High-rate short-length error correction codes are crucial for ultralow-latency communication (ULLC) applications prevalent in next-generation communication systems. Recent promising developments in this regard include the code-agnostic family of guessing random additive noise decoding (GRAND) decoders. However, they are not directly applicable to multiuser communication scenarios (e.g., in 5G and 6G) characterized by nonorthogonal multiple access (NOMA). To address this gap, we propose a novel multiuser ordered symbol switch (MOSS)-GRAND decoder operating on symbols (instead of bits) to jointly decode the information of a pair of users accessing the channel using NOMA. We also propose a NOMA-successive interference cancellation (SIC) decoder as a conventional straightforward adaptation of GRAND-based decoders to the NOMA setup. Comparing MOSS-GRAND with NOMA-SIC, we show that the former is amenable to higher throughput due to parallelization and is therefore preferred over the latter for the target ULLC applications. The algorithm is translated into a low-latency hardware architecture, where the distance between the received message and all candidate codewords is calculated in parallel to optimize performance. The experimental results demonstrate that the MOSS-GRAND decoder provides improvements between$5.70\times $and$117.51\times $in throughput, with area reductions between$1.88\times $and$789.79\times $compared to traditional GRAND-based decoders adapted for NOMA. This makes MOSS-GRAND an ideal candidate for ULLC applications on resource-constrained platforms, such as the Internet of Things (IoT) nodes.

This paper considers a radio frequency (RF) energy-harvesting (EH) node with a finite battery capacity and an RF backscatter symbiotic radio (SR) in the vicinity that modulates the EH node's RF signal with its own message. With both messages intended for the same receiver, we consider a successive interference cancellation strategy with an automatic repeat request protocol under Rayleigh fading. We derive closed-form expressions for the probability of outage as well as the latency per successfully transmitted bit experienced by the system as a function of (a) the battery capacity of the EH node and (b) the reflection coefficient of the SR. We prove that although the latency is not a convex function of the battery capacity, it exhibits a unique minima which can easily be found through an iterative search. We also analyze the latency as a function of the reflection coefficient and derive an upper bound for its optima. Using an in-depth examination of the trends of the objective function, we propose an iterative algorithm for determining the latency's global minima with respect to the two variables of interest.

This paper proposes a multi-spectral random forest classifier with suitable feature selection and masking for tree cover estimation in urban areas. The key feature of the proposed classifier is filtering out the built-up region using spectral indices followed by random forest classification on the remaining mask with carefully selected features. Using Sentinel-2 satellite imagery, we evaluate the performance of the proposed technique on a specified area (approximately 82 acres) of Lahore University of Management Sciences (LUMS) and demonstrate that our method outperforms a conventional random forest classifier as well as state-of-the-art methods such as European Space Agency (ESA) WorldCover 10m 2020 product as well as a DeepLabv3 deep learning architecture.

A reliable yet inexpensive tool for the estimation of flood water spread is conducive for efficient disaster management. The application of optical and SAR imagery in tandem provides a means of extended availability and enhanced reliability of flood mapping. We propose a methodology to merge these two types of imagery into a common data space and demonstrate its use in the identification of affected populations and infrastructure for the 2022 floods in Pakistan. The merging of optical and SAR data provides us with improved observations in cloud-prone regions; that is then used to gain additional insights into flood mapping applications. The use of open source datasets from WorldPop1and OSM2for population and roads respectively makes the exercise globally replicable. The integration of flood maps with spatial data on population and infrastructure facilitates informed policy design. We have shown that within the top five flood-affected districts in Sindh province, Pakistan, the affected population accounts for 31%, while the length of affected roads measures 1410.25 km out of a total of 7537.96 km.

Any policy-level decision-making procedure and academic research involving the optimum use of resources for development and planning initiatives depends on accurate population density statistics. The current cutting-edge datasets offered by WorldPop and Meta do not succeed in achieving this aim for developing nations like Pakistan; the inputs to their algorithms provide flawed estimates that fail to capture the spatial and land-use dynamics. In order to precisely estimate population counts at a resolution of 30 meters by 30 meters, we use an accurate built settlement mask obtained using deep segmentation networks and satellite imagery. The Points of Interest (POI) data is also used to exclude non-residential areas.

Extremist content on social media platforms has led to tragic acts of violence. A context-aware extremist content framework is the need of the hour to ensure the detection and mitigation of this type of content. This work provides an outline of our recently launched initiative to develop a context-aware framework. We also present the rudimentary results of the lynching of “Priyantha Kumara Diyawadana” to illustrate the impact of online extremist propaganda on social media platforms. Our results indicate that nearly 25 % of the total population included in the gathered data have a negative sentiment toward the lynching of Priyantha Kumara Diyawadana, demonstrating how extreme hate-mongering ex-tremist narratives are affecting social media users.

Clouds, together with their shadows, usually occlude ground-cover features in optical remote sensing images. This hinders the utilization of these images for a range of applications such as earth observation, land-cover classification and urban planning. In this work, we propose a deep unfolded and prior-aided robust principal component analysis (DUPA-RPCA) network for removing clouds and recovering ground-cover information in multi-temporal satellite images. We model these cloud-contaminated images as a sum of low rank and sparse elements and then unfold an iterative RPCA algorithm that has been designed for reweighted$\ell _{1}$minimization. As a result, the activation function in DUPA-RPCA adapts for every input at each layer of the network. Our experimental results on both Landsat and Sentinel images indicate that our method gives better accuracy and efficiency when compared with existing state of the art methods.

In this paper, we present our preliminary results towards a novel framework for non intrusive monitoring of home appliances using wireless signals such as those of Wi-Fi. The framework relies on the fact that the state - ON or OFF - of some home appliances causes detectable changes in the characteristics of the wireless channel. For proof of concept demonstrations, we select a number of home appliances whose operation is either characterized by moving parts or by generation of radio frequency interference (RFI) - properties which we show can be detected by analysing reflected radio frequency energy in the wireless channel. Using software-defined radios with a WiFi-like physical layer, we develop a prototype system for the detection of the following home appliances: (a) Ceiling Fan, (b) Pedestal Fan, (c) Washing Machine, and (d) Microwave Oven. We identify relevant features on the measured Channel State Information (CSI) that depict the environmental changes due to state of different home appliances. These features are used by a subsequent machine learning stage to classify the state and type of the home appliance. By achieving an average validation accuracy of 90.11% using a Support Vector Machine (SVM) classifier, our results indicate that the proposed framework has the potential for a cheap, easy to install, and an ubiquitous alternative to conventional non-intrusive load monitoring (NILM).

We propose, design, and prototype (using software defined radios) a layered hybrid decode-estimate- and-forward relaying strategy. The scheme relies on splitting the source message into two layers with unequal error protection (UEP). Considering half-duplex relay operation, cooperation takes place over two time-slots. The relay attempts to decode both layers during the first time-slot; with the probability of successfully decoding one of the layers being higher than the other due to UEP between the two layers. During the second time-slot, the relay transmits the jointly encoded message containing the successfully decoded layer(s) with the (imperfect) decoding estimates of the unrecoverable layer(s). In order to recover the source information, we design a decoding strategy at the destination that jointly operates on the signals received from the source and the relay. The proposed scheme is implemented using off-the-shelf systematic low-density parity-check codes, with joint decoding achieved through iterative message-passing. Simulations over narrowband Gaussian links indicate a performance gain of up to 3 dB compared to the unlayered operation at the source. We also present prototype results with over-the-air transmissions using National Instruments (NI) USRPs 2921s with all baseband processing implemented in NI LabView. These experimental results indicate significant performance gains over a conventional unlayered relaying approach.

We consider a non-orthogonal multiple access channel in which multiple users communicate with a base-station through a dedicated relay. For this setup, we propose a novel joint-noisy network-coded (J-NNC)-decode-and-forward (DF) cooperation scheme that employs NNC relaying with opportunistic DF. The proposed scheme utilizes the DF cooperation when messages from all users are successfully decoded at the relay and the NNC when the relay is unable to decode any one of the users. In the scenario when the relay decodes only a subset of users, the proposed scheme utilizes joint DF and NNC encoding. We derive the information theoretic conditions required for successful message recovery and show that the J-NNC-DF has a strictly better outage performance than the conventional DF-only, NNC-only, or DF-or-NNC schemes. For the two-user case, we derive closed-form expressions for the outage performance under Rayleigh fading while assuming access only to the local channel state information at the relay. For these expressions, let us choose the NNC quantization parameter so as to attain optimum outage performance. The simulation results validate the theoretical findings and show that the J-NNC-DF achieves a performance gain of 0.75, 1.7, and 2.1 dB compared with the DF-or-NNC, DF-only and NNC-only strategies, respectively.

In light of increasing interest in detection of concealed metallic weapons, there is a great need to have robust and non-obtrusive metal detection systems with large coverage areas. Conventional systems based on electromagnetic induction or X- rays are effective, but have small coverage areas in addition to requiring costly infrastructure. In this paper, we explore the use of ubiquitously present WiFi signals for non-obtrusive detection of concealed metal objects. For the purpose, we build a prototype system consisting of a single- antenna commodity WiFi radio as a transmitter, and two multi-antenna radios as receivers placed in an indoor environment of approximately 42 ft by 39 ft. We conduct extensive experiments with subjects walking through the setup with (or without) a sheet of metal placed around their chests. We use the channel state-information collected from the receivers to train a deep convolutional neural network, and find that the proposed system can differentiate between the metal and non-metal cases with an average accuracy of 86.44.

Risky driving is a leading cause of traffic accidents all over the world. Major factors contributing to risky driving include driver distraction (e.g. smart-phone usage) and an onset of driver drowsiness. As a result, improving road-safety requires modeling and detecting in-vehicle conditions indicative of risky driver behavior. Previous works towards driver behavior modeling and classification possess limitations such as increased expenditure and/or additional external infrastructure requirements. In this paper, we propose a novel driver behavior detection framework that at its core makes use of in-vehicle Wi-Fi signals. The main premise of the proposed framework is that risky behavior induces specific differentiable patterns in received WiFi signals - patterns that are then exploited to build an efficient classification system. More specifically, we classify drowsy and inattentive driving into four main gestures that reflect unsafe driving. These gestures include (a) Yawning, (b) Head Jerks, (c) Sideways motion, and (d) Smart-phone usage. Channel State Information (CSI) is used to determine the driver's gesture which is further used for behavior modeling. A number of low computational features are used to classify among different gestures. Experiments are performed in a real in-vehicle environment using software-defined radios. Using data collected from these experiments, a simple ensemble classifier is implemented which is shown to achieve an average accuracy of 88.64%.

We present a prototype implementation of Stalkers; a physical layer solution that lets a pair of opportunistic secondary nodes to co-exist with a primary IEEE 802.11-type transmitter-receiver pair using simultaneous in-band transmissions. For this co-existence, the secondary system first stalks the primary system, following which it shapes its own transmissions so as to cause no degradation in the primary network's performance while at the same time not necessitating any change in the primary nodes' physical layer. Drawing inspiration from existing information theoretic literature, the secondary transmissions of the implemented solution rely on (a) forwarding of the primary system's signal by the secondary system to compensate for the interference caused by the latter at the former, and (b) Tomlinson-Harashima precoding to mitigate the primary interference seen by the secondary network. The solution prototype is built with software-defined radios, using which we experimentally demonstrate that the performance of the primary 802.11-type system remains unaffected with the introduction of the secondary system, thereby acquiring throughput for the in-band co-existing solution.

We consider a two-user non-orthogonal-multiple-access (NOMA) communication channel with an iterative multi-user receiver. It is known that NOMA provides performance gains over conventional orthogonal-multiple-access, but it comes at the cost of increased decoder complexity. Moreover, the decoder complexity varies with fraction of duration in which the users' transmissions overlap using NOMA. For this scheme, we present a hardware-friendly implementation of an LDPC-code-based multi-user-detector (MUD). We also present a MATLAB-based high-level design-automation tool that generates hardware descriptions of different NOMA-receivers. Results indicate that the simplified MUD design results in significant area and power savings with negligible impact on performance.

We consider two-user relay-aided uplink nonorthogonal multiple access (NOMA) with noisy network coding (NNC) at the relay. The NNC-based cooperation strategy at the relay relies on noisy quantization followed by joint source-channel coding, while at the destination it utilizes joint decoding using the non-orthogonal signals received from the sources and the relay. After presenting the information theoretic achievable rates for the NNC-based cooperation scheme, we discuss how it outperforms conventional compress-and-forward (CF) cooperation even when the relay has access to global channel state information (CSI). For the NNC-based approach, we also derive closed-form expressions for probability of outage when the relay has access to local CSI, as well as when the relay has knowledge of the receive CSI only. These closed-form expressions help in appropriately choosing the quantizer so as to optimize the outage performance. Simulation indicate that NNC-based relaying outperforms CF by 2.2 dB when global CSI is available at the relay. Additionally, the availability of global CSI results in an improvement of 3.8 dB and 5.4 dB over the case when relay has access only to local CSI and the receive CSI, respectively.

We develop a practical layered detect-compress-and-forward (LDCF) coding framework for a three-node relay network. Assuming all transmissions to be mapped to Quadrature Amplitude Modulation (QAM) constellations, the LDCF framework at the relay utilizes maximum-likelihood detection, followed by a multi-level convolutional coding structure to achieve distributed joint source-channel coding of the detected indices. The layering is achieved by mapping the more important detection bit-planes to the more significant bit-planes of the QAM constellation, thus providing them a higher level of protection. As a result, the proposed strategy adapts to heterogeneous channel conditions by letting the destination extract as many CF cooperation layers as the channel conditions allow. At the destination, we propose an iterative message passing algorithm to achieve joint decoding of the relay's detection layers before recovering the original source information. Simulations results with off-the-shelf convolution codes indicate a performance gain of approximately 3 dB over the traditional unlayered CF strategy.

This paper presents novel results for Massive MIMO time division duplex (TDD) system where the beam- forming vector in the down-link makes use of MMSE channel estimates with quasi-orthogonal pilot sequences as opposed to orthogonal pilot sequences used in the prior art. The proposed method tackles the issue of pilot contamination by adapting the length of quasi-orthogonal pilot sequences according to the channel conditions or coherence time, leading to maximization of the total throughput. We compute the optimal length of the pilot sequences, for both orthogonal and quasi- orthogonal pilot sequences, that maximizes the achievable rates. We formulate the design criteria of the proposed scheme by using a classical theorem of estimation theory for multi- variate Gaussian distribution. Then we solve the criteria using heuristic algorithms and carry out simulations for different coherence times in a multi-cell system. It is shown that our proposed scheme requires 17\% less pilot resources as compared to schemes that employ orthogonal pilots. Furthermore, an interesting trade-off between the choice of pilots and the sum rate is observed for variable length of coherence interval.

This paper presents a prototype implementation and experimental performance evaluation of compress- and-forward (CF) relaying using software-defined radios. Under QPSK transmissions from all nodes, the relay demodulates the received signal before applying distributed joint source-channel coding (DJSCC) implemented with the help of an off-the- shelf convolutional code. The destination employs two stages of standard BCJR decoding; a DJSCC decoding stage for the relay's demodulated data followed by another to recover the source's message. Under varying channel conditions, we propose and implement an opportunistic Decode-or- Compress-and-Forward (DoCF) relaying strategy in which the relay forwards the decoded packet if a cyclic redundancy check (CRC) is satisfied, and uses CF relaying otherwise. The prototype implementation of DoCF is carried out using National Instruments USRP 2921 devices. Over-the- air experiments in an indoor office environment indicate that the proposed DoCF strategy can achieve up to ten times smaller bit-error-rate than a standard DF implementation. To the best of our knowledge, this work is the first in the literature to carry out an experimental study of CF relaying.

We consider layered decode-forward (DF) cooperation in a relay-aided wireless multicast network. Splitting the source-message into two equal layers, we provide unequal power allocation to the individual layers through a simple mapping operation on a QAM constellation. The layering process thus allows the destinations to partially recover the message from the source's transmissions. At the relay, we propose a multiplexed-coded approach that, with a single transmission, caters for the disparity in the number of layers decoded at different destinations. In addition to simulations, we validate the performance gains of the proposed strategy through a system-level implementation using software-defined radios. Catering for real- world factors such as carrier and timing synchronization, we conduct over-the-air experiments in an indoor office environment and find that the proposed scheme can achieve a frame- error-rate that is 25% of that with conventional two-hop DF relaying.

We consider a relay-aided wireless multicast channel in which all transmissions are constrained to be drawn from M-ary PAM constellations. In order to deal with heterogeneous channel conditions at different receivers, we propose a practical layered compress-and-forward (CF) scheme that utilizes scalar quantization concatenated with Slepian-Wolf coding. Exploiting the inherent unequal protection provided to different bit-planes of an M-ary PAM constellation, the layering is accomplished through an intelligent mapping of the quantization bits to the relay's transmission symbols. This allows users to adapt by extracting as many CF cooperation layers as allowed by the channel conditions. By analyzing information theoretic performance, we find that the proposed scheme performs significantly better than the traditional unlayered CF for a wide variety of channel conditions; our best results indicate a performance gap of 1.1 dB between the two.

We consider cooperative spectrum sensing in a cognitive radio network where multiple nodes exchange their sensing information over wireless links. Although sensing in a cooperative manner improves detection reliability, it comes at the cost of a significant overhead in terms of reporting energy and delay. As a means of reducing this overhead, we propose an energy-efficient cooperative sensing scheme, at the heart of which lies rateless coded relaying coupled with an efficient user selection criterion that incorporates the scheme's rateless coded nature. Through simulations, we show that for the same detection performance, a scheme based on fixed-rate codes utilizes 62% more energy and 15% higher delay compared to that with the proposed rateless coded relaying strategy.

In this paper, we use recently developed error estimation coding (EEC) to devise an efficient relaying framework for a multi-relay network. EEC utilizes an added redundancy to form an estimate of the bit-error rate (BER) of data received over a noisy channel. Utilizing the BER estimate, we propose a relaying selection strategy that uses the BER estimate at any given relay to switch between Amplify-Forward (AF) and Detect-Forward (DF) cooperation. In addition, we propose a signal combining rule at the destination that weighs different copies of the same data using the corresponding BER estimates. For performance evaluations, we implement the proposed scheme on a USRP-based platform, and test its performance by conducting experiments in an indoor office environment. Our results validate the efficacy of the proposed strategy; the proposed scheme significantly outperforms standard equal gain combining-based AF and DF cooperation.

In this paper, we focus on setting up a gunshot detection system with high detection performance, robustness to noise and low computational complexity. To achieve these objectives, we formulate a two-stage approach with a less costly impulsive event detection framework followed by a relatively more complex gunshot recognition stage. To improve detection performance of the gunshot recognition stage, we propose a template matching measure in conjunction with the eighth order linear predictive coding coefficients to train a support vector machine classifier. Using an extensive audio database, we were able to achieve a better gunshot recognition performance than with the well-known existing features used for gunshot detection.

We consider a joint Slepian-Wolf/Dirty-paper coding (SW-DPC) setup where a binary source needs to be transmitted over an additive white-Gaussian channel in the presence of interference known only at the encoder, as well as correlated side-information available only at the decoder. We propose a practical joint SW-DPC framework that uses a single low-density parity-check code coupled with trellis coded quantization to simultaneously provide error protection (in the face of noise and interference) and compression (in the face of correlated side-information available at the decoder). Simulation results indicate that the proposed joint SW-DPC scheme with finite-length codes outperforms a scheme with separate SW and DPC by 0.3 dB. In addition, we show that our joint SW-DPC code designed for one set of channel conditions is capable of achieving negligible bit-error rates for many other channel conditions as well. More specifically, it achieves successful decoding for a wide variety of channel conditions for which a separation-based scheme fails.

Tail-biting convolutional codes (TBCC) find applications in many modern-day communication standards such as LTE and IEEE 802.16e. Since tail-biting convolutional codes do not require a zero-tail, they achieve a better coding efficiency than their traditional counterparts. However, the absence of a zero-tail drastically increases the complexity of a standard maximum-likelihood decoder, making its implementation impractical. However, recently a decoder based on the Viterbi and A* algorithm has been proposed that achieves maximum likelihood performance with significantly reduced complexity. This paper presents an efficient hardware implementation of this algorithm for TBCCs corresponding to both LTE and IEEE 802.16e standards. The designs have been tested on a Xilinx Spartan 3E starter kit, achieving a throughput of 141 Mbps and 130 Mbps for the LTE and IEEE 802.16e TBCCs, respectively.

We propose a robust close-to-capacity dirty-paper coding (DPC) design framework in which multi-level low density parity check (LDPC) codes and trellis coded quantization (TCQ) are employed as the channel and source coding components, respectively. The proposed design framework is robust in the sense that it yields close to capacity solutions in the high-, medium-, and low-rate regimes. This is in contrast to existing practical DPC schemes that perform well only in one or two of these regimes, but not all three. We design codes for transmission rates of 0.5, 1.0, 1.5, and 2.0 bits/sample (b/s) using one, two, three, and four LDPC levels; at a block length of 2×105, the codes perform 0.95, 0.58, 0.55, and 0.54 dB from the corresponding information theoretic limits, respectively. We also propose a low-complexity decoding scheme that does not involve iterative message passing between the source and channel decoders; the low-complexity scheme performs only 1.08, 0.85, and 0.79 dB away from the theoretical limits at transmission rates of 1.0, 1.5, and 2.0 b/s, respectively.

This paper presents a multi-level compress and forward coding scheme for a three-node relay network in which all transmissions are constrained to be from an M-ary PAM constellation. The proposed framework employs a uniform scalar quantizer followed by Slepian-Wolf coding at the relay. We first obtain a performance benchmark for the proposed scheme by deriving the corresponding information theoretical achievable rate. A practical coding scheme involving multi-level codes is then discussed. At the source node, we use multi-level low-density parity-check codes for error protection. At the relay node, we propose a multi-level distributed joint source-channel coding scheme that uses irregular repeat-accumulate codes, the rates of which are carefully chosen using the chain rule of entropy. For a block length of 2×105symbols, the proposed scheme operates within 0.56 and 0.63 dB of the theoretical limits at transmission rates of 1.0 and 1.5 bits/sample, respectively.

We consider a cooperative wireless multicast channel with no channel state information available at the transmitters, and where all wireless links undergo independent identically distributed quasi-static Rayleigh fading. For cooperation, we propose a rateless coded approach and identify its benefits over a conventional fixed-rate two-hop strategy. In addition to providing robust communications under fading conditions, rateless coding is able to exploit the cooperation benefits to a greater degree. A hybrid rateless coded cooperation scheme is proposed which employs amplify-and-forward (AF) in conjunction with decode-and-forward (DF) strategy. Results based on Monte-Carlo simulations indicate that the rateless coded hybrid scheme is able to significantly outperform two-hop cooperation, as well as the individual AF and DF strategies.

This paper derives a formula for the wideband slope for outage capacity and consider its application to some specific wireless channels. For the broadcast channel, the formula indicates that superposition is always superior to time-division multiple access (TDMA). On the other hand, for the interference channel, we show that TDMA is better than superposition for most realistic situations.

We propose a close-to-capacity dirty-paper coding framework which employs multi-level low density parity-check (LDPC) and trellis coded quantization. The proposed coding framework is robust in the sense that it performs close to capacity in the high as well as the low rate regimes. This is in contrast to existing practical DPC schemes which perform well at one of these regimes, but never both. In order to evaluate the performance of our scheme, we consider its application to a cognitive radio channel. At a block length of 2 × 105, the designed dirty-paper coding scheme operates within 0.95, 0.58 and 0.6 dB of the theoretical limit at transmission rates of 0.5, 1.0 and 1.5 bits/sample, respectively. As far as the authors are aware, this is the best performance reported in the literature so far.

In this paper, we consider the doped decoder for Luby-Transform (LT) codes, where based on the decoder feedback, the decoding process is revived by retransmission of the in formation packet. We propose several improved doping methods to reduce the average doping rate. We then provide an analysis of the proposed doping methods based on the study of the decoding ripple evolution. Both analytical and simulation results show that the proposed doping approaches provide significant performance gain over the existing random doping strategy. We then apply the doped LT decoding to the wireless broadcast system in which limited feedback is allowed. We propose a majority vote based doping selection and demonstrate the efficiency of the proposed doping selection through simulations.

We implement a dirty-paper coded framework for the cognitive radio channel. We assume that the cognitive user has non-causal knowledge about the primary user's transmissions. Thus the secondary receiver can employ dirty-paper coding to counter the effect of any interference from the primary user. In addition, we consider a situation where the introduction of the cognitive user should not affect the performance of the primary system -- nor should the primary system have to change its encoding/decoding process. For the primary user we use a low-density parity-check code and a 4-ary pulse amplitude modulation format. For the cognitive user, we propose a dirty-paper coding scheme which employs trellis-coded quantization as the source code and an irregular repeat-accumulate code as the channel code. At a transmission rate of 1.0 bits/sample, the designed dirty-paper coding scheme operates within 1.23 dB of the theoretical limit.

We consider outage capacity in the broadcast channel when the base-station has none or little channel knowledge. We find the minimum energy per bit needed to achieve a certain outage probability and the corresponding wideband slope.

We propose a rateless coded protocol for a half-duplex wireless relay channel where all links experience independent quasi-static Rayleigh fading. The protocol utilizes a combination of rateless coded decode-forward and compress-forward relaying schemes. Assuming very limited feedback from the destination, we derive the theoretical performance limits specifically with BPSK modulation. We then implement the rateless coded relaying protocol using carefully designed Raptor codes.

This paper considers cooperation in the low power/SNR regime for the multiple access channel. We assume that transmitters have no channel state information, and consider the outage capacity. We further assume that the nodes operate in half duplex. The nodes are multiplexed by assigning to each node a unique frequency band via frequency division multiplexing (FDM). A node transmits only on this band while listening on the others. We perform outage wideband analysis on FDM based cooperation, and show perhaps surprisingly that FDM loses very little in performance compared to the idealized situation where nodes can operate in full duplex. We then develop practical rateless coding methods for FDM using Raptor codes.

Recent information-theoretic results show the optimality of dirty-paper coding (DPC) in achieving the full capacity region of the Gaussian multiple-input multiple-output (MIMO) broadcast channel (BC). This paper presents a DPC based code design for BCs. We consider the case in which there is an individual rate/signal-to-interference-plus-noise ratio (SINR) constraint for each user. For a fixed transmitter power, we choose the linear transmit precoding matrix such that the SINRs at users are uniformly maximized, thus ensuring the best bit-error rate performance. We start with Cover's simplest two-user Gaussian BC and present a coding scheme that operates 1.44 dB from the boundary of the capacity region at the rate of one bit per real sample (b/s) for each user. We then extend the coding strategy to a two-user MIMO Gaussian BC with two transmit antennas at the base-station and develop the first limit-approaching code design using nested turbo codes for DPC. At the rate of 1 b/s for each user, our design operates 1.48 dB from the capacity region boundary. We also consider the performance of our scheme over a slow fading BC. For two transmit antennas, simulation results indicate a performance loss of only 1.4 dB, 1.64 dB and 1.99 dB from the theoretical limit in terms of the total transmission power for the two, three and four user case, respectively.

Cover and El Gamal derived the tightest bounds on the capacity of the relay channel using random coding and suggested two coding strategies, namely, decode-forward (DF) and compress-forward (CF), to provide the best known lower bound of the achievable rate. Practical code designs proposed recently mainly exploit DF to approach the lower bound. Following the latest development in practical distributed source-channel coding, this paper studies CF coding with BPSK modulation for the relay channel. In CF scheme, Wyner-Ziv coding is applied at the relay to exploit the joint statistics between signals at the relay and the destination. We employ Slepian-Wolf coded nested scalar quantization (SWCNSQ) in practical Wyner-Ziv coding at the relay and compute the achievable rates of this scheme with BPSK modulation for the half-duplex Gaussian relay channel. We present a code design based on LDPC codes for error protection at the source and NSQ and IRA codes for CF at the relay. Simulation results show that our design comes within 1.48-1.92 dB of the SWCNSQ limit.

Driven by applications in data-hiding, MIMO broadcast channel coding, precoding for interference cancellation, and transmitter cooperation in wireless networks, Costa coding has lately become a very active research area. In this paper, we first offer code design guidelines in terms of source- channel coding for algebraic binning. We then address practical code design based on nested lattice codes and propose nested turbo codes using turbo-like trellis-coded quantization (TCQ) for source coding and turbo trellis-coded modulation (TTCM) for channel coding. Compared to TCQ, turbo-like TCQ offers structural similarity between the source and channel coding components, leading to more efficient nesting with TTCM and better source coding performance. Due to the difference in effective dimensionality between turbo-like TCQ and TTCM, there is a performance tradeoff between these two components when they are nested together, meaning that the performance of turbo-like TCQ worsens as the TTCM code becomes stronger and vice versa. Optimization of this performance tradeoff leads to our code design that outperforms existing TCQ/TCM and TCQ/TTCM constructions and exhibits a gap of 0.94, 1.42 and 2.65 dB to the Costa capacity at 2.0, 1.0, and 0.5 bits/sample, respectively.

Dirty paper code (DPC) can be used in a number of communication network applications; broadcast channels, multiuser interference channels and ISI channels to name a few. We study various implementation bottlenecks and issues with implementing a DPC pre-coder based on nested trellis technique. The aim is to achieve a practical hardware realization of the precoder for wireless LAN/DSL applications. We describe the architectural development process and realization of the precoder on a Xilinx Virtex 2V8000 FPGA. To the best of our knowledge this is the first reported DPC pre-coder hardware implementation.

In this paper we develop practical rateless coded cooperation strategies for a two-user multiple access channel. At the heart of our practical strategies lie Raptor codes concatenated with a space-time code, and an iterative decoding procedure to jointly recover the two users' messages at the base station. Since cooperation in multiple access channels is known to be particularly beneficial when considering the outage probability at low SNRs, or equivalently at low transmission rates, we simulate our strategies at a transmission rate of 0.25 bits/sample. Experiments indicate that our schemes perform very close to the theoretical limit, with the performance gap being less than 0.55 dB.

Driven by the applications in data hiding, multiple-input multiple-output broadcast channel coding, precoding for interference cancellation, and transmitter cooperation in wireless networks, Costa coding has lately become a very active research area. In this paper, we first offer code design guidelines in terms of source channel coding for algebraic binning. We then address a practical code design based on nested lattice codes, and propose nested turbo codes by using turbo-like trellis-coded quantization (TCQ) for source coding and turbo trellis-coded modulation (TTCM) for channel coding. Compared to the TCQ, the turbo-like TCQ offers a structural similarity between the source and channel coding components, leading to a more efficient nesting with TTCM and a better source coding performance. Due to the difference in effective dimensionality between turbo-like TCQ and TTCM, there is a performance tradeoff between these two components when they are nested together, meaning that the performance of the turbo-like TCQ worsens as the TTCM code becomes stronger and vice versa. The optimization of this performance tradeoff leads to our code design that outperforms existing TCQ/TCM and TCQ/TTCM constructions, and exhibits a gap of 0.94, 1.42, and 2.65 dB to the Costa capacity at 2.0, 1.0, and 0.5 b/s, respectively.

Recent information-theoretical results show the optimality of dirty-paper coding (DPC) in achieving the capacity of the Gaussian multiple-input multiple-output (MIMO) broadcast channel (BC). This paper presents the first practical limit approaching DPC-based design for the MIMO BC. We start with Cover's simplest two-user Gaussian BC and present a code design that operates 1.44 dB away from the capacity region boundary at a transmission rate of 1.0 bit per sample (b/s). Then we consider the non-degraded two-user MIMO fading BC with two transmit antennas. For this setup, the performance loss of our code design is 3.7 dB and 2.45 dB from the the sum-rate capacity when the transmission rate for each user is 1.0 b/s and 2.0 b/s, respectively.

Low density parity check (LDPC) codes have received much attention for their excellent performance, and the inherent parallelism involved in decoding them. We consider a type of structured binary LDPC codes, known as array LDPC codes, which have low encoding complexity and good performance, for implementation on a Xilinx field programmable gate array (FPGA) device.