presentation side, an objective measure of intelligibility facilitates speech signal modification for improved communication. The contributions of this thesis come from non-intrusive quality assessment and intelligibility-enhancing modification of speech. On the part of quality, the focus is on predictor design for limited training data. Paper A proposes a quality assessment model for bounded-support ratings that learns efficiently from a limited amount of training data, scales easily with the sampling frequency, and provides a platform for modeling variations in the individual subjective ratings. The predictive performance of the model for the mean of the subjective quality ratings compares favorably to the state-of-art in the field. Patterns in the spread of the individual ratings are captured in the feature space of the training data. Paper B focuses on enhancing predictive performance for the mean of the quality variable when the signal feature space is sparsely sampled by the training data. Using a Gaussian Processes framework, the deterministic signal-based feature set is augmented with a stochastic feature that is hypothesized to be jointly distributed with the target quality rating. An uncertainty propagation mechanism ensures that the variance of this feature is reflected in the prediction. The proposed architecture can take advantage of i) data that cannot be pooled due to subjective test protocol incompatibility and ii) models trained on data that are no longer available. With respect to intelligibility enhancement, a hierarchical perspective of the speech communication process, extended from foundational work in the field, is used in paper C to create a unified framework for method analysis and comparison. A high-level intelligibility measure related to the probability for correct recognition is derived using a hit-or-miss distortion criterion in the transcription domain. The measure is used to optimize two speech modifications at different levels of the message encoding hierarchy leading to significantly enhanced intelligibility in noise. The conceptual novelty of the method comes at the cost of higher complexity and the requirement for additional information including message transcription, sound segmentation, and a model of speech. Mapping the high-level measure to a lower level takes away the need for additional information and preserves asymptotically high-level optimality. Two methods are proposed to reduce degradation in the accuracy of the spectral dynamics due to additive noise. The focus of paper D is dynamics preservation in a range that is lower-bounded by an optimal band-power threshold. The performance of the method is competitive but allows for improvement in power efficiency. This issue is addressed in paper E which proposes and optimizes a distortion measure for spectral dynamics leading to a significant increase in intelligibility. Use of functional optimization techniques allows for families of solutions, among which are dynamic range compressors adaptive to the statistics of the speech and the noise. QC 20140523

Since wireless communication has become a standard feature in the daily life, smartphones and tablets among other things are integrated with the Bluetooth technology. While in some parts of the day wireless communication can be used for searching the internet and share information on social networks without the need of having a secure connection, there are some other parts where the security might become of high importance. When the technology gets integrated in companies the security problem becomes more evident. This is because when the radio signals spread in the medium they can be accessed by anyone that is in reach in the network and the information that was sent may not be intended for everyone. To secure the network from unintended users becomes important when handling fragile information, which companies may deal with daily. This paper gives an introduction on which security features and techniques that already exist in some personal area networks. From this it has been clear that a security feature could be implemented on the baseband layer of Bluetooth to increase the secrecy during the transmission since at the moment security is only implemented on higher layers using encryption algorithms. This paper proposes a conceptual idea of improving the secrecy in the network by using a wiretap code that is implemented before the error-correction coding in the Bluetooth's baseband. By disabling the ARQ scheme in Bluetooth one can modulate the channel as a Packet Erasure Channel that will lose packet with a certain probability. By using a nested code structure, the message can then be securely sent by using a higher rate than what the eavesdropper can recover due to the amount of errors the received signal will have. The performance of the concept is evaluated with the secrecy throughput, secrecy outage and the leakage.

In this paper, we propose a new secret sharing scheme for secure multi-party computation. We present a general framework that allows us to construct efficient secret sharing schemes from channel coding techniques for the wiretap channel. The resulting schemes can be employed to securely calculate linear functions of data that are distributed in a network without leaking any information on the data except the desired result. For the examples considered in this paper, our schemes minimize the communication overhead while keeping the data perfectly secure. Compared to conventional schemes, for which the communication overhead grows quadratically in the number of clients in the considered scenarios, the communication overhead for our approach grows only linearly with the number of clients. This property is maintained even if our secret sharing scheme is set up to introduce redundancy in order to compensate for losses of secret shares. While we only consider the case of passive eavesdroppers and implementations based on nested Reed-Solomon codes in this paper, the proposed framework can also be applied in other cases (e.g., when clients tamper with the data) by taking into account the effects of attacks in the design of the underlying wiretap code. QC 20150521

In this paper we consider the design of semi-static inter-cell interference coordination schemes for LTE networks. In this approach, base stations coordinate the power settings per resource block over long time spans such as seconds. In order to optimize the power settings, one needs to employ models which predict the rate of terminals over the next coordination period under the usage of a given power setting. However, these models are typically quite simple and neglect the impact from fading as well as from dynamic resource allocation performed at the base stations on a millisecond basis. Ignoring such properties of OFDMA networks leads therefore to suboptimal transmit power settings. In this paper, we study the impact from a precise rate prediction model that accurately accounts for fading and dynamic resource allocation. On the down-side, this more precise model leads to a much more involved optimization problem to be solved once per coordination period. We propose two different heuristic methods to deal with this problem. Especially the usage of genetic algorithm results to be promising to counteract the complexity increase. We then study the overall system performance and find precise rate prediction models to be essential for semi-static interference coordination as they provide significant performance improvements in comparison to approaches with simpler models. QC 20130830

Series data, sequences of measured values, are ubiquitous. Whenever observations are made along a path in space or time, a data sequence results. To comprehend nature and shape it to our will, or to make informed decisions based on what we know, we need methods to make sense of such data. Of particular interest are probabilistic descriptions, which enable us to represent uncertainty and random variation inherent to the world around us. This thesis presents and expands upon some tools for creating probabilistic models of sequences, with an eye towards applications involving speech and language. Modelling speech and language is not only of use for creating listening, reading, talking, and writing machines---for instance allowing human-friendly interfaces to future computational intelligences and smart devices of today---but probabilistic models may also ultimately tell us something about ourselves and the world we occupy. The central theme of the thesis is the creation of new or improved models more appropriate for our intended applications, by weakening limiting and questionable assumptions made by standard modelling techniques. One contribution of this thesis examines causal-state splitting reconstruction (CSSR), an algorithm for learning discrete-valued sequence models whose states are minimal sufficient statistics for prediction. Unlike many traditional techniques, CSSR does not require the number of process states to be specified a priori, but builds a pattern vocabulary from data alone, making it applicable for language acquisition and the identification of stochastic grammars. A paper in the thesis shows that CSSR handles noise and errors expected in natural data poorly, but that the learner can be extended in a simple manner to yield more robust and stable results also in the presence of corruptions. Even when the complexities of language are put aside, challenges remain. The seemingly simple task of accurately describing human speech signals, so that natural synthetic speech can be generated, has proved difficult, as humans are highly attuned to what speech should sound like. Two papers in the thesis therefore study nonparametric techniques suitable for improved acoustic modelling of speech for synthesis applications. Each of the two papers targets a known-incorrect assumption of established methods, based on the hypothesis that nonparametric techniques can better represent and recreate essential characteristics of natural speech. In the first paper of the pair, Gaussian process dynamical models (GPDMs), nonlinear, continuous state-space dynamical models based on Gaussian processes, are shown to better replicate voiced speech, without traditional dynamical features or assumptions that cepstral parameters follow linear autoregressive processes. Additional dimensions of the state-space are able to represent other salient signal aspects such as prosodic variation. The second paper, meanwhile, introduces KDE-HMMs, asymptotically-consistent Markov models for continuous-valued data based on kernel density estimation, that additionally have been extended with a fixed-cardinality discrete hidden state. This construction is shown to provide improved probabilistic descriptions of nonlinear time series, compared to reference models from different paradigms. The hidden state can be used to control process output, making KDE-HMMs compelling as a probabilistic alternative to hybrid speech-synthesis approaches. A final paper of the thesis discusses how models can be improved even when one is restricted to a fundamentally imperfect model class. Minimum entropy rate simplification (MERS), an information-theoretic scheme for postprocessing models for generative applications involving both speech and text, is introduced. MERS reduces the entropy rate of a model while remaining as close as possible to the starting model. This is shown to produce simplified models that concentrate on the most common and characteristic behaviours, and provides a continuum of simplifications between the original model and zero-entropy, completely predictable output. As the tails of fitted distributions may be inflated by noise or empirical variability that a model has failed to capture, MERS's ability to concentrate on high-probability output is also demonstrated to be useful for denoising models trained on disturbed data. QC 20131128 ACORNS: Acquisition of Communication and Recognition Skills LISTA – The Listening Talker

This paper considers the problem of source coding for computing functions of correlated i.i.d. random sources. The approach of combining standard and linear random coding for this problem was first introduced by Ahlswede and Han, in the special case of computing the modulo-two sum. In this paper, making use of an adapted version of that method, we generalize their result to more sophisticated scenarios, where the functions to be computed are polynomial functions. Since all discrete functions are fundamentally restrictions of polynomial functions, our results are universally applied. QC 20130109

International audience; We consider a lossless multi-terminal source coding problem with one transmitter, two receivers and side information. The achievable rate region of the problem is not well understood. In this paper, we characterise the rate region when the side information at one receiver is conditionally less noisy than the side information at the other, given this other receiver’s desired source. The conditionally less noisy definition includes degraded side information and a common message as special cases, and it is motivated by the concept of less noisy broadcast channels. The key contribution of the paper is a new converse theorem employing a telescoping identity and the Csisza ́r sum identity.

This thesis studies cooperative sensing and transmission in the context ofwireless sensor networks (WSNs). We especially focus on two means of cooperative sensing and transmission, namely, distributed source coding and relaying. We consider systems where the usefulness of the measured data is dependent on how old the data is and we therefore need low-delay transmission schemes. At first sight, the low-delay criterion may seem to be of little relevance, but it is this aspect in particular that distinguishes this thesis from many of the existing communication theoretic results, which often are asymptotic in the block lengths. The thesis is composed of an introductory part, discussing the fundamentals of communication theory and how these are related to the requirements of WSNs, followed by a part where the results of the thesis are reported in Papers A-H. Papers A-D study different scenarios for distributed source-channel coding. In Paper A, we consider transmission of correlated continuous sources and propose an iterative algorithm for designing simple and energy-efficient sensor nodes. In particular the cases of the binary symmetric channel as well as the additive white Gaussian noise channel are studied. In Paper B, the work is extended to channels with interference and it is shown that also in this case there can be significant power savings by performing a joint optimization of the system.Papers C and D use a more structured approach and propose side-information-aware source-channel coding strategies using lattices and sinusoids. In Paper E, we apply the methods we have used in joint source-channel coding to the famous Witsenhausen counterexample. By using a relatively simple iterative algorithm, we are able to demonstrate the best numerical performance known to date. For the case of systems with relays, we study the transmission of a continuous Gaussian source and the transmission of an uniformly distributed discrete source. In both situations, we propose algorithms to design low-delay source-channel and relay mappings. By studying the structure of the optimized source-channel and relay mappings, we provide useful insights into how the optimized systems work. These results are reported in Papers F and G. In Paper H, we finally consider sum-MSE minimization for the Gaussian multiple-input, multiple-output broadcast channel. By using recently discovered properties of this problem, we derive a closed-form expression for the optimal power allocation in the two-user scenario and propose a conceptually simple and efficient algorithm that handles an arbitrary number of users. Throughout the thesis we show that there are significant gains if the parts of the system are jointly optimized for the source and channel statistics. All methods that are considered in this thesis yield very low coding and decoding delays. In general, nonlinear mappings outperform linear mappings for problems where there is side-information available. Another contribution of this thesis is visualization of numerically optimized systems that can be used as inspiration when structured low-delay systems are designed. The author changed name from Johannes Karlsson to Johannes Kron in January 2011. QC 20110512

Earlier paper have demonstrated that the achievable throughput of OFDM systems can benefit significantly from individual modulation/transmit power selection on a per sub-carrier basis according to the actual gain of individual sub-carriers (so called dynamic OFDM scheme). Usage of such approach requires, however, providing support for additional functionality like: acquisition of the subcarrier gains, signaling of the used modulation types between the sender and receiver, etc. Therefore dynamic OFDM is actively pursued for future radio interfaces, rather then considered as extension of existing OFDM based standards. In this paper we present for the first time a proposal how the widely accepted IEEE 802.11a/g systems might be extended to support the dynamic OFDM in a singleuser (point-to-point) setting while assuring backward compatibility. We address these issues by a) presenting a set of protocol modifications; and b) a performance evaluation of the suggested extension (referred further on to as single-user 802.11 DYN mode) demonstrating the potential of performance improvement. QC 20140103

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

The proliferation of wireless communications has lead to a high interest to establish this technology in industrial settings. The main arguments in favor of wireless are reduced costs in deployment and maintenance, as well as increased flexibility. In contrast to home and office environments, industrial settings include mission-critical machine-to-machine applications, demanding stringent requirements for reliability and latency in the area of 1-10-9 PDR and 1ms, respectively. One way to achieve both is cooperative Automatic Repeat reQuest (ARQ), which leverages spatial diversity. This paper presents a wireless multi-user Time Division Multiple Access system with cooperative ARQ for mission-critical communication. We evaluate two design options analytically, using an outage-capacity model, to investigate whether the relaying of messages should be performed centrally at a multi-antenna AP with perfect Channel State Information (CSI) or decentrally at simultaneously transmitting stations with average CSI. Results indicate that both options are able to achieve the targeted communication guarantees when a certain degree of diversity is implemented, showing a stable system performance even with an increasing number of stations. QC 20160512

In this paper, the blocklength-limited performance of a relaying system is studied, where channels are assumed to experience quasi-static Rayleigh fading while at the same time only the average channel state information (CSI) is available at the source. Both the physical-layer performance (blocklength-limited throughput) and the link-layer performance (effective capacity) of the relaying system are investigated. We propose a simple system operation by introducing a factor based on which we weight the average CSI and let the source determine the coding rate accordingly. In particular, we prove that both the blocklength-limited throughput and the effective capacity are quasi-concave in the weight factor. Through numerical investigations, we show the appropriateness of our theoretical model. In addition, we observe that relaying is more efficient than direct transmission. Moreover, this performance advantage of relaying under the average CSI scenario is more significant than under the perfect CSI scenario. Finally, the speed of convergence (between the blocklength-limited performance and the performance in the Shannon capacity regime) in relaying system is faster in comparison to the direct transmission under both the average CSI scenario and the perfect CSI scenario., Comment: 12 figures, submitted to IEEE Transactions on Wireless Communications

Token-passing wireless network protocols (TP-WNP) (e.g., EchoRing), designed for hard real-time systems, typically need to provide ultra-low-latency coupled with ultra-high-reliability guarantees. In this paper, we initiate a study to investigate the feasibility of distributed relay beamforming (DRBF) in a TP-WNP with the aim to enhance its reliability (and latency) performance even further. Specifically, we consider employing $N$ i) amplify-and- forward (AF), ii) decode-and-forward (DF) relays in a wireless network running a TP-WNP. The relays operate in FDD mode, and do distributed transmit beamforming to realize low-latency, highly- reliable communication between each of the $M$ source- destination pairs in the TP-WNP (a.k.a TDMA) system. The enablers/pre-requisites for the proposed DRBF-TDMA system are frequency, phase and timing synchronization among the relay nodes. To this end, we propose a novel distributed method for frequency synchronization among the AF/DF relay nodes operating in FDD mode. Furthermore, for oscillators with drift, we derive a rule of thumb which provides us the maximum relaying delay $T_{delay}$ up to which the proposed frequency synchronization method is effective. For phase and timing synchronization, we employ standard techniques from the literature. Our simulation results verify the analytical results, i.e., by means of proposed DRBF using $N$ AF (DF) relays, upto a factor of $N$ ($N^2$) gains in received SNR can be achieved, at each of the $M$ destination nodes in the proposed system. QC 20150715

In this paper, we evaluate the performance of 802.11p-based vehicular communications in the presence of radio frequency (RF) jamming attacks. Specifically, we characterize the transmission success rate of a car-to-car link subject to constant, periodic, and reactive RF jamming. First, we conduct extensive measurements in an anechoic chamber, where we study the benefits of built-in techniques for interference mitigation. In addition, we identify that the periodic transmission of preamble-like jamming signals can hinder successful communication despite being up to five orders of magnitude weaker than the signal of interest. We further provide the rationale behind this remarkably high jamming effectiveness. Additionally, we quantify the impact of reaction delay and interference signal length on the effectiveness of the reactive jammer. Then, by means of outdoor measurements, we evaluate the suitability of using the indoor measurements as a model to characterize the performance of car-to-car communications in the presence of RF jamming. Finally, we conduct outdoor measurements emulating a vehicular platoon and study the threats that RF jamming poses to this vehicular ad hoc network (VANET) application. We observe that constant, periodic, but also reactive jamming can hinder communication over large propagation areas, which would threaten road safety.

Data transmission over multiple-hop networks is impaired by random deleterious events, and characterizing the probability of error for the end-to-end transmission is challenging as the size of networks grows. Adams et al. showed that, when re-encoding at intermediate nodes is enabled, coded transmission over tandem/parallel links can be reduced to a single equivalent link with a specified probability function. Although iterative application of the tandem/parallel reduction techniques in alternation can simplify the task, they are generally not sufficient to reduce an arbitrary network to a single link. In this paper, we propose upper- and lower- bounding processes to bound the end-to-end probability distribution of a network by combining the parallel/tandem link reduction with the structure of flows over the network. We evaluate the performance of the proposed bounding methods at the 99% success rate of end-to-end data transmission over randomly generated acyclic networks. The numerical results demonstrate that our bounding approaches enable us to characterize a network by a single probability function to a very good precision. QC 20160216, QC 20160421

Resource assignment problems occur in a vast variety of applications, from scheduling problems over image recognition to communication networks. Often these problems can be modeled by a maximum weight matching problem in (bipartite) graphs or generalizations thereof, and efficient and practical algorithms are known for these problems. Although in some of the applications an assignment of the resources may be needed only once, in many of these applications, the assignment has to be computed more often for different scenarios. In that case it is often essential that the assignments can be computed very fast. Moreover, implementing different assignments in different scenarios may come with a certain cost for the reconfiguration of the system. In this paper, we consider the problem of determining optimal assignments sequentially over a given time horizon, where consecutive assignments are coupled by constraints that control the cost of reconfiguration. We develop fast approximation and online algorithms for this problem with provable approximation guarantees and competitive ratios. Moreover, we present an extensive computational study about the applicability of our model and our algorithms in the context of orthogonal frequency division multiple access (OFDMA) wireless networks, finding a significant performance improvement for the total bandwidth of the system using our algorithms. For this application (the downlink of an OFDMA wireless cell) , the run time of matching algorithms is extremely important, having an acceptable range of a few milliseconds only. For the considered realistic instances, our algorithms perform extremely well: the solution quality is, on average, within a factor of 0.8–0.9 of optimal off-line solutions, and the running times are at most 5 ms per phase even in the worst case. Thus, our algorithms are well suited to be applied in the context of OFDMA systems. QC 20150531

We study a multinode network, where a multiantenna transmitter $\text{T}_\mathrm{x}$ communicates with its desired receiver $\text{R}_\mathrm{x}$ , whereas a cluster $\mathcal{P}\triangleq\{\text{P}_{\mathrm{x,n}}, \ n=1,\ldots,N\}$ of unintended nodes is disturbed by the $\text{T}_\mathrm{x}$ – $\text{R}_\mathrm{x} $ (TR) communication. To prevent severe performance degradation, we impose a constraint on the total interference that is inflicted at the nodes of $\mathcal{P} $ . The TR link contains a line-of-sight component, whereas the propagation environment for each $\text{T}_\mathrm{x}$ – $\text{P}_{\mathrm{x,n}}$ link is shadowed. The $\text{T}_\mathrm{x} $ node is preprocessing the information sequence by means of a precoding matrix that is optimized to achieve the ergodic capacity under a constraint on the maximum admissible ergodic interference power, arriving on $\mathcal{P} $ . In this paper, we show that the optimum precoding strategy involves the transmission of a single stream over the precoding direction, i.e., the eigenvector of the precoding matrix, which corresponds to beamforming along the instantaneous direction of the TR-link channel. The solution of the remaining power allocation problem yields the optimal precoding matrix. For this setup, we provide an efficient stochastic characterization of the network, which allows us to obtain an analytical expression for the TR-link ergodic capacity; this problem has been previously open, even for the case of a single-antenna node $\text{T}_\mathrm{x}$ and a single-element set $\mathcal{P} $ . We complement the analysis by deriving the TR-link signal-to-noise ratio and the average bit error rate, which are associated with our transmission scheme. Numerical results corroborate the theoretical analysis and reveal an interplay between the network parameters and their impact on the TR-link performance.

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

Spurred by recent industrial trends, such as factoryautomation or phase synchronization in the smart grid, thereis a significant interest for wireless industrial networks lately.In contrast to traditional applications, the focus is on carryingout communication at very short latencies together with highreliabilities. Meeting such extreme requirements with wirelessnetworks is challenging. A potential candidate for such a networkis a token-passing protocol, as it allows to bound latencies.However, it lacks mechanisms to cope with the dynamics ofwireless channels. In this paper, we present EchoRing, a novelwireless token-passing protocol. Cooperative communication andan improved fault tolerance allow this decentralized protocol tosupport industrial applications over wireless networks. Based onexperimental results, we demonstrate the suitability of EchoRingto support demands of industrial applications. EchoRing outper-forms other schemes by several orders of magnitude in terms ofreliability for latencies of and below10ms. QC 20150715

In this paper, we study the higher layer performance as well as the “throughput, delay, energy consumption” tradeoff problem for multirelay-assisted cooperative automatic repeat request (C-ARQ) protocols. We study a practical scenario where only the average channel state information (CSI) is available at the source and relays. We consider four multirelay C-ARQ protocols and derive closed-form expressions for the transmission delay distribution, the energy consumption, and the higher layer queuing performance. Furthermore, we analyze the quality of service (QoS)-constrained energy efficiency performances of the protocols. Our analysis is validated by simulations. In addition, we evaluate the system performance under these C-ARQ protocols and for different topologies. We conclude several guidelines for the design of efficient C-ARQ protocols. Finally, a simple extension of the studied C-ARQ protocols is proposed, which improves the QoS-constrained energy efficiency by 4%.

Jamming is a well-known reliability threat for mass-market wireless networks. With the rise of safety-critical applications this is likely to become a constraining issue in the future. Thus, the design of accurate jamming detection algorithms becomes important to react to ongoing jamming attacks. With respect to experimental work, jamming detection has been mainly studied for sensor networks. However, many safety-critical applications are also likely to run over 802.11-based networks where the proposed approaches do not carry over. In this paper we present a jamming detection approach for 802.11 networks. It uses metrics that are accessible through standard device drivers and performs detection via machine learning. While it allows for stand-alone operation, it also enables cooperative detection. We experimentally show that our approach achieves remarkably high detection rates in indoor and mobile outdoor scenarios even under challenging link conditions. QC 20150123

In this paper, the privacy problem of a parallel distributed detection system vulnerable to an eavesdropper is proposed and studied in the Neyman-Pearson formulation. The privacy leakage is evaluated by a metric related to the Neyman-Pearson criterion. We will show that it is sufficient to consider a deterministic likelihood-ratio test for the optimal detection strategy at the eavesdropped sensor. This fundamental insight helps to simplify the problem to find the optimal privacy-constrained distributed detection system design. The trade-off between the detection performance and privacy leakage is illustrated in a numerical example. QC 20150122. QC 20160314

With the considerable growth of wireless networks in recent years, the issue of network security has taken an important role in the design of communication devices and protocols. Indeed, due to the broadcast nature of these networks, communications can potentially be attacked by malicious parties, and therefore, the protection of transmitted data has become a main concern in today's communications. On the other hand the cooperation of nodes overhearing the transmission may potentially lead to a better performance. In this thesis we combine both fundamental concepts of cooperation and secrecy in wireless networks. In particular we investigate the cooperation between transmitters in a cognitive radio network where the secondary receiver is treated as a potential eavesdropper to the primary transmission. We study this novel model focusing on several fundamental aspects. First we derive achievable rate regions for different transmission schemes, such as cooperative jamming and relaying, with and without primary message knowledge at the secondary transmitter. For these schemes, we formulate and solve three relevant power allocation problems: the maximization of the achievable primary and secondary rates, and the minimization of the secondary transmitting power. We model the interaction between the transmitting users as a Stackelberg game corresponding to a more realistic power allocation problem. We solve the game and illustrate its impact on the achievable rates. Secondly we generalize our system model by introducing the multi-phase clean relaying (CR) scheme, which takes into account the message-learning constraint at the secondary transmitter, and we derive the achievable rate region for this scheme. We compare our CR scheme to other transmission strategies such as dirty paper coding, interference neutralization, and pure cooperative jamming. Thirdly we extend our model to the generalized scenario where multiple secondary transmitter-receiver pairs wish to access the spectrum. For this scenario, we define and study several types of games between the primary network and the secondary pairs, such as Stackelberg games, power control games, and auction games. We derive the equilibrium of each game considered, which allows us to predict the behavior of the users in the cognitive radio network with multiple secondary pairs. Moreover we consider the important concept of energy efficiency (EE) for the performance of the cognitive radio network and we derive the power allocation and power splitting maximizing the secondary transmitter's energy efficiency. An important EE Stackelberg game between the two transmitters is formulated, and the impact of the game theoretic interaction is analyzed. Finally we motivate and investigate information theoretic secrecy using key agreement techniques in wireless networks. In particular we derive achievable secret key rate regions for two different key agreement schemes in Gaussian channels using several transmission strategies such as power control and cooperative jamming. The interaction between transmitting users is analyzed from a game theoretic perspective using non-cooperative game theory. For every fundamental perspective considered for the analysis of the model studied in the thesis, our results are illustrated through numerical examples based on a geometrical setup, highlighting the impact of the node geometry on the achievable rates, the optimal strategies, the games' equilibria and the impact of the game theoretic interaction between transmitters on the system performance. QC 20141110

In this paper, the privacy problem of a tandem distributed detection system vulnerable to an eavesdropper is proposed and studied in the Bayesian formulation. The privacy risk is evaluated by the detection cost of the eavesdropper which is assumed to be informed and greedy. For the sensors whose operations are constrained to suppress the privacy risk, it is shown that the optimal detection strategies are likelihood-ratio tests. This fundamental insight allows for the optimization to reuse known algorithms extended to incorporate the privacy constraint. The trade-off between the detection performance and privacy risk is illustrated in an example. QC 20150123. QC 20160314

Parallelization is crucial for ecient execution of large-scale network simulation. Today's computing clusters commonly used for that purpose are built from a large amount of multiprocessor machines. The traditional approach to utilize all CPU cores in such a system is to partition the network and distribute the partitions to the cores. This, however, does not incorporate the presence of shared memory into the design, such that messages between partitions on the same computing node have to be serialized and synchronization becomes more complex. In this paper, we present an approach that combines the shared-memory parallelization scheme Horizon [9] with the standard approach to distributed simulation to leverage the strengths of today's computing clusters. To further reduce the synchronization overhead, we introduce a novel synchronization algorithm that takes domain knowledge into account to reduce the number of synchronization points. In a case study with a UMTS LTE model, we show that both contributions combined enable much higher scalability achieving almost linear speedup when simulating 1,536 LTE cells on 1,536 CPU cores. QC 20150123

In this paper, eavesdropping in parallel distributed sequential detections is considered. The privacy risk is evaluated by the minimal achievable Bayesian risk of a greedy and informed eavesdropper who is curious about the hypothesis realization. We propose a novel metric based on Bayesian risk to take the detection performance and privacy risk with different weights into account. We formulate and study the privacy-concerned parallel distributed Bayesian sequential detection problem under a finite time-horizon assumption. Solving this problem will lead to the optimal distributed sequential detection design which achieves the minimal privacy-concerned Bayesian risk. The study shows that it is not sufficient to consider a deterministic likelihood-ratio test for a remote decision maker at an active time index in the optimal privacy-concerned system design. However, properties of the optimal design indicate that the standard method can be extended to solve the proposed problem. QC 20160209

PHY layer authentication of a wireless sender has gained much interest recently. In this paper, we consider the famous Alice, Bob and Eve model and investigate (for the first time) the feasibility of using time-varying clock offsets for sender-node-authentication at Bob. Specifically, we exploit the fact (and de-facto problem) that clock offset between every node pair is unique; moreover, the two clock offsets between any two node pairs drift independently and randomly over time. Therefore, an explicit mechanism is needed to track the time-varying clock offsets. To this end, we model oscillator drift as brownian motion frequency and phase drift, and present a novel framework which is based on interplay between a hypothesis testing device and a bank of two Kaiman filters; one KF (KFh0) tracks Alice's clock while other KF (KFh1) tracks Eve's clock. Building on aforementioned framework, we then propose a novel sender-node-authentication method (so-called MHF method) by means of which Bob can automatically accept (reject) a received packet if it is sent by Alice (Eve). Finally, simulation results are presented which corroborate the efficiency of the proposed method. QC 20150223

Parallel and distributed simulation have been extensively researched for a long time. Nevertheless, many simulation models are still executed sequentially. We attribute this to the fact that many of those models are simply not capable of being executed in parallel since they violate particular constraints. In this paper, we analyze the INET model suite, which enables network simulation in OMNeT++ with regard to parallelizability. We uncovered several issues preventing parallel execution of INET models. We analyzed those issues and developed solutions allowing INET models to be run in parallel. A case study shows the feasibility of our approach. Though there are parts of the model suite that we didn’t investigate yet and the performance can still be improved, the results show parallelization speedup for most configurations. The source code of our implementation is available through our web site at code.comsys.rwth-aachen.de QC 20150123

Multiple relays assisted transmission is an effective way to enhance the reliability of wireless communication network. However multiple relays joining in the transmission costs more energy and may reduce the energy efficiency of the system. In this paper, we investigate the energy efficiency performance of multiple decode-and-forward relays system under a quality of service constraint. In particular, we study a feedback-limited scenario where only the knowledge of average channel gain is available at the source. First, we define a tradeoff factor and based on it design a tradeoff mechanism between energy efficiency and outage probability. Second, we prove that the energy efficiency of the system is a piecewise strictly monotonic function of our tradeoff factor and has only one extreme value which is the global maximum. Third, by means of simulation we show that the numerical results perfectly match our theoretical analysis. In addition, we show that energy efficiency can achieve the extreme value under the loose QoS constraint. QC 20140102

A subspace method for channel estimation is pro- posed for asymmetric antenna array systems. The so-called pilot contamination problem reported in [1] is found to be due to the linearity of channel estimation in [2]. We show that it does not occur in cellular systems with power control and power-controlled handoff when the nonlinear channel estimation method proposed in this paper is used. Power-control hand-off is needed to guarantee separability between signal and interference subspaces. We derive the transmission conditions for subspace separability based on free probability and perturbation theory. QC 20130709

Among different Nonnegative Matrix Factorization (NMF) approaches, probabilistic NMFs are particularly valuable when dealing with stochastic signals, like speech. In the current literature, little attention has been paid to develop NMF methods that take advantage of the temporal dependencies of data. In this paper, we develop a hidden Markov model (HMM) with a gamma distribution as output density function. Then, we reformulate the gamma HMM as a probabilistic NMF. This shows the analogy of the proposed HMM and NMF, and will lead to a new probabilistic NMF approach in which the temporal dependencies are also captured inherently by the model. Furthermore, we propose an expectation maximization (EM) algorithm to estimate all the model parameters. Compared to the available probabilistic NMFs that model data with Poisson, multinomial, or exponential distributions, the proposed NMF is more suitable to be used with continuous-valued data. Our experiments using speech signals shows that the proposed approach leads to a better compromise between sparsity, goodness of fit, and temporal modeling compared to state-of-the-art. QC 20130703

A good utilization of a wireless network while still ensuring QoS-constraints for all users is a hard challenge for service providers. Especially for admission control or handover decisions it is important to have a good approximation of the possible rate a user can achieve with respect to QoS parameters like delay or outage probabilities. Since fourth generation cellular networks utilize a frequency reuse of one, especially terminals at the cell edge suffer from inter-cell interference. In order to provide them with rates that guarantee specific QoS parameters without reserving too much resources, a good prediction of their possible rates is needed. Unfortunately, the prediction of the rates in an interference-limited cell is a complex and hard problem. Hence, it is a common way to simplify calculations by treating interference as additional noise in the used system models. In this paper we derive closed-form solutions for the delay distribution of interference-limited cells with respect to an OFDMA and a round robin scheduling approach. These distributions can be used to predict the possible rates of users given their average received transmitter gains, interferer gains and their QoS-constraints in a very accurate way. We validate our derivations and show that a simplification by treating interference as noise leads to an underestimation of rates which lowers the cell throughput. Furthermore, we show that due to the resulting equations, there is no way to derive the rates for interference limited cells in a linear way from easier solutions given by noise-limited cells. QC 20131213

Emerging machine-to-machine communication scenarios are envisioned to deal with more stringent quality-of-service demands. This relates mainly to outage and latency requirements, which are for example for safety-critical messages quite different than for traditional applications. On the other hand, it is widely accepted that machine-to-machine communication systems need to be energy-efficient because of the widespread use of battery-powered devices, but also due to their huge deployment numbers. In this paper, we address these issues with respect to multi-hop transmissions. Specifically, we deal with minimizing the consumed energy of transmitting a packet with end-to-end outage and latency requirements. We account for the cases in which the system can utilize solely average channel state information, or in addition obtain and profit from instantaneous channel state information. The developed solution is based on convex optimization. It is shown numerically that despite accounting for the energy consumption of acquiring instantaneous channel state information, especially as the outage and latency requirements become tough, it is by up to 100 times more energy efficient to convey a packet with instantaneous than with average channel state information. QC 20131209

Rate adaptation in vehicular networks is known to be more challenging than in WLANs due to the high mobility of stations. Nevertheless, vehicular networks are subject to certain recurring patterns particularly if stations communicate to roadside units. This has lead to the proposal of learning-based rate adaptation schemes which are trained for a certain propagation environment. In general, these schemes outperform other approaches at the price of being specific for a particular environment. In this paper we present RFRA, a novel rate adaptation scheme for vehicular networks. It is based on the machine-learning algorithm Random Forests which is known to be superior to most other learning approaches. Firstly, we show that RFRA outperforms other learning-based methods significantly. We also study the question how sensitive RFRA is to changes of the learned environment, especially with respect to the propagation characteristics. We show that, although this reduces the gain of our scheme, RFRA still provides a much higher performance than state-of-the-art rate adaptation schemes. QC 20131030

This paper considers joint transceiver design and power control for K-user multiple-input multiple-output (MIMO) interference networks. Each source intends to communicate with its corresponding destination at a fixed data rate. Only local channel side information (i.e. knowledge related to the channels directly connected to a terminal) is available at each terminal. We propose iterative algorithms to perform power control to guarantee successful communication while designing transmitter beamforming matrices and receiver filtering matrices according to the interference alignment concept. The proposed algorithms can exhibit a substantial performance improvement compared to the conventional orthogonal transmission schemes. QC 20130424

Noisy network coding (NNC) has been shown to outperform standard compress-and-forward (CF) in networks with multiple relays and/or multiple destinations. Recently, short-message noisy network coding (SNNC) has been proved to achieve the same rate region as NNC for independent sources but with significantly reduced encoding delay and decoding complexity. In this paper, we show that when partial cooperation between source nodes is possible, by performing rate-splitting, message exchange, and superposition coding with proper power allocation at the source nodes, SNNC can achieve a strictly larger rate region than NNC. The gain comes from coherent combining at all the receiving nodes. QC 20121218

This paper explores the trade-off between cooperation and secrecy in cognitive radio networks. We consider a scenario consisting of a primary and a secondary system. In the simplest case, each system is represented by a pair of transmitter and receiver. We assume a secrecy constraint on the transmission in the sense that the message of the primary transmitter has to be concealed from the secondary receiver. Both situations where the secondary transmitter is aware and unaware of the primary message are investigated and compared. In the first case, the secondary transmitter helps by allocating power for jamming, which increases the secrecy of the first message. In the latter case, it can also act as a relay for the primary message, thus improving the reliability of the primary transmission. Furthermore, we extend our results to the scenario where the secondary system comprises multiple receivers. For each case we present achievable rate regions. We then provide numerical illustrations for these rate regions. Our main result is that, in spite of the secrecy constraint, cooperation is beneficial in terms of the achievable rates. In particular, the secondary system can achieve a significant rate without decreasing the primary rate below the benchmark rate achievable without the help of the secondary transmitter. Finally, we investigate the influence of the distances between users on the system's performance. Refereed/Peer-reviewed

This paper explores the trade-off between cooperation and secrecy in cognitive radio networks. We consider a scenario consisting of a primary and a secondary system. In the simplest case, each system is represented by a pair of transmitter and receiver. We assume a secrecy constraint on the transmission in the sense that the message of the primary transmitter has to be concealed from the secondary user. Both situations where the secondary transmitter is aware and unaware of the primary message are investigated and compared. Furthermore, we extend our results to the scenario where the secondary system comprises multiple users. For each case we sketch the derivation of the rates that are achievable from an information theoretic perspective. We then investigate the findings by numerical simulations. Our main result is that, in spite of the secrecy constraint, cooperation is beneficial in terms of the achievable rates. The secondary transmitter has the two contradicting tasks of helping the primary system and transmitting its own message. Our results show that both tasks can be accomplished simultaneously, improving both systems' performance. In particular, the secondary system can achieve a significant rate without decreasing the primary rate below the benchmark rate achievable without the help of the secondary transmitter. In the case of multiple secondary users, the rate region reduces, which results in a lower individual rate. However, the linear increase in sumrate counterbalances this effect. QC 20121122

In this paper, we endeavor for predicting the performance of quantized compressive sensing under the use of sparse reconstruction estimators. We assume that a high rate vector quantizer is used to encode the noisy compressive sensing measurement vector. Exploiting a block sparse source model, we use Gaussian mixture density for modeling the distribution of the source. This allows us to formulate an optimal rate allocation problem for the vector quantizer. Considering noisy CS quantized measurements, we analyze upper- and lower-bounds on reconstruction error performance guarantee of two estimators - convex relaxation based basis pursuit de-noising estimator and an oracle-assisted least-squares estimator., Comment: 5 pages, 1 figure, Published in 2012 International Symposium on Information Theory and its Applications

Smart phones and tablet computers have greatly boosted the demand for services via wireless access points, keeping constant pressure on the network providers to deliver vast amounts of data over the wireless infrastructure. To enlarge coverage and enhance throughput, relaying has been adopted in the new generation of wireless communication systems, such as in the Long-Term Evolution Advanced standard, and will continue to play an important role in the next generation wireless infrastructure. Depending on functionality, relaying can be characterizing into three main categories: amplify-and-forward (AF), compression-and-forward (CF), and decode-and-forward (DF). In this thesis, we investigate different cooperative strategies in wireless networks when relaying is in use. We first investigate the capacity outer and inner bounds for a wireless multicast relay network where two sources, connected by error-free backhaul, multicast to two destinations with the help of a full-duplex relay node. For high-rate backhaul scenarios, we find the exact cut-set bound of the capacity region by extending the proof of the converse for the Gaussian relay channel. For low-rate backhaul scenarios, we present two genie-aided outer bounds by extending the previous proof and introducing two lemmas on conditional (co-)variance. Our inner bounds are derived from various cooperative strategies by combining DF/CF/AF relaying with network coding schemes. We also extend the noisy network coding scheme and the short-message noisy network coding approach to correlated sources. For low-rate backhaul, we propose a new coding scheme, partial-decode-and-forward based linear network coding. We derive the achievable rate regions for these schemes and measure the performance in term of achievable rates over Gaussian channels. By numerical investigation we observe significant gains over benchmark schemes and demonstrate that the gap between upper and lower bounds is in general not large. We also show that for high-rate backhaul, the cut-set bound can be achieved when the signal-to-noise ratios lie in the sphere defined by the source-relay and relay-destination channel gains. For wireless networks with independent noise, we propose a simple framework to get capacity outer and inner bounds based on the ``one-shot'' bounding models. We first extend the models for two-user broadcast channels to many-user scenarios and then establish the gap between upper and lower bounding models. For networks with coupled links, we propose a channel decoupling method which can decompose the network into overlapping multiple-access channels and broadcast channels. We then apply the one-shot models and create an upper bounding network with only bit-pipe connections. When developing the lower bounding network, we propose a two-step update of these models for each coupled broadcast and multiple-access channels. We demonstrate by some examples that the resulting upper bound is in general very good and the gap between the upper and lower bounds is usually not large. For relay-aided downlink scenarios, we propose a cooperation scheme by cancelling interference at the transmitter. It is indeed a symbol-by-symbol approach to one-dimension dirty paper coding (DPC). For finite-alphabet signaling and interference, we derive the optimal (in terms of maximum mutual information) modulator under a given power constraint. A sub-optimal modulator is also proposed by formulating an optimization problem that maximizes the minimum distance of the signal constellation, and this non-convex optimization problem is approximately solved by semi-definite relaxation. Bit-level simulation shows that the optimal and sub-optimal modulators can achieve significant gains over the Tomlinson-Harashima precoder (THP) benchmark and over non-DPC reference schemes, especially when the power of the interference is larger than the power of the noise. QC 20121015

In a Bayesian linear model, suppose observation y = Hx + n stems from independent inputs x and n which are Gaussian mixture (GM) distributed. With known matrix H, the minimum mean square error (MMSE) estimator for x , has analytical form. However, its performance measure, the MMSE itself, has no such closed form. Because existing Bayesian MMSE bounds prove to have limited practical value under these settings, we instead seek analytical bounds for the MMSE, both upper and lower. This paper provides such bounds, and relates them to the signal-to-noise-ratio (SNR). QC 20120730

In this paper the problem of sum-rate maximization for secondary cognitive users (SU) in presence of a primary user (PU) transmission is considered. We assume that all of the users are equipped with multiple transmit/receive antennas and that the cognitive users must not induce more interference to the primary users than it is defined in the permissive policy and expressed in terms of a spectrum mask or interference temperature constraint. We employ game-theory to find the so-called correlated equilibrium that corresponds to the playing strategy definitions for all users. Actually, the Vickrey-Clarke-Groves theory has been applied and the regret-matching algorithm has been implemented to find the optimal playing strategy for both secondary users. New cost function has been proposed that allows for fast convergence of the developed solutions to the optimal or sub-optimal point. Extensive simulation results have been carried out to corroborate the correctness of the proposed approach. © 2012 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 20120605

We propose a new code design for compress-and-forward relaying over bandlimited relay-to-destination channels. The main contribution of this paper is a code design based on joint (source-channel) coding and modulation that uses the correlation between the observations at the relay and the destination as protection against channel errors. This allows for relay nodes with reduced complexity, shifting most of the processing requirements to the destination node. Moreover, by using scalar quantizers with an entropy constraint our system provides remarkable performance in channel conditions where neither amplify-and-forward nor compress-and-forward efficiently exploit the presence of a relay node. Simulation results confirm the benefits of our proposed system. © 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Qc 20111207

Considering a single-antenna M-user interferencechannel with symmetrically distributed channel gains, when thechannel state information (CSI) is globally available, applyingthe ergodic interference alignment scheme, each transmitterreceiverpair achieves a rate proportional to 1⁄2 of a single user’sinterference-free achievable rate. This is substantially higher thanthe achievable rate of the conventional orthogonal transmissionschemes such as TDMA. Since the rigid requirement on theCSI may be difficult to realize in practice, in this paper weinvestigate the performance of applying the ergodic interferencealignment scheme when the estimation of each channel gain ismade globally known through exploiting only a limited feedbacksignal from the associated receiver of that channel. Under a blockfading environment, we provide a lower bound on the achievableaverage throughput of the network. Our results imply that thebetter performance of interference alignment over TDMA maystill exist even without the assumption of perfect CSI. Also, thetrade off between allocating feedback rate of each receiver tothe desired channel or the interference channels at deferent SNRregion investigated. © 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. QC 20111026

Recently, it has been shown that the capacity of certain Gaussian networks can be approximated by the capacity of the corresponding network in the discrete superposition model (DSM) [1], [2]. The gap between the capacities is an additive constant only depending on the number of nodes in the network. Hence, the capacity in the DSM is a good approximation in the high SNR regime. Finding this capacity involves optimizing over a finite set of coding strategies. However, the problem space grows with both the number of nodes and with SNR, rendering the optimization infeasible. In this paper we find upper and lower bounds on the capacity in the DSM. We start with the point-to-point channel, and we extend our strategy to the multiple-access channel. We show that the gap between our bounds is at most an additive constant independent of the channel gains. Hence, combining our results with the results of [1], [2], we find closed form bounds on the Gaussian capacity to within an additive constant. QC 20111114

In this paper, we study the design of a causal anytime encoding and decoding scheme for transmission of real-time information over a binary symmetric channel. In particular, our scheme combines unequal-error protection (UEP) rateless codes with sequential belief propagation decoding. In order to minimize delay in decoding and reduce distortion, we formulate and solve a linear programming problem. Moreover, degree distributions of the UEP-rateless codes are optimized since the efficiency of rateless codes highly depends on the corresponding degree distribution. The performance of the proposed scheme is demonstrated by using numerical simulations, and will be compared with an existing work. QC 20111213

In a scenario where a cognitive radio unit wishes to transmit, it needs to know over which frequency bands it can operate. It can obtain thisknowledge by estimating the power spectral density from a Nyquist-rate sampled signal. For wide-band signals sampling at the Nyquistrate is a major challenge and may be unfeasible. In this paper we accurately detect spectrum holes in sub-Nyquist frequencies without assuming wide sense stationarity in the compressed sampled signal. A novel extension to further reduce the sub-Nyquist samples is thenpresented by introducing a memory based compressed sensing thatrelies on the spectrum to be slowly varying. © 2010 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 20110707