The significance of distributed learning and inference algorithms in Internet of Things (IoT) network is growing since they flexibly distribute computation load between IoT devices and the infrastructure, enhance data privacy, and minimize latency. However, a notable challenge stems from the influence of communication channel conditions on their performance. In this work, we introduce COMSPLIT: a novel communication-aware design for split learning (SL) and inference paradigm tailored to processing time series data in IoT networks. COMSPLIT provides a versatile framework for deploying adaptable SL in IoT networks affected by diverse channel conditions. In conjunction with the integration of an early-exit strategy, and addressing IoT scenarios containing devices with heterogeneous computational capabilities, COMSPLIT represents a comprehensive design solution for communication-aware SL in IoT networks. Numerical results show superior performance of COMSPLIT compared to vanilla SL approaches (that assume ideal communication channel), demonstrating its ability to offer both design simplicity and adaptability to different channel conditions.

In this paper, we propose a novel decoding method for Quantum Low-Density Parity-Check (QLDPC) codes based on Graph Neural Networks (GNNs). Similar to the Belief Propagation (BP)-based QLDPC decoders, the proposed GNN-based QLDPC decoder exploits the sparse graph structure of QLDPC codes and can be implemented as a message-passing decoding algorithm. We compare the proposed GNN-based decoding algorithm against selected classes of both conventional and neural–enhanced QLDPC decoding algorithms across several QLDPC code designs. The simulation results demonstrate excellent performance of GNN-based decoders along with their low complexity compared to competing methods.

With the advent of the multimodal immersive communication system, people can interact with each other using multiple devices for sensing, communication and/or application level control either onsite or remotely. As a breakthrough concept, a distributed sensing, computing, communications, and control (DS3C) fabric is introduced in this paper for provisioning 6G services in multi-tenant environments in a unified manner. The DS3C fabric can be further enhanced by natively incorporating intelligent algorithms for network automation and managing networking, computing, and sensing resources efficiently to serve vertical use cases with extreme and/or conflicting requirements. As such, the paper proposes a novel end-to-end 6G system architecture with enhanced intelligence spanning across different network, computing, and business domains, identifies vertical use cases and presents an overview of the relevant standardisation and pre-standardisation landscape.

In this paper, we investigate performance of a random access scheme that exploits binary-tree algorithm (BTA) with the free access. We assume a scenario where the receiver is capable to perform both multi-packet reception (MPR) and successive interference cancellation (SIC), where for the purpose of the latter only the last received and undecoded signal can be stored. We distinguish between two variants of the algorithm, where in the first the SIC can be triggered by a decoding event but also executed blindly among yet undecoded slots, while in the second the receiver can only execute the SIC after the decoding event. We analytically derive the maximum stable throughput (MST) of the scheme assuming Poisson arrivals. The evaluation shows that the scheme is able of achieving a favorable performance in comparison to the scenarios when only either MPR or SIC with single-slot memory is used, making it a suitable candidate for an access solution in applications that are characterized with a massive number of users and sporadic traffic arrivals. We also compare the performance of the scheme with the best performing BTA scheme that also exploits K-MPR and SIC and does not have memory limitations, showing that the relative difference in the MST’s of the two schemes diminishes with K.

Most of today's communication systems are designed to target reliable message recovery after receiving the entire encoded message (codeword). However, in many practical scenarios, the transmission process may be interrupted before receiving the complete codeword. This paper proposes a novel rateless autoencoder (AE)-based code design suitable for decoding the transmitted message before the noisy codeword is fully received. Using particular dropout strategies applied during the training process, rateless AE codes allow to trade off between decoding delay and reliability, providing a graceful improvement of the latter with each additionally received codeword symbol. The proposed rateless AEs significantly outperform the conventional AE designs for scenarios where it is desirable to trade off reliability for lower decoding delay.

Enabling real-time communication in Industrial Internet of Things (IIoT) networks is crucial to support autonomous, self-organized and re-configurable industrial automation for Industry 4.0 and the forthcoming Industry 5.0. In this paper, we consider a SIC-assisted real-time IIoT network, in which sensor nodes generate reports according to an event-generation probability that is specific for the monitored phenomena. The reports are delivered over a block-fading channel to a common Access Point (AP) in slotted ALOHA fashion, which leverages the imbalances in the received powers among the contending users and applies successive interference cancellation (SIC) to decode user packets from the collisions. We provide an extensive analytical treatment of the setup, deriving the Age of Information (AoI), throughput and deadline violation probability, when the AP has access to both the perfect as well as the imperfect channel-state information. We show that adopting SIC improves all the performance parameters with respect to the standard slotted ALOHA, as well as to an age-dependent access method. The analytical results agree with the simulation based ones, demonstrating that investing in the SIC capability at the receiver enables this simple access method to support timely and efficient information delivery in IIoT networks.

LiDAR semantic segmentation plays an important role in 3D scene understanding for autonomous driving. However, the performance based on the LiDAR equipped on a vehicle may be limited due to small perception perspective, object occlusion, and sparsity of point clouds in the distance. To address these challenges, we propose a vehicle-infrastructure cooperative semantic segmentation (VICSS) framework to enhance the vehicle-side perception capability. An infrastructure feature extraction (IFE) module is employed to extract features from the roadside LiDAR point cloud. A local feature extraction (LFE) module and a global feature extraction (GFE) module are applied to the vehicle point cloud to capture the local and global features, respectively. The features from both point clouds are fused through a feature aggregation (FA) module, which applies the cross-attention mechanism to learn beneficial information from the infrastructure features. Using a dataset generated by CARLA we show that the proposed VICSS framework achieves good performance in terms of semantic segmentation accuracy.

In this paper, we investigate the design and analysis of a novel hybrid optical wireless communication (OWC)/radio frequency (RF) solution suitable for massive machine-type communications (mMTC). We consider a two-tier network architecture where a massive collection of indoor OWC-based small cells, each consisting of low-cost Internet of Things (IoT) devices and an OWC access point, are connected to the network infrastructure via an outdoor low-power wide-area network (LP WAN). Both indoor OWC and outdoor LP WAN tiers of the mMTC system deploy the Slotted ALOHA (SA) random access protocol. For the proposed hybrid OWC/RF IoT system, we are interested in the error probability in the finite block-length regime, i.e., the probability that a short block-length data packet originating from an OWC-based IoT device is delivered at its nearest LP WAN base station. Based on the derived error probability expression, we present numerical results that indicate important insights into the design of an SA-based hybrid OWC/RF IoT system.

We consider the problem of maximum-likelihood estimation in linear models represented by factor graphs and solved via the Gaussian belief propagation algorithm. Motivated by massive Internet of Things (IoT) networks and edge computing, we set the above problem in a clustered scenario, where the factor graph is divided into clusters and assigned for processing in a distributed fashion across a number of edge computing nodes. For these scenarios, we show that an alternating Gaussian belief propagation (AGBP) algorithm that alternates between inter- and intracluster iterations, demonstrates superior performance in terms of convergence properties compared to the existing solutions in the literature. We present a comprehensive framework and introduce appropriate metrics to analyze the AGBP algorithm across a wide range of linear models characterized by symmetric and nonsymmetric, square, and rectangular matrices. We extend the analysis to the case of dynamic linear models by introducing the dynamic arrival of new data over time. Using a combination of analytical and extensive numerical results, we show the efficiency and scalability of the AGBP algorithm, making it a suitable solution for large-scale inference in massive IoT networks.

Several communication models that are of relevance in practice are asymmetric in the way they act on the transmitted "objects". Examples include channels in which the amplitudes of the transmitted pulses can only be decreased, channels in which the symbols can only be deleted, channels in which non-zero symbols can only be shifted to the right (e.g., timing channels), subspace channels in which the dimension of the transmitted vector space can only be reduced, unordered storage channels in which the cardinality of the stored (multi)set can only be reduced, etc. We introduce a formal definition of an asymmetric channel as a channel whose action induces a partial order on the set of all possible inputs, and show that this definition captures all the above examples. Such a general approach allows one to treat all these different models in a unified way, and to obtain a characterization of optimal error-detecting codes for many interesting asymmetric channels by using Sperner theory.

This paper studies properties of binary runlength-limited sequences with additional constraints on their Hamming weight and/or their number of runs of identical symbols. An algebraic and a probabilistic (entropic) characterization of the exponential growth rate of the number of such sequences, i.e., their information capacity, are obtained by using the methods of multivariate analytic combinatorics, and properties of the capacity as a function of its parameters are stated. The second-order term in the asymptotic expansion of the rate of these sequences is also given, and the typical values of the relevant quantities are derived. Several applications of the results are illustrated, including bounds on codes for weight-preserving and run-preserving channels (e.g., the run-preserving insertion-deletion channel), a sphere-packing bound for channels with sparse error patterns, and the asymptotics of constant-weight sub-block constrained sequences. In addition, the asymptotics of a closely related notion—$q$-ary sequences with fixed Manhattan weight—is briefly discussed, and an application in coding for molecular timing channels is illustrated.

We study properties of binary runlength-limited sequences with additional constraints on their weight and/or the number of runs of identical symbols they contain. An algebraic and a probabilistic (entropic) characterization of the exponential growth rate of the number of such sequences, i.e., their information capacity, are obtained, and properties of the capacity as a function of its parameters are stated. The second-order term in the asymptotic expansion of the rate of these sequences is also given, and the typical values of the relevant quantities are derived. Several applications of the results are illustrated, including bounds on codes for the run-preserving insertion-deletion channel, a sphere-packing bound for channels with sparse error patterns, and the asymptotics of constant-weight sub-block constrained sequences. A full version of this paper, containing the proofs of all the statements, as well as some additional material, is accessible at: https://arxiv.org/abs/2105.04617.

Wi-Fi systems based on the family of IEEE 802.11 standards that operate in unlicenced bands are the most popular wireless interfaces that use Listen Before Talk (LBT) methodology for channel access. Distinctive feature of majority of LBT-based systems is that the transmitters use preambles that precede the data to allow the receivers to acquire initial signal detection and synchronization. The first digital processing step at the receiver applied over the incoming discrete-time complex-baseband samples after analog-to-digital conversion is the packet detection step, i.e., the detection of the initial samples of each of the frames arriving within the incoming stream. Since the preambles usually contain repetitions of training symbols with good correlation properties, conventional digital receivers apply correlation-based methods for packet detection. Following the recent interest in data-based deep learning (DL) methods for physical layer signal processing, in this paper, we challenge the conventional methods with DL-based approach for Wi-Fi packet detection. Using one-dimensional Convolutional Neural Networks (1D-CNN), we present a detailed complexity vs performance analysis and comparison between conventional and DL-based Wi-Fi packet detection approaches.

Smart Grid is expected to support a variety of services for energy prosumers - entities that are able both to produce and consume energy. Enabling cooperation among such prosumers in the form of energy trading may be highly beneficial for all actors in Smart Grid. However, in order to provide energy trading capabilities, a communication infrastructure that will be able to offer energy trading service to a massive number of energy traders dispersed over a large geographic area is needed. In this paper, we argue that a 4G/5G cellular network that offers cellular Internet of Things (IoT) services and provides mobile edge computing (MEC) capabilities is ideally suited for future widespread energy trading. We discuss architecture of such a system, identify and present analytic models for each of its parts that also account for stochastic aspects, and propose an overall energy trading system model. By doing so, we obtain a novel optimization problem formulation of the energy trading, that is capable of handling uncertainties in price changes. Our results and discussion provides initial insights towards the design of energy trading services via emerging IoT/MEC-enabled mobile cellular networks.

We consider the problem of detecting a random walk on a graph, based on observations of the graph nodes. When visited by the walk, each node of the graph observes a signal of elevated mean, which we assume can be different across different nodes. Outside of the path of the walk, and also in its absence, nodes measure only noise. Assuming the Neyman-Pearson setting, our goal then is to characterize detection performance by computing the error exponent for the probability of a miss, under a constraint on the probability of false alarm. Since the exact computation of the error exponent is known to be difficult, equivalent to the computation of the Lyapunov exponent, we approximate its value by finding a tractable lower bound. The bound reveals an interesting detectability condition: the walk is detectable whenever the entropy of the walk is smaller than one half of the expected signal-to-noise ratio. We derive the bound by extending the notion of Markov types to Gauss-Markov types. These are sequences of the state-observation pairs with a given number of node-to-node transition counts and the same average signal values across nodes, computed from the measurements made during the times the random walk visited each node's respective location. The lower bound has an intuitive interpretation: among all Gauss-Markov types that are asymptotically feasible in the absence of the walk, the bound finds the most typical one under the presence of the walk. Finally, we show by a sequence of judicious problem reformulations that computing the bound reduces to solving a convex optimization problem, which is a result of in its interest own right.

Machine-type communications and large-scale information processing architectures are among key (r)evolutionary enhancements of emerging fifth-generation (5G) mobile cellular networks. Massive data acquisition and processing will make 5G network an ideal platform for large-scale system monitoring and control with applications in future smart infrastructures. In this work, we investigate a capability of such a 5G network architecture to provide the state estimate of an underlying linear system from the input obtained via large-scale deployment of measurement devices. Assuming that the measurements are communicated via densely deployed cloud radio access network (C-RAN), we formulate and solve the problem of estimating the system state from the set of signals collected at C-RAN base stations. Our solution, based on the Gaussian Belief-Propagation (GBP) framework, allows for large-scale and distributed deployment within the emerging 5G information processing architectures. The presented numerical study demonstrates the accuracy, convergence behavior and scalability of the proposed GBP-based solution to the large-scale state estimation problem.

In a previous work [1] we applied the idea of HEVC intra coding to compression of 32 b/p seismic images. Results are significantly better than those from a licensed commercial wavelet-based codec that is currently used at Shell for seismic image compression, which performs on par with JPEGXR. Building upon [1], this paper exploits HEVC-based inter predictive coding for 3D 32 b/p seismic data. We propose a new model for the Lagrange multiplier in R-D optimization to accommodate 32 b/p bit-depth and extended quantization parameter range. We also focus on reducing the complexity of motion estimation to meet application needs. Experiments with our new codec show a 95% complexity reduction of encoding time at 10:1 compression ratio with only 1 dB loss on average PSNR. The compression performance and subjective quality of our codec received high evaluation marks from Shell geologists.

Inspired by the high performance of High Efficiency Video Coding (HEVC), this paper reports our work on applying the ideas of HEVC intra coding to compression of high-depth images such as 32 bits per pixel (b/p) seismic data. Compared to a licensed commercial wavelet-based codec that is currently used for seismic image compression, which performs on par with JPEG-XR, our new image codec significantly improves the PSNR vs. compression ratio performance. The codec's subject performance is rated by geologist as highly satisfactory.

Delivery of multicast video services over fourth generation (4G) networks such as 3GPP Long Term Evolution-Advanced (LTE-A) is gaining momentum. In this paper, we address the issue of efficiently multicasting layered video services by defining a novel resource allocation framework that aims to maximize the service coverage whilst keeping the radio resource footprint low. A key point in the proposed system mode is that the reliability of multicast video services is ensured by means of an Unequal Error Protection implementation of the Network Coding (UEP-NC) scheme. In addition, both the communication parameters and the UEP-NC scheme are jointly optimized by the proposed resource allocation framework. Numerical results show that the proposed allocation framework can significantly increase the service coverage when compared to a conventional Multi-rate Transmission (MrT) strategy.

We introduce a neighborhood-based data access model for distributed coded storage allocation. Storage nodes are connected in a generic network and data is accessed locally: a user accesses a randomly chosen storage node, which subsequently queries its neighborhood to recover the data object. We aim at finding an optimal allocation that minimizes the overall storage budget while ensuring recovery with probability one. We show that the problem reduces to finding the fractional dominating set of the underlying network. Furthermore, we develop a fully distributed algorithm where each storage node communicates only with its neighborhood in order to find its optimal storage allocation. The proposed algorithm is based upon the recently proposed proximal center method-an efficient dual decomposition based on accelerated dual gradient method. We show that our algorithm achieves a (1 + ε)-approximation ratio in O(dmax3/2/ε) iterations and per-node communications, where dmaxis the maximal degree across nodes. Simulations demonstrate the effectiveness of the algorithm.

The explosive growth of content-on-the-move, such as video streaming to mobile devices, has propelled research on multimedia broadcast and multicast schemes. Multirate transmission strategies have been proposed as a means of delivering layered services to users experiencing different downlink channel conditions. In this paper, we consider point-to-multipoint layered service delivery across a generic cellular system and improve it by applying different random linear network coding approaches. We derive packet error probability expressions and use them as performance metrics in the formulation of resource-allocation frameworks. The aim of these frameworks is both the optimization of the transmission scheme and the minimization of the number of broadcast packets on each downlink channel, while offering service guarantees to a predetermined fraction of users. As a case of study, our proposed frameworks are then adapted to the LTE-A standard and the eMBMS technology. We focus on the delivery of a video service based on the H.264/SVC standard and demonstrate the advantages of layered network coding over multirate transmission. Furthermore, we establish that the choice of both the network coding technique and the resource-allocation method play a critical role on the network footprint, as well as the quality of each received video layer.

We introduce a framework to study slotted Aloha with cooperative base stations. Assuming a geographic-proximity communication model, we propose several decoding algorithms with different degrees of base stations' cooperation (noncooperative, spatial, temporal, and spatio-temporal). With spatial cooperation, neighboring base stations inform each other whenever they collect a user within their coverage overlap; temporal cooperation corresponds to (temporal) successive interference cancellation done locally at each station. We analyze the four decoding algorithms and establish several fundamental results. With all algorithms, the peak throughput (average number of decoded users per slot, across all base stations) increases linearly with the number of base stations. Further, temporal and spatio-temporal cooperations exhibit a threshold behavior with respect to the normalized load (number of users per station, per slot). There exists a positive load G*, such that, below G*, the decoding probability is asymptotically maximal possible, equal the probability that a user is heard by at least one base station; with non-cooperative decoding and spatial cooperation, we show that G* is zero. Finally, with spatio-temporal cooperation, we optimize the degree distribution according to which users transmit their packet replicas; the optimum is in general very different from the corresponding optimal distribution of the single-base station system.

We consider framed slotted Aloha where m base stations cooperate to decode messages from n users. Users and base stations are placed uniformly at random over an area. At each frame, each user sends multiple replicas of its packet according to a prescribed distribution, and it is heard by all base stations within the communication radius r. Base stations employ a decoding algorithm that utilizes the successive interference cancellation mechanism, both in space-across neighboring base stations, and in time-across different slots, locally at each base station. We show that there exists a threshold on the normalized load G = n/(τm), where τ is the number of slots per frame, below which decoding probability converges asymptotically (as n, m, τ → ∞, r → 0) to the maximal possible value-the probability that a user is heard by at least one base station, and we find a lower bound on the threshold. Further, we give a heuristic evaluation of the decoding probability based on the and-or-tree analysis. Finally, we show that the peak throughput increases linearly in the number of base stations.

In this paper we investigate the design of DASH stream-switching solution suitable for mobile cellular video streaming consumers. Our goal is to design a solution that reduces the amount of downloaded content while adhering to user preferences regarding received video quality. We firstly present a graphical (trellis-like) representation of a stream-switching process, and then investigate different approaches to search for an optimal path through a “stream-switching graph” using suitably defined utility function. We target our search towards the path that will: i) respect underlying wireless-link rate limitations, ii) maintain segment selection that is efficient in terms of the quality vs the amount of the downloaded data, and iii) reduce disturbing quality variations. The simulation results demonstrate the performance of the proposed QoE-aware Rate-Conservative (QaRC) DASH as compared to the recently proposed baseline stream-switching solution.

Random Network Coding (RNC) has recently been investigated as a promising solution for reliable multimedia delivery over wireless networks. RNC possess the potential for flexible and adaptive matching of packet-level error resilience to both video content importance and variable wireless channel conditions. As the demand for massive multimedia delivery over fourth generation wireless cellular standards such as Long-Term Evolution (LTE)/LTE-Advanced (LTE-A) increases, novel video-aware transmission techniques are needed. In this paper, we investigate RNC as one such promising technique, building upon our recent work on RNC integration within the LTE/LTE-A Radio Access Network at the Multiple Access Control (MAC) layer (MAC-RNC). The paper argues that the proposed MAC-RNC solution provides fundamentally new set of opportunities for dynamic collaborative transmission, content awareness, resource allocation and unequal error protection (UEP) necessary for efficient wireless multimedia delivery in LTE/LTE-A.

In this paper, we propose a novel advanced multirate design for evolved Multimedia Multicast/Broadcast Service (eMBMS) in fourth generation (4G) Long-Term Evolution (LTE)/LTE-Advanced (LTE-A) networks. The proposed design provides: i) reliability, based on random network coded (RNC) transmission, and ii) efficiency, obtained by optimized rate allocation across multi-rate RNC streams. The paper provides an in-depth description of the system realization and demonstrates the feasibility of the proposed eMBMS design using both analytical and simulation results. The system performance is compared with popular multi-rate multicast approaches in a realistic simulated LTE/LTE-A environment.

Mobile video services are becoming a dominant traffic category in emerging fourth generation (4G) cellular networks such as the 3GPP Long-Term Evolution (LTE) and LTE-Advanced (LTE-A). In particular, mobile video broadcasting services such as 3GPP evolved Multimedia Multicast/Broadcast Service (eMBMS) will require considerable resources for highquality video service delivery with high coverage probability. Faced with the challenge of bandwidth and energy efficient eMBMS provisioning over LTE/LTE-A, in this paper, we develop simple analytical tools for evaluation of average service data rates, bandwidth and energy-consumption requirements in different eMBMS LTE/LTE-A service configurations. Moreover, we introduce and evaluate novel bandwidth and energy performance measures defined per unit of service with the goal of comparing the service delivery efficiency of different LTE/LTE-A configurations. The results in the paper focus on eMBMS provisioning over two macro-cellular LTE/LTE-A configurations in 3GPP: i) a single frequency network eMBMS, and ii) a single-cell eMBMS.

The error free communication of video data over multi-hop wireless networks is a challenging research problem. Multiple description coding has been proposed as a possible solution to leverage path diversity for error robustness. Forward error correction is an additional protection that can be provided to each description. Random linear codes have had renewed interest fostered by the multi-hop and multi-interface radio receivers. In this study, the descriptions are created using the encoding features of slicing and data partitioning for H.264/AVC video. The unequally protected video is protected with Expanding window-Random linear codes against channel errors. Fading channel error model is used to simulate real-world wireless channels. We also propose an adaptive scheme for video transmission over multiple paths. Such scheme may adapt to the varying channel conditions as is frequently the case in wireless transmission. The results show that the proposed scheme can be used for emerging wireless standards.

In this paper, we propose integration of random network coding (R-NC) sublayer at the Radio Link Control/Multiple Access Control (RLC/MAC) layer boundary within the 3GPP Long Term Evolution (LTE) Radio Access Network (RAN) protocol stack. R-NC is introduced as a simple alternative to the MAC/Physical Layer (PHY) Hybrid Automatic Repeat reQuest (HARQ) protocol for reliable RLC protocol data unit (PDU) transmission over the radio interface between the base station (eNB) and the mobile user equipment (UE). To evaluate and compare the performance of the R-NC-based solution against the standardized HARQ-based one, we develop a novel link-level simulator which combines packet-level Finite-State Markov Chain (FSMC) channel models and existing LTE PHY layer simulators. Using this simulator, the R-NC and the HARQ-based alternatives are compared with respect to continuous transmission of RLC PDUs between the eNB and the UE over a direct radio link or via relay-node (RN) assisted multi-hop connection. The focus of this technical study is on the possible throughput, energy, delay and implementation complexity reduction benefits of the proposed R-NC-based solution over the HARQ-based protocol.

Random Network Coding (RNC) has recently been investigated as a promising solution for reliable multimedia delivery over wireless networks. Based on Random Linear Codes (RLC) and their systematic, sparse and Unequal Error Protection (UEP) extensions, RNC possess the potential for flexible and adaptive matching of packet-level error resilience to both video content importance and variable wireless channel conditions. As the demand for massive multimedia delivery over 4G wireless cellular standards such as LTE/LTE-A increases, novel video-aware transmission techniques are needed. In this paper, we investigate RNC as one such promising technique. In contrast to the normal use of RNC as an application layer technique (AL-RNC), this paper focuses on RNC integration within the MAC layer (MAC-RNC) of the LTE/LTE-A Radio Access Network (RAN). The paper argues that the proposed MAC-RNC solution could successfully replace the current MAC layer HARQ error resilience mechanism while providing new opportunities for content awareness, resource allocation and UEP which are fundamental for efficient wireless multimedia delivery.

In this paper, we provide the performance analysis of unequal error protection (UEP) random linear coding (RLC) strategies designed for transmission of source messages containing packets of different importance over lossy packet erasure links. By introducing the probabilistic encoding framework, we first derive the general performance limits for the packet-level UEP coding strategies that encode the packets of each importance class of the source message independently (non-overlapping windowing strategy) or jointly (expanding windowing strategy). Then, we demonstrate that the general performance limits of both strategies are achievable by the probabilistic encoding over non-overlapping and expanding windows based on RLC and the Gaussian Elimination (GE) decoding. Throughout the paper, we present a number of examples that investigate the performance and optimization of code design parameters of the expanding window RLC strategy and compare it with the non-overlapping RLC strategy selected as a reference.

Recent trends witness the shift of the 3GPP Long Term Evolution - Advanced (LTE-A) radio access network (RAN) architecture from a traditional macro-cellular layout towards smaller base stations moving closer to end users. The evolved LTE-A RAN offers rich environment for multi-point and multi-hop cooperation and coordination resulting in increased capacity and more predictable channel conditions between heterogeneous base stations and end users. While these opportunities are recently well investigated at the physical layer through various cooperative multi-point (CoMP) schemes, upper layer protocols preserve the design proposed for macro-cellular single-hop data delivery. In this paper, we address this issue by proposing and investigating in detail a cooperative RAN-wide MAC layer protocol based on random network coding (RNC) that is designed specifically for reliable and flexible data delivery over the evolved LTE-A RAN. The proposed RNC-based MAC protocol (MAC-RNC) is evaluated and compared with the existing HARQ-based (MAC-HARQ) protocol in various LTE-A RAN layouts using a customized packet-based link-level simulator based on Finite-State Markov Chain (FSMC) channel models. Our results show that the MAC-RNC protocol introduces simplicity and flexibility required for future LTE-A RANs, while preserving or improving the performance of the MAC-HARQ protocol in traditional single-point single-hop macro-cellular scenarios.

Application Layer Forward Error Correction (AL)-FEC is increasingly being employed in the emerging wireless multimedia applications, where the multimedia data is sent along with repair data that can be used at the receiver to recover any losses. Raptor codes and Random Linear Codes (RLC) have emerged as promising rateless coding solutions. The DVB-H standard has adopted Raptor codes for IP datacasting, whereas for real-time communications, Reed-Solomon (RS) codes are used as link-layer FEC. This study compares the performance of Raptor codes and RLC for sending the H.264/AVC compressed video traffic over the DVB-H network. The simulations are performed using error traces depicting physical-layer Transport Stream (TS) packet losses in DVB-H. The study highlights the possibility of using RLC for the AL-FEC by selectively configuring codes for optimum performance.

Rateless codes can be advantageously used to provide Application Layer Forward Error Correction (AL-FEC) with a distinct advantage that an infinite number of packets can be generated on the fly from the source packets. For heterogeneous users and/or variable channel conditions, significant adaptation features and improvement in the video quality can be achieved by partitioning the video data into different priority classes. Such partitioned data can be unequally protected using appropriate FEC scheme based on its contribution to video reconstruction. Expanding Window Random Linear Codes (EW RLC) are a simple unequal error protection fountain coding scheme which can adapt to the prioritized data transmission. In this paper, EW RLC are proposed for broadcasting the H.264/Advanced video coding partitioned with the data partitioning feature. The results show viability of the EW RLC for multimedia broadcast applications to suit different data rates and channel conditions.

Application layer forward error correction is becoming a popular addition to protocols for real-time video delivery over IP-based wireless networks. Since each part of video data is not equally important for video reconstruction, it is beneficial to divide video data based on its importance. Such partitioned data could then be provided with different degree of protection, with the important data having more protection against channel erasures. Data partitioning (DP) is one such low-cost feature in H.264/AVC enabling partitioning of video data based on its importance. In this paper, we propose an Unequal error protection (UEP) scheme to protect the DP H.264/AVC coded video data with Raptor and Random linear codes (RLC). The simulations have been performed using error traces which depict physical-layer Transport Stream (TS) packet losses in DVBH. The results highlight that for broadcasting applications with varying channel conditions, better results can be obtained with dynamic probability of selection of different importance layers.

In this paper we propose an end-to-end solution for urban infrastructure-to-vehicle traffic data delivery based on a class of unequal error protection (UEP) rateless codes called expanding window fountain (EWF) codes. The proposed solution relies on attractive features that rateless codes introduce to networks with unpredictable dynamics: the universal capacity approaching property which is well-matched to time-varying behavior of wireless links, and the innovative nature of each encoded packet which makes both time-consuming retransmission and content-reconciliation mechanisms unnecessary. Furthermore, usage of EWF codes allows separation of delivered data in importance classes with different error protection and recovery time guarantees, enabling mobile users to retrieve more important information more reliably and in shorter time span, thus making the proposed solution suitable for time-critical services. The addressed urban communication scenario consists of large number of sensors that sample and relay traffic flow information to network of Access Points (APs). APs use the existing underlying communication infrastructure, such as metropolitan area networks (MANs), to exchange traffic flow data, encode it using EWF coding principles, and finally disseminate it to roaming vehicles that join the network service in an ad-hoc manner in order to retrieve information regarding the surrounding environment. The proposed approach is suitable for real-time applications, such as frequent periodic reporting of urban traffic conditions, that could be used by on-board computers to provide improved navigation for end-users.

In this paper, an approach minimizing delivery latency, while keeping transmitted power below a certain threshold, is proposed to provide reliable real time wireless communications when applying network coding principle in order to point out additional advantage in terms of capacity. An analytical tool is proposed to derive the performance of optimal power adaptation scheme NC scheme aiming at minimizing the number of coded packets transmitted on a link-by-link basis, thereby lowering the end-to-end delay and overall power consumption.

Application-layer forward error correcting (AL-FEC) codes, providing error protection across blocks of packetized data, are becoming increasingly important in emerging networking applications. In this paper, we investigate AL-FEC solutions based on rate- compatible low-density parity-check (RC LDPC) codes, as they may offer excellent performance with low system complexity. Recent studies on RC LDPC codes are focused on physical layer incremental redundancy hybrid automatic-repeat-request (IR-HARQ) systems and typical wireless channel models. The focus of our study is RC LDPC code design as an AL-FEC solution in packet-level IR-HARQ systems assuming erasure channels between the source and the destination(s). We propose novel design methods for RC LDPC codes over erasure channels motivated by the dependance of finite-length LDPC code performance on the size of the smallest stopping set of its code graph. Simulation experiments demonstrate that the proposed RC LDPC design significantly outperforms the state-of-the-art RC LDPC codes in the error-floor region, and in particular, in the domain of poor channel conditions.

H.264 Scalable Video Coding (SVC) is an extension to the Advanced Video Coding (AVC) H.264 standard which provides efficient scalability functionalities on top of the high coding efficiency of H.264/AVC. SVC allows for temporal, spatial, and quality scalability of the output video stream, encoding the video information into an H.264/AVC base layer and a series of enhancement layers which incrementally improve the quality, increase screen resolution and/or frame rate. SVC is particularly suited for mobile TV reception, since the received video quality is adaptable to variable reception conditions and heterogeneous receiver capabilities. However, mobile TV Digital Video Broadcasting (DVB) standards such as DVB-H and DVB-SH were designed prior to the introduction of SVC, and therefore the underlying transmission protocols are not optimized for scalable video delivery. In this overview paper, we review recently proposed solutions for SVC stream adaptation on the underlying DVB-H/SH protocols, and point out novel technical solutions that are currently under consideration for the next generation mobile broadcasting standard DVB-NGH.

In this paper, we investigate the design of decentralized encoding procedure for distributed random linear coding (RLC) in resource-constrained wireless networks. We propose a novel distributed RLC scheme called “Fireworks”, analyze its performance and support it by simulation results. The presented results demonstrate design flexibility of the proposed scheme, where the design choices influence the trade-off between the coding efficiency and encoding communication costs.

This paper presents a data gathering scheme designed for an emergency monitoring Wireless Sensor Network (WSN), which employs distributed rateless encoding and dispersion of the encoded data toward the perimeter of the monitored area, where it is collected by a mobile collector (MC). Rate-less codes are low encoding/decoding complexity codes, suitable for resource constrained devices, such as sensor nodes, while the proposed distributed encoding and dispersion, performed using only local knowledge, are adapted to WSNs where the frequent changes in underlying topology and connectivity are expected. The proposed scheme allows for data persistence and efficient data gathering in the scenarios where decentralized WSNs are deployed in inaccessible locations with no infrastructure support, where nodes are prone to failures due to harsh operating conditions and where the access of the MC to the monitoring area is severely limited.

This paper focuses on recent research on unequal error protection random linear coding (UEP RLC) for applications in network coded (NC) multimedia communications. We define a class of UEP RLC called expanding window random linear coding (EW-RLC) and provide exact decoding probability analysis for different importance classes of the source data assuming the Gaussian Elimination (GE) decoder applied at the receiver. Using this analysis, we provide a detailed investigation of the EW-RLC design for the distortion optimized scalable H.264/SVC coded video transmission over packet networks with packet erasures over a range of heterogeneous receivers with varying receiver reception overhead capabilities.

In this paper, we propose a novel approach for data gathering in wireless sensor networks (WSN) based on distributed rateless codes. Rateless codes are an efficient, lowcomplexity solution for coded data transmission over channels with packet erasures, which motivates their application in distributed network scenarios such as WSN. Recently proposed distributed rateless coding techniques for WSN are node-centric, i.e., collecting sufficient number of different sensor data packets and performing rateless encoding is the task of sensor nodes. In the proposed packet-centric approach, this task is assigned to encoded packets called rateless packets. While randomly moving through the network, rateless packets collect and encode into their content required number of uniformly sampled sensor data packets, completing their paths in randomly selected network nodes. Using this approach, any degree distribution of rateless codes can be exactly obtained. The problem of uniform combining of sensor data into rateless packets, and uniform dispersion throughout the network is addressed. The efficiency of the proposed scheme and comparison with the performance of centralized rateless codes are demonstrated by simulation results.

This paper deals with the evaluation of the end-to-end packet delay for a lossy network where network coding is adopted to achieve better performance. Differently from previous published papers on this subject, connection oriented services are assumed, this means that packets have to be in-order received and wait at the receiver buffer till all the previous ones have been correctly delivered. The focus is on a network model where two source nodes broadcast packets to a group of two sink nodes over lossy wireless channels. Three different alternatives have been considered in order to assure a reliable data multicasting, namely: a classical random linear network coding scheme, a linear network coding combined with a basic ARQ or, alternatively, with a soft combined ARQ scheme. Performance comparisons provided by means of analytical and numerical results clearly highlight that the better solution is to adopt the latter alternative.

In this paper, we propose a simple and efficient two-stage list synchronizer for frame synchronization of low-density parity-check (LDPC) coded data transmitted over the additive white Gaussian noise (AWGN) channel. The proposed method uses both synchronization sequence and code constraints for frame synchronization. In the first stage, a list of the most likely frame starting positions is made using synchro-sequence, while in the second stage the code constraints are used to select the most likely position from the list. We analytically relate the synchro- sequence length, the frame length and the list length in order to keep the synchronization frame error rate (FER) lower than the error-correcting FER of the LDPC code, and we investigate the trade-off between the sequence length and the list length. We demonstrate that the proposed scheme outperforms both the frame synchronization methods based on synchro-sequence only and the recently proposed blind synchronization methods based on LDPC code constraints, the former in terms of the synchronization FER for the same sequence lengths and the latter in terms of complexity.

In this paper, we address the problem of distributed Raptor code design over information packets located across the network nodes. We propose a novel approach to this problem that consists of generating, encoding and dispersing Raptor packets across the network. Unlike recent node-centric proposals, where network nodes are responsible for collecting information packets and performing Raptor encoding, in the proposed packet-centric approach this task is assigned to Raptor packets. In a two-step encoding procedure that corresponds to precoding and LT-coding step of standard Raptor encoding, Raptor packets randomly traverse the network, collect and encode sufficient number of information packets following exactly a given degree distribution, and finish their paths in a random network node. The efficiency of the distributed Raptor coding scheme is confirmed by simulation results, where their performance is demonstrated to approach closely the performance of standard (centralized) Raptor codes.

In this paper, we propose a novel approach for data gathering in wireless sensor networks (WSN) based on distributed rateless codes. Rateless codes are an efficient, low-complexity solution for coded data transmission over erasure channels, which motivates their application in distributed network scenarios such as WSN. Recently proposed distributed rateless coding techniques for WSN are node-centric, i.e., the task of collecting sufficient number of different sensor data packets and performing rateless encoding is responsibility of sensor nodes. In the proposed packet- centric approach, this task is assigned to encoded data packets called rateless packets. While randomly moving through the network, rateless packets collect required number of uniformly sampled sensor data packets, finishing their paths in randomly selected network nodes. Using this approach, any degree distribution of rateless codes can be exactly obtained. The problem of uniform combining of sensor data into rateless packets, and uniform dispersion throughout the network is addressed. The efficiency of the proposed scheme and its comparison with the performance of centralized rateless codes are demonstrated by simulation results.

A novel approach to provide unequal error protection (UEP) using rateless codes over erasure channels, named Expanding Window Fountain (EWF) codes, is developed and discussed. EWF codes use a windowing technique rather than a weighted (non-uniform) selection of input symbols to achieve UEP property. The windowing approach introduces additional parameters in the UEP rateless code design, making it more general and flexible than the weighted approach. Furthermore, the windowing approach provides better performance of UEP scheme, which is confirmed both theoretically and experimentally.

The construction of finite-length irregular LDPC codes with low error floors is currently an attractive research problem. In particular, for the binary erasure channel (BEC), the problem is to find the elements of selected irregular LDPC code ensembles with the size of their minimum stopping set being maximized. Due to the lack of analytical solutions to this problem, a simple but powerful heuristic design algorithm, the approximate cycle extrinsic message degree (ACE) constrained design algorithm, has recently been proposed. Building upon the ACE metric associated with a cycle in a code graph, we introduce the ACE spectrum of LDPC codes as a useful tool for evaluation of codes from selected irregular LDPC code ensembles. Using the ACE spectrum, we generalize the ACE constrained design algorithm, making it more flexible and efficient. We justify the ACE spectrum approach through examples and simulation results.

Fountain codes were introduced as an efficient and universal forward error correction (FEC) solution for data multicast over lossy packet networks. They have recently been proposed for large scale multimedia content delivery in practical multimedia distribution systems. However, standard fountain codes, such as LT or Raptor codes, are not designed to meet unequal error protection (UEP) requirements typical in real-time scalable video multicast applications. In this paper, we propose recently introduced UEP expanding window fountain (EWF) codes as a flexible and efficient solution for real-time scalable video multicast. We demonstrate that the design flexibility and UEP performance make EWF codes ideally suited for this scenario, i.e., EWF codes offer a number of design parameters to be “tuned” at the server side to meet the different reception criteria of heterogeneous receivers. The performance analysis using both analytical results and simulation experiments of H.264 scalable video coding (SVC) multicast to heterogeneous receiver classes confirms the flexibility and efficiency of the proposed EWF-based FEC solution.

Digital Fountain (DF) codes have recently been suggested as an efficient forward error correction (FEC) solution for video multicast to heterogeneous receiver classes over lossy packet networks. However, to adapt DF codes to low-delay constraints and varying importance of scalable multimedia content, unequal error protection (UEP) DF schemes are needed. Thus, in this paper, Expanding Window Fountain (EWF) codes are proposed as a FEC solution for scalable video multicast. We demonstrate that the design flexibility and UEP performancemake EWF codes ideally suited for this scenario, i.e., EWF codes offer a number of design parameters to be “tuned” at the server side to meet the different reception conditions of heterogeneous receivers. Performance analysis of H.264 Scalable Video Coding (SVC) multicast to heterogeneous receiver classes confirms the flexibility and efficiency of the proposed EWF-based FEC solution.

The construction of good, finite-length, LDPC codes is currently an attractive research area. Reducing attention to the binary erasure channel (BEC), this problem translates into the problem of finding elements of selected (irregular) LDPC code ensemble with the size of the minimal stopping set being maximized. Faced with the lack of analytical solution to this problem, simple but powerful heuristic design algorithm, an ACE constrained design algorithm, was recently introduced. Building upon the ACE metric associated with every cycle in the code graph, we introduce the ACE spectrum of LDPC code as a useful measure of success in selection of the LDPC code from selected ensemble. Using ACE spectrum, we farther generalize ACE constrained design, making it more flexible and efficient.

An analytical approach to a search process is a mathematical prerequisite for digital synchronization acquisition analysis and optimization. A search is performed for an arbitrary set of sequences within random but not equiprobable L-ary data. This paper derives in detail an expression for probability distribution function, from which other statistical parameters - expected value and variance - can be obtained. The probabilistic nature of (cross-) bifix indicators is shown and application examples are outlined, ranging beyond the usual telecommunication field

In order to understand the performance of a code under maximum-likelihood (ML) decoding, it is crucial to know the minimal codewords. In the context of linear programming (LP) decoding, it turns out to be necessary to know the minimal pseudo-codewords. This paper studies the minimal codewords and minimal pseudo-codewords of some families of codes derived from projective and Euclidean planes. Although our numerical results are only for codes of very modest length, they suggest that these code families exhibit an interesting property. Namely, all minimal pseudo-codewords that are not multiples of a minimal codeword have an AWGNC pseudo-weight that is strictly larger than the minimum Hamming weight of the code. This observation has positive consequences not only for LP decoding but also for iterative decoding