In this paper, we study an information-theoretic problem of designing a fair representation that attains bounded statistical (demographic) parity. More specifically, an agent uses some useful data$X$to solve a task$T$. Since both$X$and$T$are correlated with some sensitive attribute or secret$S$, the agent designs a representation$Y$that satisfies a bounded statistical parity and/or privacy leakage constraint, that is, such that$I(Y; S) \leq \epsilon$. Here, we relax the perfect demographic (statistical) parity and consider a bounded-parity constraint. In this work, we design the representation$Y$that maximizes the mutual information$I(Y; T)$about the task while satisfying a bounded compression (or encoding rate) constraint, that is, ensuring that$I(Y; X) \leq r$. Simultaneously,$Y$satisfies the bounded statistical parity constraint$I(Y; S) \leq \epsilon$. To design$Y$, we use extended versions of the Functional Representation Lemma and the Strong Functional Representation Lemma which are based on randomization techniques and study the tightness of the obtained bounds in special cases. The main idea to derive the lower bounds is to use randomization over useful data$X$or sensitive data$S$. Considering perfect demographic parity, i.e.,$\epsilon=0$, we improve the existing results (lower bounds) by using a tighter version of the Strong Functional Representation Lemma and propose new upper bounds. We then propose upper and lower bounds for the main problem and show that allowing non-zero leakage can improve the attained utility. Finally, we study the bounds and compare them in a numerical example. The problem studied in this paper can also be interpreted as one of code design with bounded leakage and bounded rate privacy considering the sensitive attribute as a secret.

This paper studies the truncation method from Alquier [1] to derive high-probability PAC-Bayes bounds for unbounded losses with heavy tails. Assuming that the p-th moment is bounded, the resulting bounds interpolate between a slow rate$1/\sqrt{n}$when$p=2$, and a fast rate$1/n$when$p\rightarrow\infty$and the loss is essentially bounded. Moreover, the paper derives a high-probability PAC-Bayes bound for losses with a bounded variance. This bound has an exponentially better dependence on the confidence parameter and the dependency measure than previous bounds in the literature. Finally, the paper extends all results to guarantees in expectation and single-draw PAC-Bayes. In order to so, it obtains analogues of the PAC-Bayes fast rate bound for bounded losses from [2] in these settings. The full version of the paper can be found in https://arxiv.org/abs/2403.16681.

In this paper, we present new high-probability PAC-Bayes bounds for different types of losses. Firstly, for losses with a bounded range, we recover a strengthened version of Catoni's bound that holds uniformly for all parameter values. This leads to new fast-rate and mixed-rate bounds that are interpretable and tighter than previous bounds in the literature. In particular, the fast-rate bound is equivalent to the Seeger--Langford bound. Secondly, for losses with more general tail behaviors, we introduce two new parameter-free bounds: a PAC-Bayes Chernoff analogue when the loss' cumulative generating function is bounded, and a bound when the loss' second moment is bounded. These two bounds are obtained using a new technique based on a discretization of the space of possible events for the "in probability" parameter optimization problem. This technique is both simpler and more general than previous approaches optimizing over a grid on the parameters' space. Finally, using a simple technique that is applicable to any existing bound, we extend all previous results to anytime-valid bounds.

To date, no “information-theoretic” frameworks for reasoning about generalization error have been shown to establish minimax rates for gradient descent in the setting of stochastic convex optimization. In this work, we consider the prospect of establishing such rates via several existing information-theoretic frameworks: input-output mutual information bounds, conditional mutual information bounds and variants, PAC-Bayes bounds, and recent conditional variants thereof. We prove that none of these bounds are able to establish minimax rates. We then consider a common tactic employed in studying gradient methods, whereby the final iterate is corrupted by Gaussian noise, producing a noisy “surrogate” algorithm. We prove that minimax rates cannot be established via the analysis of such surrogates. Our results suggest that new ideas are required to analyze gradient descent using information-theoretic techniques.

Power consumption is a major challenge for a massive deployment of wireless sensors in Internet of Things (IoT) networks. This article studies the use of analog joint source-channel coding (AJSCC) mappings in low-power sensing schemes. In particular, we propose a noveltriangularmapping geometry as a low-complexity dimension reduction mapping. The proposed triangular mapping is employed for analog compression of multiple sensor readings into one signal and, thus, limits the need for power-hungry analog-to-digital conversion and processing at the sensing nodes. A comprehensive performance analysis of the proposed triangular mapping in terms of the mean squared error (MSE) performance is provided analytically and verified numerically. The problem of mapping adaptation to different source distributions is also studied. Moreover, the proposed triangular mapping is adopted in an energy scheduling problem in which the sensing nodes schedule their use of the received powers at different time instants and adjust the mapping parameters accordingly with the goal of minimizing the sum distortion at the receiver. We present a fast low-complexity algorithm for optimal energy scheduling and verify its performance in comparison with commercial convex optimization solvers. It is shown that the proposed mapping provides a very good MSE performance compared to the AJSCC benchmarks despite having a much lower complexity circuit implementation.

Feature-based physical layer authentication (PLA) schemes, using position-specific channel characteristics as identifying features, can provide lightweight protection against impersonation attacks in overhead-limited applications like e.g., mission-critical and low-latency scenarios. However, with PLA-aware attack strategies, an attacker can maximize the probability of successfully impersonating the legitimate devices. In this paper, we provide worst-case detection performance bounds under such strategies for a distributed PLA scheme that is based on the channel-state information (CSI) observed at multiple distributed remote radio-heads. This distributed setup exploits the multiple-channel diversity for enhanced detection performance and mimics distributed antenna architectures considered for 4G and 5G radio access networks. We consider (i) a power manipulation attack, in which a single-antenna attacker adopts optimal transmit power and phase; and (ii) an optimal spatial position attack. Interestingly, our results show that the attacker can achieve close-to-optimal success probability with only statistical CSI, which significantly strengthens the relevance of our results for practical scenarios. Furthermore, our results show that, by distributing antennas to multiple radio-heads, the worst-case missed detection probability can be reduced by 4 orders of magnitude without increasing the total number of antennas, illustrating the superiority of distributed PLA over a co-located antenna setup.

This paper analyzes the worst-case detection performance of a feature-based physical layer authentication (PLA) scheme subject to optimal multiple-antenna impersonation attacks. The PLA scheme is based on the location-specific channel in the uplink towards a multiple-antenna receiver, and the attacker is using pre-coding with the objective of maximizing the missed detection probability. We solve the optimal attack strategy problem under perfect channel-state information (CSI) at the attacker, imperfect CSI at the attacker, and for a power constrained attacker. As a counter strategy, we propose to reserve a subset of silent receive antennas for reception only, in order to limit the CSI that an attacker can extract from overhearing downlink transmissions. We evaluate the performance under the attack- and counter-strategies, both analytically and for recorded real-world channel traces, and show that the worst-case performance is determined by the feature-energy outside the attacker’s channel range and the attack-power constraints. Results indicate that an unconstrained attacker with favorable conditions can achieve a success probability close to 1; however, under more realistic channel constraints, detection performance guarantees in the order of 10−6− 10−4can be obtained. Moreover, we find that performance can be improved by 1-2 orders of magnitude through the proposed counter strategy.

Physical layer authentication (PLA) has recently been discussed in the context of URLLC due to its low complexity and low overhead. Nevertheless, these schemes also introduce additional sources of error through missed detections and false alarms. The trade-offs of these characteristics are strongly dependent on the deployment scenario as well as the processing architecture. Thus, considering a feature-based PLA scheme utilizing channel-state information at multiple distributed radio-heads, we study these trade-offs analytically. We model and analyze different scenarios of centralized and decentralized decision-making and decoding, as well as the impacts of a single-antenna attacker launching a Sybil attack. Based on stochastic network calculus, we provide worst-case performance bounds on the system-level delay for the considered distributed scenarios under a Sybil attack. Results show that the arrival-rate capacity for a given latency deadline is increased for the distributed scenarios. For a clustered sensor deployment, we find that the distributed approach provides 23% higher capacity when compared to the centralized scenario.

5G and beyond 5G low power wireless networks make Internet of Things (IoT) and Cyber-Physical Systems (CPS) applications capable of serving massive amounts of devices and machines. Due to the broadcast nature of wireless networks, it is crucial to secure the communication between these devices and machines from spoofing and interception attacks. This paper is concerned with the security of carrier frequency offset (CFO) based continuous physical layer authentication. The interaction between an attacker and a defender is modeled as a dynamic discrete leader-follower game with imperfect information. In the considered model, a legitimate user (Alice) communicates with the defender/operator (Bob) and is authorized by her CFO continuously. The attacker (Eve), by listening/eavesdropping the communication between Alice and Bob, tries to learn the CFO characteristics of Alice and aims to inject malicious packets to Bob by impersonating Alice. First, by showing that the optimal attacker strategy is a threshold policy, an optimization problem of the attacker with exponentially growing action space is reduced to a tractable integer optimization problem with a single parameter, then the corresponding defender cost is derived. Extensive simulations illustrate the characteristics of optimal strategies/utilities of the players depending on the actions, and show that the defender’s optimal false positive rate causes attack success probabilities to be in the order of 0.99. The results show the importance of the parameters while finding the balance between system security and efficiency.

In this study, Nash and Stackelberg equilibria of single-stage and multi-stage quadratic signaling games between an encoder and a decoder are investigated. In the considered setup, the objective functions of the encoder and the decoder are misaligned, there is a noisy channel between the encoder and the decoder, the encoder has a soft power constraint, and the decoder has also noisy observation of the source to be estimated. We show that there exist only linear encoding and decoding strategies at the Stackelberg equilibrium, and derive the equilibrium strategies and costs. Regarding the Nash equilibrium, we explicitly characterize affine equilibria for the single-stage setup and show that the optimal encoder (resp. decoder) is affine for an affine decoder (resp. encoder) for the multi-stage setup. On the decoder side, between the information coming from the encoder and noisy observation of the source, our results describe what should be the combining ratio of these two channels. Regarding the encoder, we derive the conditions under which it is meaningful to transmit a message.

In this article, we propose a new variational approach to learn private and/or fair representations. This approach is based on the Lagrangians of a new formulation of the privacy and fairness optimization problems that we propose. In this formulation, we aim to generate representations of the data that keep a prescribed level of the relevant information that is not shared by the private or sensitive data, while minimizing the remaining information they keep. The proposed approach (i) exhibits the similarities of the privacy and fairness problems, (ii) allows us to control the trade-off between utility and privacy or fairness through the Lagrange multiplier parameter, and (iii) can be comfortably incorporated to common representation learning algorithms such as the VAE, the $\beta$-VAE, the VIB, or the nonlinear IB.

This work presents several expected generalization error bounds based on the Wasserstein distance. More specifically, it introduces full-dataset, single-letter, and random-subset bounds, and their analogous in the randomized subsample setting from Steinke and Zakynthinou [1]. Moreover, when the loss function is bounded and the geometry of the space is ignored by the choice of the metric in the Wasserstein distance, these bounds recover from below (and thus, are tighter than) current bounds based on the relative entropy. In particular, they generate new, non-vacuous bounds based on the relative entropy. Therefore, these results can be seen as a bridge between works that account for the geometry of the hypothesis space and those based on the relative entropy, which is agnostic to such geometry. Furthermore, it is shown how to produce various new bounds based on different information measures (e.g., the lautum information or several $f$-divergences) based on these bounds and how to derive similar bounds with respect to the backward channel using the presented proof techniques.

In this work, we unify several expected generalization error bounds based on random subsets using the framework developed by Hellström and Durisi. First, we recover the bounds based on the individual sample mutual information from Bu et al. and on a random subset of the dataset from Negrea et al. Then, we introduce their new, analogous bounds in the randomized subsample setting from Steinke and Zakynthinou, and we identify some limitations of the framework. Finally, we extend the bounds from Haghifam et al. for Langevin dynamics to stochastic gradient Langevin dynamics and we refine them for loss functions with potentially large gradient norms.

Recent studies proved that optimized multi-tone signals can significantly enhance the performance of wireless power transfer (WPT) systems. However, optimizing the power allocation for multi-tone signals in order to maximize the efficiency of WPT is a computationally complex task. In this paper, a novel low-complexity algorithm, the truncated maximum-ratio transmission (TMRT) algorithm, for allocating power to multitone signals for WPT is proposed. The algorithm exploits the fact that optimal algorithms tend to allocate power to tones having the strongest channels and no power to weaker channels, and therefore, performs maximum ratio transmission power allocation on the subset of the m strongest channels. In this way, the power allocation problem is reduced to finding the optimal m that maximizes the efficiency. Simulation results confirm that the proposed TMRT algorithm achieves a performance very close to the optimal power allocation, despite its very low complexity, and significantly outperforms other low-complexity solutions.

In this paper, we study optimization of multi-tone signals for wireless power transfer (WPT) systems. We investigate different non-linear energy harvesting models. Two of them are adopted to optimize the multi-tone signal according to the channel state information available at the transmitter. We show that a second-order polynomial curve-fitting model can be utilized to optimize the multi-tone signal for any RF energy harvester design. We consider both single-antenna and multi-antenna WPT systems. In-band co-existing communication links are also considered in this work by imposing a constraint on the received power at the nearby information receiver to prevent its RF front end from saturation. We emphasize the importance of imposing such constraint by explaining how inter-modulation products, due to saturation, can cause high interference at the information receiver in the case of multi-tone signals. The multi-tone optimization problem is formulated as a non-convex linearly constrained quadratic program. Two globally optimal solution approaches using mixed-integer linear programming and finite branch-and-bound techniques are proposed to solve the problem. The achieved improvement resulting from applying both solution methods to the multi-tone optimization problem is highlighted through simulations and comparisons with other solutions existing in the literature.

This paper proposes a new approach for physical layer authentication where transmissions are authenticated based on the single-input/multiple-output channel-states observed at multiple distributed antenna-arrays. The receiver operating characteristics (ROC) are derived in terms of closed form expressions for the false alarm and missed detection probability in order to evaluate the effectiveness compared to single-array authentication. To this end, we study the worst-case missed detection probability based on the optimal attacker position. Finally, we apply our previously developed queueing analytical tools, based on stochastic network calculus, in order to assess the delay performance impacts of the physical layer authentication scheme in a mission-critical communication scenario. Our results show that the distributed approach significantly outperforms single-array authentication in terms of worst-case missed detection probability and that this can help mitigating the delay performance impacts of authentication false alarms.

We study the detection and delay performance impacts of a feature-based physical layer authentication (PLA) protocol in mission-critical machine-type communication (MTC) networks. The PLA protocol uses generalized likelihood-ratio testing based on the line-of-sight (LOS), single-input multiple-output channel-state information in order to mitigate impersonation attempts from an adversary node. We study the detection performance, develop a queueing model that captures the delay impacts of erroneous decisions in the PLA (i.e., the false alarms and missed detections), and model three different adversary strategies: data injection, disassociation, and Sybil attacks. Our main contribution is the derivation of analytical delay performance bounds that allow us to quantify the delay introduced by PLA that potentially can degrade the performance in mission-critical MTC networks. For the delay analysis, we utilize tools from stochastic network calculus. Our results show that with a sufficient number of receive antennas (approximately 4-8) and sufficiently strong LOS components from legitimate devices, PLA is a viable option for securing mission-critical MTC systems, despite the low-latency requirements associated to corresponding use cases. Furthermore, we find that PLA can be very effective in detecting the considered attacks, and in particular, it can significantly reduce the delay impacts of disassociation and Sybil attacks.

Feature-based authentication schemes that verify wireless transmitter identities based on physical-layer features allow for fast and efficient authentication with minimal overhead. Hence, they are interesting to consider for safety-critical applications where low latency and high reliability is required. However, as erroneous authentication decisions will introduce delays, we propose to study the impact of feature-based schemes on the system-level performance. In this paper, we therefore study the queuing performance of a line-of-sight wireless link that employs a feature- based authentication scheme based on the complex channel gain. Using stochastic networks calculus, we provide bounds on the delay performance which are validated by numerical simulations. The results show that the delay and authentication performance is highly dependent on the SNR and Rice factor. However, under good channel conditions, a missed-detection rate of 10E-8 can be achieved without introducing excessive delays in the system.

A generalization of the ensemble of non-terminated systematic LDPC convolutional codes developed in our previous work is proposed that allows us to design codes with lower rates than the original structure. We show that over the BEC, the modified codes have improved asymptotic and finite-length behavior and we determine the operational anytime exponent. Having shown the advantages of lowering the rate of the code, we propose a feedback protocol that permits encoder and decoder to operate at a variable rate. The rate is set on-the-fly and depends on the decoding success of the decoder. We describe the construction of the variable rate code structure and demonstrate by simulations the superiority of the variable rate scheme as compared to a scheme using a fixed rate.

Here we propose an ensemble of non-terminated systematic LDPC convolutional codes with increasing memory, and show that, over the binary erasure channel (BEC), these codes achieve anytime reliability asymptotically when decoded with an expanding-window message-passing decoder. The corresponding anytime exponents are determined through protograph-based extrinsic information transfer charts. Fundamental complications arising when transmitting with finite block lengths are identified and a combinatorial performance analysis, when transmitting over a static BEC with a fixed number of erasures per codeword block, is developed. Based on the performance analysis, we explore the use of feedback for achieving anytime behavior with constraints on block length. To meet complexity constraints, with or without feedback, the code memory can be limited at the cost of an error floor emerging with a delay proportional to the memory constraint. Although the analysis is developed for a static BEC we show numerically that we can design efficient low-complexity finite-length codes with anytime properties even for the conventional BEC.

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

In this paper we present bilayer LDPC convolutional codes for half-duplex relay channels. Two types of codes, bilayer expurgated LDPC convolutional codes and bilayer lengthened LDPC convolutional codes, are proposed for decode-and-forward (DF) relaying. In the case of the binary erasure relay channel, we prove analytically that both code constructions achieve the capacities of the source-relay link and the source-destination link simultaneously, provided that the channel conditions are known when designing the codes. Meanwhile, both codes enable the highest transmission rate possible with DF relaying for a wide range of channel parameters. In addition, the regular degree distributions can easily be computed from the channel parameters, which significantly simplifies the code optimization. The code construction and performance analysis are extended to the general binary memoryless symmetric channel, where a capacity-achieving performance is conjectured. Numerical results are provided for both types of codes with finite node degrees over binary erasure channels and binary-input additive white Gaussian noise channels, which verify the aforementioned theoretical analysis.

We consider transmission over a wiretap channel where both the main channel and the wiretapper's channel are binary erasure channels (BEC). A code construction method is proposed using two edge-type low-density parity-check (LDPC) codes based on the coset encoding scheme. Using a single edge-type LDPC ensemble with a given threshold over the BEC, we give a construction for a two edge-type LDPC ensemble with the same threshold. If the given single edge-type LDPC ensemble has degree two variable nodes, our construction gives rise to degree one variable nodes in the code used over the main channel. This results in zero threshold over the main channel. In order to circumvent this problem, the degree distribution of the two edge-type LDPC ensemble is numerically optimized. We find that the resulting ensembles are able to perform close to the boundary of the rate-equivocation region of the wiretap channel. Further, a method to compute the ensemble average equivocation of two edge-type LDPC ensembles is provided by generalizing a recently published approach to measure the equivocation of single edge-type ensembles for transmission over the BEC in the point-to-point setting. From this analysis, we find that relatively simple constructions give very good secrecy performance.

We propose a LDPC Convolutional Code ensemble together with an expanding-window message-passing decoder that asymptotically have anytime properties when used for streaming transmission on the binary erasure channel. We show analytically that the decoding erasure probability of these codes decays exponentially over decoding delay and determine the corresponding anytime exponents.

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.

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.

We consider the symmetric discrete memoryless relay channel with orthogonal receiver components and show that polar codes are suitable for decode-and-forward and compress-and-forward relaying. In the first case we prove that polar codes are capacity achieving for the physically degraded relay channel; for stochastically degraded relay channels our construction provides an achievable rate. In the second case we construct sequences of polar codes that achieve the compress-and-forward rate by nesting polar codes for source compression into polar codes for channel coding. In both cases our constructions inherit most of the properties of polar codes. In particular, the encoding and decoding algorithms and the bound on the block error probability O(2-Nβ) which holds for any 0<;β<;1/2.

In this paper, we propose a new family of rate-compatible regular low-density parity-check (LDPC) convolutional codes. The construction is based on graph extension, i.e., the codes of lower rates are generated by successively extending the graph of the base code with the highest rate. Theoretically, the proposed rate-compatible family can cover all the rational rates from 0 to 1. In addition, the regularity of degree distributions simplifies the code optimization. We prove analytically that all the LDPC convolutional codes of different rates in the family are capable of achieving the capacity of the binary erasure channel (BEC). The analysis is extended to the general binary memoryless symmetric channel, for which a capacity-approaching performance can be achieved. Analytical thresholds and simulation results for finite check and variable node degrees are provided for both BECs and binary-input additive white Gaussian noise channels. The results confirm that the decoding thresholds of the rate-compatible codes approach the corresponding Shannon limits over both channels.

In this paper we present regular bilayer LDPC convolutional codes for half-duplex relay channels. For the binary erasure relay channel, we prove that the proposed code construction achieves the capacities for the source-relay link and the source-destination link provided that the channel conditions are known when designing the code. Meanwhile, this code enables the highest transmission rate with decode-and-forward relaying. In addition, its regular degree distributions can easily be computed from the channel parameters, which significantly simplifies the code optimization. Numerical results are provided for the codes with finite node degrees over binary erasure channels. We can observe that the gaps between the decoding thresholds and the Shannon limits are impressively small.

In this paper we construct a family of rate-compatible LDPC convolutional codes for Type-II HARQ systems. For each code family, the codes of lower rates are constructed by successively extending the graph of the high-rate base code. Theoretically, the proposed rate-compatible family includes all rates from 0 to 1. We prove analytically that all LDPC convolutional codes in the family are capacity achieving over the binary erasure channel (BEC). Thus, if applied to an idealized HARQ system over the BEC where the channel parameter stays constant within one complete information delivery, the throughput achieves the capacity of the channel. Moreover, the code construction is realized by regular degree distributions, which greatly simplifies the optimization.

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.

In this paper, we investigate the wiretap channel in the presence of a cooperative relay node. We analyze and compare the outage performance of three cooperatives schemes: cooperative jamming (CJ), decode-and-forward (DF), and amplify-and-forward (AF) for the Rayleigh slow fading channel. In particular, we derive a closed-form expression for the outage probability for the DF and CJ strategies, which allows an optimal strategy selection in terms of outage performance. We compare the three cooperative schemes through numerical simulations.

We propose a new compress-and-forward implementation for the relay channel based on joint source-channel coding techniques. The relay performs scalar quantization of its observation in combination with a redundant index mapping. Our system utilizes the correlation between the quantized signal and the direct-link observation of the transmitted symbols as redundancy for error protection on the relay-to-destination link. In order to fully exploit this correlation the destination requires iterative decoding to recover the quantized observation sent by the relay. Once regenerated, this quantized signal is optimally combined with the direct-link observation to decode the message conveyed by the source. By quantizing the observed signal itself rather than a measure on the reliability of the information bits (e.g. a posteriori probabilities from a decoder), and by using digital communication methods on the relay- to-destination link our system yields superior performance to that of amplify-and-forward, decode- and-forward and previous implementations of compress-and-forward based on soft decoding.

We consider a source coding problem where the encoder can take actions that influence the availability and/or quality of the side information which is available partially and noncausally at the encoder and the decoder. We then characterize the associated achievable tradeoffs between rate, distortion, and cost. In addition, we state and discuss a capacity result for the channel coding dual problem where the formula duality of special cases is recognized.

We derive detection metrics for soft-output diversity combining for the case of imperfect channel state information at the receiver. We treat in particular the case when the noise variance at the receiver is unknown. We contrast conventional training-based methods to a detector based on the generalized likelihood-ratio (GLR) test paradigm. We study the performance of the detectors via EXIT chart analysis and via simulations of LDPC coded transmission over a fast Rayleigh fading channel. The results show that the GLR receivers can significantly outperform the conventional detectors.

In this paper we propose a new adaptive coding scheme for distributed channel coding for the three-node relay channel. In order to make it feasible for application in wireless sensor networks, the distributed code is built from standard components like Turbo and convolutional codes, and adaptation at the relay is obtained by puncturing the input and output of the employed channel code. The proposed code structure includes distributed Turbo codes and distributed serially concatenated codes as special cases. As the results of our optimization show, significant improvements in terms of rate and coverage are obtained. Compared to theoretical limits a decent performance is achieved considering that the focus is on feasibility.

We present a novel variable-length code (VLC) construction which exhibits an inherent error correcting capability due to the exclusive presence of codewords with even Hamming weight. Besides error robustness, the proposed code construction features a similar codeword length distribution as Golomb-Rice codes, and therefore, in particular for sources with exponentially distributed symbols, has good source compression properties at the same time. We show that in a source channel coding framework with outer source encoding, inner channel encoding with a recursive convolutional code, and iterative decoding the proposed VLC construction can lead to significant performance improvements compared to fixed-length source encoding with optimized mappings. In particular, simulation results for the AWGN channel verify that for Gauss-Markov sources a performance close to the theoretical limit can be achieved.

We address the optimization of joint source-channel coding schemes for iterative source-channel decoding of first- order Markov sources. Compared to the traditional design, we propose two novelties: (1) source encoders, providing code words with a minimum Hamming distance dminges2, realized by linear block codes, and (2) irregular channel encoders which are optimized for both the source characteristics and the conditions on the channel. Inner code rates RC> 1 may be chosen in order to compensate for the additional source redundancy if required. Design examples for the AWGN channel and an overall code rate R=0.66 show that the proposed system is able to establish reliable communication within 0.3 dB of the capacity limit for an interleaver length of approximatively 200000 bits.

In this paper, we present a novel packetized bit-level decoding algorithm for variable-length encoded Markov sources, which calculates reliability information for the decoded bits in the form of a posteriori probabilities (APPs). An interesting feature of the proposed approach is that symbol-based source statistics in the form of the transition probabilities of the Markov source are exploited as a priori information on a bit-level trellis. This method is especially well-suited for long input blocks, since in contrast to other symbol-based APP decoding approaches, the number of trellis states does not depend on the packet length. When additionally the variable-length encoded source data is protected by channel codes, an iterative source-channel decoding scheme can be obtained in the same way as for serially concatenated codes. Furthermore, based on an analysis of the iterative decoder via extrinsic information transfer charts, it can be shown that by using reversible variable-length codes with a free distance of two, in combination with rate-1 channel codes and residual source redundancy, a reliable transmission is possible even for highly corrupted channels. This justifies a new source-channel encoding technique where explicit redundancy for error protection is only added in the source encoder.

We present a novel symbol-based soft-input a posteriori probability (APP) decoder for packetized variable-length encoded source indexes transmitted over wireless channels where the residual redundancy after source encoding is exploited for error protection. In combination with a mean-square or maximum APP estimation of the reconstructed source data, the whole decoding process is close to optimal. Furthermore, solutions for the proposed APP decoder with reduced complexity are discussed and compared to the near-optimal solution. When, in addition, channel codes are employed for protecting the variable-length encoded data, an iterative source-channel decoder can be obtained in the same way as for serially concatenated codes, where the proposed APP source decoder then represents one of the two constituent decoders. The simulation results show that this iterative decoding technique leads to substantial error protection for variable-length encoded correlated source signals, especially, when they are transmitted over highly corrupted channels.

In this paper, typical uplink scenarios in a cellular system are considered, where two cooperating mobile stations (serving, for example, as mobile relays) are transmitting the some information to a base station by using a distributed space-time coding scheme. Due to the distributed nature of the system, the transmitted signals are typically subject to different average path losses. For fixed distances between the mobile stations and the base station, the error performance of the distributed space-time coding scheme is determined analytically. Then, based on considerations concerning the spatial distribution of the mobile stations, analytical expressions for the distribution of the average path losses are derived and verified by means of simulations. These results are then used in order to compute the expected error performance of the system. It is shown that in most scenarios the average performance loss compared to a conventional multiple-antenna system with colocated antennas is less than 2 dB at a bit error rate of 10/sup -3/. The most significant performance losses occur for a large path-loss exponent.

A novel approach is presented for assessing the quality of transmission systems, comprising quantized source signals and APP source decoders, via Monte-Carlo simulation. A-posteriori probabilities are exploited in order to obtain an unbiased estimate of both the symbol error probability and the expected distortion for the transmission system; knowledge of the transmitted source signal is not necessary. Compared to the conventional method this blind quality estimation has a smaller estimation variance

We present an improved index-based a-posteriori probability (APP) decoding approach for variable-length encoded packetized data, where implicit residual source correlation is exploited for error protection. The proposed algorithm is based on a novel generalized two-dimensional state representation which leads to a three-dimensional trellis with unique state transitions. APP decoding on this trellis is realized by employing a two-dimensional version of the classical BCJR algorithm. This new method has the advantage that, due to the unique state representation, all available a-priori information can be fully exploited, which especially holds for the transition probabilities of the Markov model associated with the variable-length encoded source indices. Simulation results for an additional error protection by channel codes and iterative joint source-channel decoding show that the proposed approach leads to an increased error-correction performance compared to previously published results where a one-dimensional state representation is used.

We utilize both the implicit residual source correlation and the explicit redundancy from a forward error correction (FEC) scheme for the error protection of packetized variable-length encoded source indices. The implicit source correlation is exploited in a novel symbol-based soft-input a-posteriori probability (APP) decoder, which leads to an optimal decoding process in combination with a mean-squares or maximum a-posteriori probability estimation of the reconstructed source signal. When, additionally, the variable-length encoded source data is protected by channel codes, an iterative source-channel decoder can be obtained in the same way as for serially concatenated codes, where the outer constituent decoder is replaced by the proposed APP source decoder. Simulation results show that, by additionally considering the correlations between the variable-length encoded source indices, the error-correction performance can be highly increased.