We propose a two-layer coding architecture for communication of multiple users over a shared slotted medium enabling joint collision resolution and decoding. Each user first encodes its information bits with an outer code for reliability, and then transmits these coded bits with possible repetitions over transmission time slots of the access channel. The transmission patterns are dictated by the inner collision-resolution code and collisions with other users' transmissions may occur. We analyze two types of codes for the outer layer: long-blocklength LDPC codes, and short-blocklength algebraic codes. With LDPC codes, a density evolution analysis enables joint optimization of both outer and inner code parameters for maximum throughput. With algebraic codes, we invoke a similar analysis by approximating their average erasure correcting capability while assuming a large number of active transmitters. The proposed low-complexity schemes operate at a significantly smaller gap to capacity than the state of the art. Our schemes apply both to a multiple access scenario where the number of users within a frame is known a priori, and to a random access scenario where that number is known only to the decoder. In the latter case, we optimize an outage probability due to the variability in user activity.

Solving a large-scale system of linear equations is a key step at the heart of many algorithms in scientific computing, machine learning, and beyond. When the problem dimension is large, computational and/or memory constraints make it desirable, or even necessary, to perform the task in a distributed fashion. In this paper, we consider a common scenario in which a taskmaster intends to solve a large-scale system of linear equations by distributing subsets of the equations among a number of computing machines/cores. We propose a new algorithm called Accelerated Projection-based Consensus, in which at each iteration every machine updates its solution by adding a scaled version of the projection of an error signal onto the nullspace of its system of equations, and the taskmaster conducts an averaging over the solutions with momentum. The convergence behavior of the proposed algorithm is analyzed in detail and analytically shown to compare favorably with the convergence rate of alternative distributed methods, namely distributed gradient descent, distributed versions of Nesterov's accelerated gradient descent and heavy-ball method, the block Cimmino method, and Alternating Direction Method of Multipliers. On randomly chosen linear systems, as well as on real-world data sets, the proposed method offers significant speed-up relative to all the aforementioned methods. Finally, our analysis suggests a novel variation of the distributed heavy-ball method, which employs a particular distributed preconditioning and achieves the same theoretical convergence rate as that in the proposed consensus-based method.

Alternating direction method of multipliers (ADMM) is a popular convex optimisation algorithm, which is implemented in a distributed manner. Applying this algorithm to consensus optimisation problem, where a number of agents cooperatively try to solve an optimisation problem using locally available data, leads to a fully distributed algorithm which relies on local computations and communication between neighbours. In this study, the authors analyse the convergence of the distributed ADMM algorithm for solving a consensus optimisation problem over a lossy network, whose links are subject to failure. They present and analyse two different distributed ADMM‐based algorithms. The algorithms are different in their network connectivity, storage and computational resource requirements. The first one converges over a sequence of networks which are not the same but remains connected over all iterations. The second algorithm is convergent over a sequence of different networks whose union is connected. The former algorithm, compared to the latter, has lower computational complexity and storage requirements. Numerical experiments confirm the proposed theoretical analysis.

In the last years, the advancements in signal processing and integrated circuits technology allowed several research groups to develop working prototypes of in-band full-duplex wireless systems. The introduction of such a revolutionary concept is promising in terms of increasing network performance, but at the same time poses several new challenges, especially at the MAC layer. Consequently, innovative channel access strategies are needed to exploit the opportunities provided by full-duplex while dealing with the increased complexity derived from its adoption. In this direction, this paper proposes RTS/CTS in the Frequency Domain (RCFD), a MAC layer scheme for full-duplex ad hoc wireless networks, based on the idea of time-frequency channel contention. According to this approach, different OFDM subcarriers are used to coordinate how nodes access the shared medium. The proposed scheme leads to efficient transmission scheduling with the result of avoiding collisions and exploiting full-duplex opportunities. The considerable performance improvements with respect to standard and state-of-the-art MAC protocols for wireless networks are highlighted through both theoretical analysis and network simulations.

The emergence of networks of many devices in the context of cyber-physical systems motivates novel solutions for communication over random access channels. Currently deployed random access protocols attempt to avoid collisions, and target the performance of a scheduled multiple access system (a strategy known to be only suboptimal from the information-theoretic perspective). In contrast, in this paper, we allow collisions among the transmissions of different users. We consider code design for random access channels with erasures in which the number of users in each frame is unknown at the transmitters but known at the receiver, and we present a two-layer coding architecture for joint contention resolution and erasure correction. For random LDPC codes based on this scheme, the density evolution is asymptotically analyzed, which enables optimized code design for maximized throughput with constrained outage. The results demonstrate that the proposed low-complexity scheme approaches the outage capacity of the random access channel with erasures when the average number of active users is small.

State estimation is an essential part of energy management system in smart grid as it is a basis for many of the associated management and control processes. In this paper, we present a decentralized state estimation approach, based on consensus optimization and the alternating direction method of multipliers, that is robust against certain harsh class of false data injection schemes. The proposed scheme provides a reliable estimate of the global system state in a distributed manner even if the system is regionally unobservable to some regional controllers, but globally observable across regions. The scheme also accommodates different communication network topologies for a given power network. We assess the performance of the presented schemes on IEEE 14 and 118 bus test systems.

ADMM is a popular algorithm for solving convex optimization problems. Applying this algorithm to distributed consensus optimization problem results in a fully distributed iterative solution which relies on processing at the nodes and communication between neighbors. Local computations usually suffer from different types of errors, due to e.g., observation or quantization noise, which can degrade the performance of the algorithm. In this work, we focus on analyzing the convergence behavior of distributed ADMM for consensus optimization in presence of additive node error. We specifically show that (a noisy) ADMM converges linearly under certain conditions and also examine the associated convergence point. Numerical results are provided which demonstrate the effectiveness of the presented analysis.

Recently, several working implementations of inband full-duplex wireless systems have been presented, where the same node can transmit and receive simultaneously in the same frequency band. The introduction of such a possibility at the physical layer could lead to improved performance but also poses several challenges at the MAC layer. In this paper, an innovative mechanism of channel contention in full-duplex OFDM wireless networks is proposed. This strategy is able to ensure efficient transmission scheduling with the result of avoiding collisions and effectively exploiting full-duplex opportunities. As a consequence, considerable performance improvements are observed with respect to standard and state-of-the-art MAC protocols for wireless networks, as highlighted by extensive simulations performed in ad hoc wireless networks with varying number of nodes.

In this paper, statistical Quality of Service provisioning in next generation heterogeneous mobile cellular networks is investigated. To this aim, any active entity of the cellular network is regarded as a queuing system, whose statistical QoS requirements depend on the specific application. In this context, by quantifying the performance in terms of effective capacity, we introduce a lower bound for the system performance that facilitates an efficient analysis. We exploit this analytical framework to give insights about the possible improvement of the statistical QoS experienced by the users if the current heterogeneous cellular network architecture migrates from a Half Duplex to a Full Duplex mode of operation. Numerical results and analysis are provided, where the network is modeled as a Matérn point processes with a hard core distance. The results demonstrate the accuracy and computational efficiency of the proposed scheme, especially in large scale wireless systems.

Recent studies indicate the feasibility of in-band full-duplex (FD) wireless communications, where a wireless radio transmits and receives simultaneously in the same band. Due to its potential to increase the capacity, analyzing the performance of a cellular network that contains full-duplex devices is crucial. In this paper, we consider maximizing the weighted sum-rate of downlink and uplink of a single cell OFDMA network which consists of an imperfect FD base-station (BS) and a mixture of half-duplex and imperfect full-duplex mobile users. To this end, the joint problem of sub-channel assignment and power allocation is investigated and a two-step solution is proposed. A heuristic algorithm to allocate each sub-channel to a pair of downlink and uplink users with polynomial complexity is presented. The power allocation problem is convexified based on the difference of two concave functions approach, for which an iterative solution is obtained. Simulation results demonstrate that when all the users and the BS are perfect FD nodes the network throughput could be doubled, Otherwise, the performance improvement is limited by the inter-node interference and the self-interference. We also investigate the effect of the self-interference cancellation capability and the percentage of FD users on the network performance in both indoor and outdoor scenarios.

Digital crowdsourcing (CS) is a modern approach to perform certain large projects using small contributions of a large crowd. In CS, a taskmaster typically breaks down the project into small batches of tasks and assigns them to so-called workers with imperfect skill levels. The crowdsourcer then collects and analyzes the results for inference and serving the purpose of the project. In this work, the CS problem, as a human-in-the-loop computation problem, is modeled and analyzed in an information theoretic rate-distortion framework. The purpose is to identify the ultimate fidelity that one can achieve by any form of query from the crowd and any decoding (inference) algorithm with a given budget. The results are established by a joint source channel (de)coding scheme, which represent the query scheme and inference, over parallel noisy channels, which model workers with imperfect skill levels. We also present and analyze a query scheme dubbed k-ary incidence coding and study optimized query pricing in this setting.

Self-Healing is one of the key important functionalities in self-organizing mobile communication networks. Despite its importance, self-healing has yet to receive a deserving research attention in the literature. This paper considers important quality indicators in a live mobile communications network and presents an automatic unified detection and diagnosis framework to identify root causes of faults occurred in the network. The proposed solution relies on unsupervised clustering of both traffic and signaling (continuous) key performance indicators for diagnosis. Hence, it is immune to the human error in the modeling phase. It also allows to effectively encapsulate experts knowledge as they relate clusters to root causes in the design phase. Detailed analysis of fault detection, the clustering schemes and the diagnosis are provided using operations support systems data of a real cellular network.

This paper investigates distortion-power tradeoffs in transmission of quasi-stationary sources over delay and buffer limited block fading channels by studying encoder and decoder buffering techniques to smooth out the source and channel variations. Four source and channel coding schemes that consider buffer and power constraints are presented to minimize the reconstructed source distortion. The first one is a high performance scheme, which benefits from optimized source and channel rate adaptation. In the second scheme, the channel coding rate is fixed and optimized along with transmission power with respect to channel and source variations; hence this scheme enjoys simplicity of implementation. The two last schemes have fixed transmission power with optimized adaptive or fixed channel coding rate. For all the proposed schemes, closed form solutions for mean distortion, optimized rate, and power are provided and in the high SNR regime, the mean distortion exponent and the asymptotic mean power gains are derived. The proposed schemes with buffering exploit the diversity due to source and channel variations. Specifically, when the buffer size is limited, fixed channel rate adaptive power scheme outperforms an adaptive rate fixed power scheme. Furthermore, analytical and numerical results demonstrate that with limited buffer size, the system performance in terms of reconstructed signal SNR saturates as transmission power increases, suggesting that appropriate buffer size selection is important to achieve a desired reconstruction quality.

There are now several works on the use of the additive inverse Gaussian noise (AIGN) model for the random transit time in molecular communication (MC) channels. The randomness invariably causes inter- symbol interference (ISI) in MC, an issue largely ignored or simplified. In this paper we derive an upper bound and two lower bounds for MC based on amplitude shift keying (ASK) in presence of ISI. The Blahut-Arimoto algorithm (BAA) is modified to find the input distribution of transmitted symbols to maximize the lower bounds. Our results show that over wide parameter values the bound are close.

We investigate the ability of the physical layer approaches of constellation shaping and forward error correction to enhance the performance of full duplex (FD) wireless radio systems. We use low density parity check and optimized shaping codes to transmit non-equiprobable constellation points from an amplitude phase shift keying modulation with imperfect power amplifier and in the presence of FD residual self interference. A simple approach to the design of optimized shaping codes for a given signal to noise ratio and residual self interference power is presented. Simulation results show that a FD transceiver with bit interleaved coded modulation, optimized shaping and iterative demodulation and decoding noticeably improves the performance in comparison to the same system without constellation shaping.

In this paper, a scheme based on 1-D nested lattice quantization followed by multi-level distributed arithmetic coding (MLDAC) as the Slepian-Wolf (SW) code is proposed for the lossy source coding of continuous sources. This system can be employed in distributed video and image coding applications. The output of the quantizer is first converted to binary, and then the SW coding is applied on each bit plane. An efficient algorithm for joint decoding of bit planes at the decoder is proposed, which exploits the dependency of the bit planes with each other and the side-information available at the decoder. Also, a methodology for rate allocation among the bit planes is presented. The quantization parameters are designed to improve the end-to-end system performance based on its overall rate-distortion function taking into account both the distortions due to quantization and imperfect practical Slepian-Wolf coding. The simulation results demonstrate the effectiveness of the proposed MLDAC with the lattice quantization.

In this paper, a general algorithm is proposed for rate analysis and code design of linear index coding problems. Specifically a solution for minimum rank matrix completion problem over finite fields representing the linear index coding problem is devised in order to find the optimum transmission rate given vector length and size of the field. The new approach can be applied to both scalar and vector linear index coding.

In this paper we investigate the consequences of biologically plausible constraints on predictions of the Berger-Levy energy efficient neuron model. As new constraints we consider i) a peak power constraint, ii) peak energy expenditure per ISI constraint, iii) a lower bound on the value of inter spike interval (ISI), and iv) lower and upper bounds on the excitatory postsynaptic potential (EPSP) intensity, λ. Our analysis shows that considering these constraints of the capacity per unit cost maximization problem changes the shape of probability distribution function (PDF) of λ and the ISIs. We show, using numerical solutions of the optimization problem, that the new constraints change the PDFs of λ and the ISIs in term of their shape and location of the peak value. We also derive predictions for how the coefficient of variation (CV) of the ISI is changed, which is easier to characterize experimentally than the full PDF.

In this paper, delay-limited transmission of quasi-stationary sources over block fading channels is considered. Considering distortion outage probability as the performance measure, two source and channel coding schemes with power adaptive transmission are presented. The first one is optimized for fixed rate transmission, and hence enjoys simplicity of implementation. The second one is a high performance scheme, which also benefits from optimized rate adaptation with respect to source and channel states. In high SNR regime, the performance scaling laws in terms of outage distortion exponent and asymptotic outage distortion gain are derived, where two schemes with fixed transmission power and adaptive or optimized fixed rates are considered as benchmarks for comparisons. Various analytical and numerical results are provided which demonstrate a superior performance for source and channel optimized rate and power adaptive scheme. It is also observed that from a distortion outage perspective, the fixed rate adaptive power scheme substantially outperforms an adaptive rate fixed power scheme for delay-limited transmission of quasi-stationary sources over wireless block fading channels. The effect of the characteristics of the quasi-stationary source on performance, and the implication of the results for transmission of stationary sources are also investigated.

In this paper, a link adaptation and untrusted relay assignment (LAURA) framework for efficient and reliable wireless cooperative communications with physical layer security is proposed. Using sharp channel codes in different transmission modes, reliability for the destination and security in the presence of untrusted relays (low probability of interception) are provided through rate and power allocation. Within this framework, several schemes are designed for highly spectrally efficient link adaptation and relay selection, which involve different levels of complexity and channel state information requirement. Analytical and simulation performance evaluation of the proposed LAURA schemes are provided, which demonstrates the effectiveness of the presented designs. The results indicate that power adaptation at the source plays a critical role in spectral efficiency performance. Also, it is shown that relay selection based on the signal to noise ratio of the source to relays channels provides an interesting balance of performance and complexity within the proposed LAURA framework.

In this paper, the problem of uniform quantization for the Wyner-Ziv (WZ) problem in presence of uncertain side information at the encoder is addressed. The uncertain side information problem arises in data compression with side information (SI) when the joint probability distribution of source and SI is not (fully) known at the encoder. The uncertainty is due to the time varying nature of the dependency of source and SI in settings such as wireless sensor networks and distributed video coding. In this paper, a solution is proposed which consists of a multi-mode WZ encoder in conjunction with a carefully designed feedback scheme from the decoder. Each encoder mode is associated with a uniform nested quantizer. In this framework, the quantization parameters, the feedback scheme, and the source coding rate are jointly optimized to minimize the average rate or distortion. Two distinct schemes with adaptive or fixed transmission rate are proposed. Simulation results are provided which demonstrate the effectiveness of the presented solutions and assess the effect of different design parameters including the number of modes (feedback rate) on the performance.

A secure link adaptation framework is proposed, which exploits the inherent fluctuations of wireless fading channels for high-performance communications and physical layer security in the presence of an eavesdropper. The authors use very sharp channel codes intended for reliability and demonstrate that they also provide security, when successfully incorporated in the link adaptation design framework and the security constraint is not very stringent. Two scenarios are considered in which the transmitter has access to the eavesdropper channel state information either instantaneously or statistically. The proposed secure link adaptation framework is formulated to maximise the spectral efficiency of the communication, whereas both reliability and security constraints are provisioned. Different designs are considered when the security constraint is quantified by instantaneous bit error rate (BER), average BER or leakage probability. For the problem with instantaneous BER constraint, an efficient analytical solution and a numerical solution are presented. A closed-form analytical solution is also provided for secure link adaptation with average BER constraint, whereas the problem with the leakage constraint is tackled numerically. Extensive results and detailed analysis are provided to draw insights on the effects of different design parameters on the performance.

This paper addresses network code design for robust transmission of sources over an orthogonal two-hop wireless network with a broadcasting relay. The network consists of multiple sources and destinations in which each destination, benefiting the relay signal, intends to decode a subset of the sources. Two special instances of this network are orthogonal broadcast relay channel and the orthogonal multiple access relay channel. The focus is on complexity constrained scenarios, e.g., for wireless sensor networks, where channel coding is practically imperfect. Taking a source-channel and network coding approach, we design the network code (mapping) at the relay such that the average reconstruction distortion at the destinations is minimized. To this end, by decomposing the distortion into its components, an efficient design algorithm is proposed. The resulting network code is nonlinear and substantially outperforms the best performing linear network code. A motivating formulation of a family of structured nonlinear network codes is also presented. Numerical results and comparison with linear network coding at the relay and the corresponding distortion-power bound demonstrate the effectiveness of the proposed schemes and a promising research direction.

In mobile Ad-hoc networks forwarding data through intermediate relays extends the coverage area and enhances the network throughput. Dynamic assignment of routes for data transmission from sources to destinations that is optimized based on channel conditions in a cross-layer manner could provide improved performance. We consider a general multiuser two-hop transmission, where each flow is subject to a constraint on the end-to-end buffering delay and its associated packet loss rate as a quality of service requirement. The objective is to maximize the weighted sum-rate between source destination pairs, while the QoS requirement is satisfied. We introduce two new cross-layer dynamic route selection schemes in this setting that are designed involving physical, MAC, and network layers. In cross-layer opportunistic dynamic route selection, the routes are assigned dynamically based on the instantaneous state of fading channels. In time division cross-layer dynamic route selection, the route for data transmission is scheduled based on the average quality of links. Simulation results show that our proposed cross-layer dynamic route selection schemes provide superior performance in comparison to prior art.

This paper presents performance analysis and cross-layer design approaches for hybrid ARQ (HARQ) protocols in wireless networks, which employ adaptive modulation and coding (AMC) in conjunction with adaptive cooperative diversity and are subject to time-correlated fading channels. We first consider a point-to-point scenario, i.e., non-cooperative HARQ with AMC. Utilizing a Markov channel model which accounts for the temporal correlation in the successive transmission of incremental redundancy by the HARQ protocol, we derive the system throughput and the packet loss probability based on a rate compatible punctured convolutional code family. Next, we consider a cooperative HARQ (CHARQ) scheme in which a relay node, also equipped with AMC, retransmits redundancy packets when it is able to decode the source information packet correctly. For this scenario, we also derive the throughput and packet loss performance. Finally, we present a cross-layer AMC design approach which takes into account the hybrid ARQ protocol at the link layer. The results illustrate that including AMC in the HARQ protocols leads to a substantial throughput gain. While the performance of the AMC with HARQ protocol is strongly affected by the channel correlation, the CHARQ protocol provides noticeable performance gains over correlated fading channels as well.

We consider a primary and a cognitive user transmitting over a wireless fading interference channel. A discrete rate link adaptation scheme is proposed to maximize the average spectral efficiency of the cognitive radio, while a minimum average spectral efficiency for the primary user is provisioned. The discrete rate scheme for interfering transmission is devised based on two sets of modulation and coding pairs for the primary and cognitive links. The primary transmitter adapts its rate (modulation and coding) solely based on signal to interference plus noise ratio of its link; however, the cognitive transmitter influences the primary link rate by imposing a proper SINR. The adaptive modulation and coding mode selection and power control at the cognitive transmitter are optimized based on SINR of both links. The problem is then cast as a nonlinear discrete optimization problem for which a fast and efficient suboptimum solution is presented. We also present a scheme with rate adaptive and constant power cognitive radio. An important characteristic of the proposed schemes is that no computation overhead is imposed on primary radio due to cognitive radio activity. Numerical results and comparison with the interweave approach to cognitive radio demonstrate the efficiency of the proposed solutions.

A Wyner-Ziv Quantizer design method is introduced when the indices at the output of the encoder are transmitted over a noisy channel. The source encoder is considered as a scalar Lloyd quantizer followed by a binning and an index assignment (BIA) mapping. A modified simulated annealing based algorithm is used for BIA mapping design. A minimax solution for Wyner-Ziv problem under channel mismatch condition is also suggested when the channel is assumed to be binary symmetric channel and no information about the statistic of channel is available except the range of bit error rate. Finally the simulation results are presented which show the effectiveness of the proposed algorithm over other common alternative approaches. These results approve the proposed minimax solution too.

This paper presents performance analysis and cross-layer design approaches for hybrid ARQ (HARQ) protocol in wireless networks which employ adaptive modulation and coding (AMC) at the physical layer and are subject to time-correlated fading channels. Utilizing a Markov channel model which accounts for the temporal correlation in successive parity transmissions by the adaptive rate HARQ protocol, we derive the system throughput and the packet loss probability based on a rate compatible punctured convolutional (RCPC) code family. As an application, we then present a cross-layer AMC design which takes into account the performance gain of the HARQ protocol at the link layer. The results illustrate that including AMC in the HARQ protocol leads to a substantial throughput gain, but the channel correlation strongly diminishes the performance of the HARQ protocol in terms of throughput and packet loss rate.

In this study, an approach for robust network coding is introduced for multicast in a directed acyclic network in the presence of network edge failures. The proposed designs aim at combating the resulting path failures, which result in interestingly scalable solutions. A robust network coding scheme (RNC1) is proposed that, devising a rate-path diversity trade-off for the receivers, attains the post-failure capacity of the network with high probability. The scheme is receiver based and can also be applied for correcting random erasures. Next, a rate-guaranteed robust network coding scheme (RNC2) is proposed. The code guarantees the maximum rate for a predetermined number of path failures. The scheme, of course, attains the refined Singleton bound for the edge failure model. A path failure may not necessarily reduce the network capacity, as the remaining intact edges within the network may still facilitate backup paths from the source to the sinks. We introduce RNC3 to employ such backup paths in addition to the original paths and guarantee multicast at a certain rate in the presence of all edge/path failure patterns that do not reduce the capacity below this rate. All the three proposed schemes for multicast are robust to a number of edge failures that may, in general, exceed the refined Singleton bound. Our analyses indicate that the design complexities and the required field sizes grow as a function of the number of network paths, as opposed to the number of network edges because of prior schemes.

An optimisation framework for wireless link adaptation which maximises the system throughput subject to a packet-level quality of service (QoS) constraint is presented. The authors consider joint adaptive variation of the transmitted power level, transmission data rate and packet-error-rate (PER) at the physical layer to improve the throughput performance of the selective-repeat automatic repeat request (SR-ARQ) protocol over block-fading channels. Specifically, for discrete-rate coded M-QAM schemes, the authors present a power and rate adaptation algorithm that guarantees a target PER constraint. The proposed framework also facilitates optimising the throughput performance for delay constrained wireless applications, which imposes a limit on the number of retransmissions for the ARQ. In particular, a link adaptation scheme is presented, which guarantees a target packet-loss rate (PLR) when a truncated SR ARQ protocol at the data-link layer is employed. Numerical results indicate that the proposed adaptation schemes compared with other adaptive schemes noticeably enhance the system throughput. Also, in the case of truncated ARQ, the results illustrate a fundamental trade-off between the delay and PLR QoS metrics achieved at the maximum throughput performance.

A flexible rate Slepian-Wolf (SW) code is constructed, which is vital for wireless sensor network applications. The proposed solution is based on an efficient and practical algorithm to compute the syndrome of the rate-compatible convolutional codes (RCPC). Using this algorithm, there is no need to compute the syndrome of punctured version of the mother code for each puncturing matrix, which is complex. Instead, the syndrome of the punctured code is the punctured version of the syndrome of the mother code using the same pattern of puncturing. The algorithm is general for all convolutional codes in Zq. The strategy is also generalized for parallel and serial concatenated convolutional codes. For the cases, where the dependencies among sources are modeled as a virtual discrete channel, a simplified decoding scheme is suggested. This method is generalized to achieve all points on the SW boundary using a simple code design technique. Simulation results demonstrate the performance and effectiveness of the proposed methods.

In this paper, we consider the problem of transmission of a delay-sensitive and bursty traffic source over a time-varying Nakagami-m fading channel in a cross-layer optimization framework. Aiming at minimizing the packet delay due to queuing at the data link layer, we present power and rate adaptation policies for coded M-QAM modulation schemes, which guarantee a prescribed channel packet error rate constraint. This is also equivalent to minimizing the system packet loss rate. The proposed adaptation policies are derived both for block and correlated channel fading scenarios. To enable the transmission policy design, we use a statistical model to characterize the packet delay derived from a known result of large deviations theory. Considering the error resolution capability of the automatic repeat request protocol, we then provide the appropriate analytical tools to incorporate the effect of packet retransmission in the proposed optimization framework for transmission policy design. The results show that the proposed adaptation policies compared to others adaptive solutions, significantly improve the delay and throughput performance for delay-sensitive bursty traffic over time-varying fading channels.

In this paper, we consider the problem of transmission of a bursty data-packet traffic over MIMO space-time diversity fading channels. Aiming at minimizing the packet loss probability due to buffer overflow at the data link layer, we propose a cross-layer analytical framework for transmission policy design which directly satisfies a packet error rate (PER) constraint at the physical layer. Particularly, we present a power and rate adaptation policy for coded M-QAM modulation schemes that are implemented in conjunction with a MIMO diversity system. To enable the transmission policy design, we use a statistical model to characterize the buffer overflow derived from a large deviations result. Numerical results demonstrate that the proposed adaptation policy significantly improves the packet-loss performance for bursty data traffic over MIMO fading channels, when compared to others adaptive solutions.

In this paper, we consider the problem of transmitting a delay-sensitive and bursty traffic over a time- varying MIMO Nakagami-ra fading channel, and optimize the delay performance of packet queuing at the data link layer. We use a statistical model to characterize the packet delay derived from a known large deviations result, and allows for analytical insights into transmission policy design. We derive power and rate adaptation policies for coded M-QAM modulation schemes that are implemented in conjunction with a MIMO diversity system; such that the packet delay probability is minimized subject to a prescribed channel packet error rate constraint. This is also equivalent to minimizing the system packet loss rate. The proposed adaptation policies depend not only on channel conditions but also on the quality of service performance at the data link layer. Numerical results show that the proposed cross- layer adaptation policies significantly improve the delay performance for delay-sensitive bursty traffic over MIMO fading channels, when compared to others adaptive solutions.

The block network error control coding, BNEC, is presented to combat error and erasure for multicast in directed acyclic networks. Aiming at reducing complexity, BNEC syndrome-based decoding and detection is introduced. Next, we propose a three-stage syndrome-based BNEC decoding, comprising error detection, finding error positions and error values. Besides considering bounded-distance decoding for error correction up to refined Singleton bound, we present BNEC complete decoding and show that, a code with redundancy order deltatfor receiver t, corrects deltat-1errors with a probability approaching 1, for a sufficiently large field size. Also, complete maximum likelihood BNEC decoding is proposed. As probability of error in different network edges is not equal in general, the number of edge errors, assessed in Singleton bound, is not a sufficient statistic for ML decoding.

A simple but powerful scheme for distributed source coding (DSC) based on the concept of binning and syndromes and non traditional turbo codes is proposed. The previous works on the compression with side information using turbo codes and the binning technique are focused on binary turbo codes. The source is considered to be binary or is converted to a binary stream. This conversion, however, reduces the redundancy that could be exploited by the compression algorithm. To achieve higher compression efficiency, the authors propose using a scheme based on a turbo decoder that decides over symbols rather than bits. In the same direction and for further performance improvement, at the cost of increased encoder complexity, they also present a DSC scheme based on non-binary turbo codes. The results demonstrate improved performance. Based on the suggested algorithms, a scheme for gathering real data in wireless sensor networks and assess the corresponding energy savings is proposed.

In this paper, we consider a deadline constrained traffic and analyze the joint effects of packet queuing and adaptive modulation (AM) for packet transmission in MIMO systems. We present a general analytical procedure, and derive the achievable delay-bound violation probability, the packet loss rate, and the average throughput of AM in MIMO wireless links employing orthogonal space time block coding (STBC) over Nakagami fading channels. In the proposed analytical procedure, we use statistical delay-bound for deadline constrained traffic based on the effective bandwidth theory. Based on our performance analysis, we propose a cross-layer design, which selects the optimal target packet error rate of AM at the physical layer, to minimize the packet loss rate and maximize the average throughput, when combined with packet queuing at the data link layer. Numerical results demonstrate that the system performance depends on various parameters, and show the performance gain due to cross-layer design. We also discuss briefly possible applications of our proposed modeling to quality of service guarantee in multiuser scenarios.

We introduce a centralized joint network-channel coding scheme for maximized throughput and increased robustness against link failure in a communication network. We introduceRNC1(h,k)to multicastkindependent data in a directed and acyclic network with capacityh,k≤h. The redundancy through path diversity increases the resistance of the network against link failure. An improved version ofRNC1,RNC1(h,k), is introduced to guarantee the ratekforh-kflow failures. The proposed schemes exploit the network structure for code design, which results in a manageable design complexity.

Adaptive modulation and coding (AMC) and automatic repeat request (ARQ) protocol are powerful techniques for improving the spectral efficiency (SE) or the error performance in wireless packet networks over fading channels. In this paper, we first consider a discrete-rate M-QAM based AMC scheme and propose a variable power transmission scheme for transmission of packets over block-fading wireless channels. Considering the error-correcting capability of the truncated-ARQ, which depends on the maximum number of retransmissions (Nr), we then optimize the AMC and power control schemes that guarantee the required packet loss rate (PLR) performance while satisfying an average transmit power constraint. Numerical results reveal that in wireless packet systems with limited packet length, the proposed link adaptation algorithm improves the system performance in terms of the SE and the PLR However, this improvement depends on the target PLR and NrWhen the target PLR is sufficiently small, this improvement will be observed in terms of the SE for small Nphowever, in the larger target PLR, the improvement will only be observed in terms of the achievable PLR.

Network coding is used to efficiently transmit information in a network from source nodes to sink nodes through intermediate nodes. It has been shown that linear coding is sufficient to achieve the multicast network capacity. In this paper, we introduce a method to design capacity achieving network codes based on reinforcement learning and makes using the market theory concepts. We demonstrate that the proposed algorithm is decentralized and polynomial time complex; while it constructs the codes much faster than other random methods with the same complexity order, especially in large networks with small field sizes. Furthermore, the proposed algorithm is robust to link failures and is used to reduce the number of encoding nodes in the network.

Abstract—Exploiting the residual redundancy in a source coder output stream during the decoding process has been proven to be a bandwidth efficient way to combat the noisy channel degradations. In this paper, we consider soft reconstruction of speech spectrum, in GSM adaptive multirate and IS-641 vocoders, transmitted over a channel disturbed with noise and/or packet loss. Several schemes are presented which exploit different levels of intraframe and interframe residual redundancy for improved source decoding at the receiver. A packetization strategy is proposed which is matched to the presented error concealment units. For decoders that exploit the residual redundancy, extensive complexity has been a serious concern, especially as the quantizer bitrate increases. In this paper, a novel method is presented to construct reduced complexity algorithms. The proposed methodology is based on the classification of the signal domain and efficient approximation of the residual redundancy or the a priori transition probabilities. The presented schemes provide high quality error concealment solutions for code excited linear prediction (CELP) coders. Index Terms—Erasure channel, forward–backward recursion, global system for mobile communications–adaptive multirate (GSM–AMR), IS-641, joint source channel coding (JSC), linear predictive coding (LPC), line spectral frequency (LSF), Markov models, minimum mean squared error (MMSE) estimation, multiple description coding (MDS), packet loss concealment (PLC), residual redundancies, source decoding, speech coding, speech error concealment. I.

A lattice-based scheme for the single-frame and the double-frame quantization of the speech line spectral frequency parameters is proposed. The lattice structure provides a low-complexity vector quantization framework, which is implemented using a trellis structure. In the single-frame scheme, the intraframe dependencies are exploited using a linear predictor. In the double-frame scheme, the parameters of two consecutive frames are jointly quantized and hence the interframe dependencies are also exploited. A switched scheme is also considered in which, lattice-based double-frame and single-frame quantization is performed for each two frame and the one which results in a lower distortion is chosen. Comparisons to the Split-VQ, the Multi-Stage VQ, the Trellis Coded Quantization, the interframe Block-Based Trellis Quantizer, and the interframe scheme used in IS-641 EFRC and the GSM AMR codec are provided. These results demonstrate the effectiveness of the proposed lattice-based quantization schemes, while maintaining a very low complexity. Finally, the issue of the robustness to channel errors is investigated

We study a multiple-input multiple-output (MIMO) wireless system where the channel state information is partially available at the transmitter through a feedback link. Based on singular value decomposition, the MIMO channel is split into independent sub-channels. Effective feedback of the required spatial channel information entails efficient quantization/encoding of a unitary matrix. We propose two schemes for quantizing unitary matrices via Givens rotations and examine the performance for a scenario where the rates allocated to the sub-channels are selected according to their corresponding gains. Numerical results show that the proposed schemes offer a significant performance improvement as compared to that of MIMO systems without feedback, with a negligible increase in the complexity.

In this work, we study a multiple-input multiple-output wireless system, where the channel state information is partially available at the transmitter through a feedback link. Based on singular value decomposition, the MIMO channel is split into independent subchannels, which allows separate, and therefore, efficient decoding of the transmitted data signal. Effective feedback of the required spatial channel information entails efficient quantization/encoding of a Haar unitary matrix. The parameter reduction of an n /spl times/ n unitary matrix to its n/sup 2/ - n basic parameters is performed through Givens decomposition. We prove that Givens matrices of a Haar unitary matrix are statistically independent. Subsequently, we derive the probability distribution function (PDF) of the corresponding matrix elements. Based on these analyses, an efficient quantization scheme is proposed. The performance evaluation is provided for a scenario where the rates allocated to each independent channel are selected according to its corresponding gain. The results indicate a significant performance improvement compared to the performance of MIMO systems without feedback at the cost of a very low-rate feedback link.

The design of source decoders that employ the residual redundancy at the source coder output is an interesting research direction in the joint source channel coding framework. Such decoders are expected to replace the traditionally heuristic error concealment units that are elements of most multimedia communication systems. In this work, we consider the reconstruction of signals encoded with a multi-stage vector quantizer (MSVQ) and transmitted over a noisy channel. The MSVQ maintains a moderate complexity and, due to its successive refinement feature, is a suitable choice for the design of layered (progressive) source codes. An approximate MMSE source decoder for MSVQ is presented and its application to reconstruction of LPC parameters in MELP is analyzed. Numerical results demonstrates the effectiveness of the proposed schemes.

In this paper, we consider the problem of decoding predictively encoded signal over a noisy channel when there is residual redundancy (captured by a /spl gamma/-order Markov model) in the sequence of transmitted data. Our objective is to minimize the mean-squared error (MSE) in the reconstruction of the original signal (input to the predictive source coder). The problem is formulated and solved through minimum mean-squared error (MMSE) decoding of a sequence of samples over a memoryless noisy channel. The related previous works include several maximum a posteriori (MAP) and MMSE-based decoders. The MAP-based approaches are suboptimal when the performance criterion is the MSE. On the other hand, the previously known MMSE-based approaches are suboptimal, since they are designed to efficiently reconstruct the data samples received (the prediction residues) rather than the original signal. The proposed scheme is set up by modeling the source-coder-produced symbols and their redundancy with a trellis structure. Methods are presented to optimize the solutions in terms of complexity. Numerical results and comparisons are provided, which demonstrate the effectiveness of the proposed techniques.

Exploiting the residual redundancy in a source coder output stream during the decoding process has been proven to be a bandwidth-efficient way to combat noisy channel degradations. This redundancy can be employed to either assist the channel decoder for improved performance or design better source decoders. In this work, a family of solutions for the asymptotically optimum minimum mean-squared error (MMSE) reconstruction of a source over memoryless noisy channels is presented when the redundancy in the source encoder output stream is exploited in the form of a /spl gamma/-order Markov model (/spl gamma//spl ges/1) and a delay of /spl delta/,/spl delta/>0, is allowed in the decoding process. It is demonstrated that the proposed solutions provide a wealth of tradeoffs between computational complexity and the memory requirements. A simplified MMSE decoder which is optimized to minimize the computational complexity is also presented. Considering the same problem setup, several other maximum a posteriori probability (MAP) symbol and sequence decoders are presented as well. Numerical results are presented which demonstrate the efficiency of the proposed algorithms.

An efficient block-based trellis quantization (BTQ) scheme is proposed for the quantization of the line spectral frequencies (LSF) in speech coding applications. The scheme is based on the modeling of the LSF intraframe dependencies with a trellis structure. The ordering property and the fact that LSF parameters are bounded within a range is explicitly incorporated in the trellis model. BTQ search and design algorithms are discussed and an efficient algorithm for the index generation (finding the index of a path in the trellis) is presented. Also the sequential vector decorrelation technique is presented to effectively exploit the intraframe correlation of LSF parameters within the trellis. Based on the proposed block-based trellis quantizer, two intraframe schemes and one interframe scheme are proposed. Comparisons to the split-VQ, the trellis coded quantization of LSF parameters, and the multi-stage VQ, as well as the interframe scheme used in IS-641 EFRC and the GSM AMR codec are provided. These results demonstrate that the proposed BTQ schemes outperform the above systems.

Exploiting the residual redundancy in a source coder output stream during the decoding process has been proven to be a bandwidth efficient way to combat the noisy channel degradations. We consider soft reconstruction of LSF parameters in the IS-641 CELP coder transmitted over a noisy channel. We propose two schemes. The first scheme attempts to exploit the interframe residual redundancies in the sequence of received parameters. The second approach exploits both interframe and intraframe residual redundancies. Simulation results are provided which demonstrates the efficiency of the algorithms. Another issue addressed here, is a methodology to efficiently approximate and store the residual redundancies or the a priori transition probabilities. For quantizers with high rates calculating these probabilities require a huge number of source samples, and storing them also require a large amount of memory. These issues can well make the decoder design process an impractical task. The proposed method is based on the classification of the signal domain. The presented schemes provide high quality error concealment solutions for CELP coders.

In this article, a low bit-rate low-complexity block-based trellis quantization (BTQ) scheme is proposed for quantization of line spectral frequencies for speech coding applications. The branches in the trellis diagram correspond to the LSF difference code-words and the states correspond to quantized LSF parameters. An efficient algorithm for the index generation (finding the index of a path in the trellis) is introduced. The proposed BTQ achieves the transparent coding quality at 23 bits/frame (1150 b/sec.) and offers a gain of 3 b/frame (150 b/s) and significant reduction in complexity, compared to the IS-641 split-VQ. An interframe BTQ scheme is also presented to exploit the redundancies between the adjacent frames. The interframe scheme is found to achieve an additional 50 b/sec reduction of the bit-rate and is based on adaptive block-based trellis quantization of the prediction residues.