This paper investigates a low Earth orbit (LEO) satellite communication system enhanced by an active stacked intelligent metasurface (ASIM), mounted on the backplate of the satellite’s solar panels to efficiently utilize limited onboard space and reduce the main satellite power amplifier requirements. The system serves multiple ground users via rate-splitting multiple access (RSMA) and IoT devices through a symbiotic radio network. Multi-layer sequential processing in the ASIM improves effective channel gains and suppresses inter-user interference, outperforming active RIS and beyond-diagonal RIS designs. Three optimization approaches are evaluated: block coordinate descent with successive convex approximation (BCD-SCA), model-assisted multi-agent constraint soft actor-critic (MA-CSAC), and multi-constraint proximal policy optimization (MCPPO). Simulation results show that BCD-SCA converges fast and stably in convex scenarios without learning, MCPPO achieves rapid initial convergence with moderate stability, and MA-CSAC attains the highest long-term spectral and energy efficiency in large-scale networks. Energy–spectral efficiency trade-offs are analyzed for different ASIM elements, satellite antennas, and transmit power. Overall, the study demonstrates that integrating multi-layer ASIM with suitable optimization algorithms offers a scalable, energy-efficient, and high-performance solution for next-generation LEO satellite communications.

In the evolving landscape of wireless communications for 5G, 6G, and beyond, the deployment of unmanned aerial vehicles (UAVs) has emerged as a groundbreaking strategy to expand coverage areas due to their flexibility and ease of deployment. Simultaneously, reflecting intelligent surfaces (RISs) have introduced a transformative paradigm aimed at improving key performance metrics, such as average sum-rate and energy efficiency (EE). The seamless integration of advanced technologies, including UAVs, RIS, and nonorthogonal multiple access (NOMA), presents a promising avenue for significantly boosting the performance and efficiency of next-generation communication systems. This study investigates EE maximization for two scenarios in a NOMA-enabled mmWave network: 1) multi-UAV-mounted base stations (BSs) and 2) multi-UAV-mounted distributed RIS. In both cases, each UAV serves a NOMA cluster with imperfect successive interference cancellation (SIC), capturing the impact of hardware impairments in real-world NOMA systems. For each scenario, an optimization problem is formulated to maximize EE by jointly optimizing the beamforming matrix, phase shift matrix, NOMA power allocation, and UAV 3-D placement. The nonconvex problems are tackled using both model-based and model-free deep reinforcement learning (DRL) algorithms under constraints, such as minimum Quality of Service (QoS), beamforming and phase shift limits, and UAV trajectory constraints. The simulation results demonstrate that the proposed DRL algorithms significantly enhance spectral efficiency (SE) and EE, showcasing their suitability for 6G communication systems. Furthermore, a comparative analysis with orthogonal multiple access (OMA) and spatial-division multiple access (SDMA) confirms that NOMA outperforms both techniques, achieving substantial gains in efficiency and performance.

Physical-layer key generation (PLKG) can significantly enhance the security of classic encryption schemes by efficiently providing secret keys in resource-limited network like the Internet of Things (IoT). However, reaching a high key generation rate (KGR) is challenging in applications like smart home or remote area sensing with quasi-static channels. Recently, exploiting reconfigurable intelligent surface (RIS) to induce randomness in quasi-static wireless channels has received significant research interest. However, the inherent spatial correlation among the RIS elements is rarely studied, which can alter the optimum physical-layer key generation (PLKG) approach in terms of KGR and randomness in the key sequence. Specifically, for the first time, in this contribution, we take into account a spatially correlated RIS, which intends to enhance the KGR in a quasi-static medium. Novel closed-form analytical expressions for KGR are derived for the two cases of random phase shift (RPS) and our proposed equal phase shift (EPS) in the RIS elements. We also analyze the correlation between the channel samples to ensure the randomness of the generated secret key sequence. It is shown that the EPS scheme can effectively exploit the inherent spatial correlation between the RIS elements and it leads to a higher KGR compared to the widely used RPS strategy. We further formulate an optimization problem in which we determine the optimal portion of time dedicated to direct and indirect channel estimation, which has never been addressed in previous studies. We show the accuracy and the fast convergence of our sequential convex programming (SCP)-based algorithm and discuss the various parameters affecting spatially correlated RIS-assisted PLKG.

In this paper, a generalization of deep learning-aided joint source channel coding (Deep-JSCC) approach to secure communications is studied. We propose an end-to-end (E2E) learning-based approach for secure communication against multiple eavesdroppers over complex-valued fading channels. Both scenarios of colluding and non-colluding eavesdroppers are studied. For the colluding strategy, eavesdroppers share their logits to collaboratively infer private attributes based on ensemble learning method, while for the non-colluding setup they act alone. The goal is to prevent eavesdroppers from inferring private (sensitive) information about the transmitted images, while delivering the images to a legitimate receiver with minimum distortion. By generalizing the ideas of privacy funnel and wiretap channel coding, the trade-off between the image recovery at the legitimate node and the information leakage to the eavesdroppers is characterized. To solve this secrecy funnel framework, we implement deep neural networks (DNNs) to realize a data-driven secure communication scheme, without relying on a specific data distribution. Simulations over CIFAR-10 dataset verifies the secrecy-utility trade-off. Adversarial accuracy of eavesdroppers are also studied over Rayleigh fading, Nakagami-m, and AWGN channels to verify the generalization of the proposed scheme. Our experiments show that employing the proposed secure neural encoding can decrease the adversarial accuracy by 28%.

In this paper, a cost-effective and high-accuracy precoding technique based on$\epsilon $-Fuzzy pareto active learning (FPAL) is proposed for millimeter wave (mmWave) massive MIMO communications. The proposed method achieves a low iteration convergence with low complexity. Two practical structures, namely fully-connected and partially-connected structures are considered for hybrid precoding. Furthermore, the effect of high and low-resolution quantization in the digital-to-analog converter, phase shifter, and imperfect channel state information are discussed. The performance results of the proposed technique and alternating minimization methods beside fully digital techniques are compared and discussed in the terms of the spectral efficiency (SE), bit error rate (BER), normalized mean square error (NMSE), energy efficiency (EE) for phase-shifter (PS) with low bit quantization, and imperfect channel state information (CSI). To address the applicability of the proposed method, experimental results are obtained from a real mmWave hardware setup compliant with 3GPP standards, and verify the simulated ones for the proposed$\epsilon $-FPAL hybrid precoding scheme. The reduced complexity, higher performance and EE make the proposed method suitable for 5G and beyond communication systems.

Digital healthcare services are rapidly evolving for new methodologies, including hospital-to-home (H2H) services and intelligent Internet-of-Medical-Things (IIoMT). The sixth generation (6G) technology is considered as the fabric that facilitates the realization of these technologies, creating a paradigm shift towards personalized e-health services. To deal with the high security requirements and the energy constraints of 6G-enabled e-health services, we propose a lightweight learning-based key generation scheme for a pair of wireless-powered nodes in a cooperative communication system, where the legitimate nodes and the intermediate node have low-cost hardware-impaired transceivers. We utilize an echo state network (ESN) to enhance the “randomness distillation” phase, in which the legitimate parties try to obtain a common source of randomness as the raw data for key agreement. The PHY-based observed data is passed to the ESN, containing a reservoir of sparsely connected neurons to compensate for observation mismatches caused by the unbalanced hardware impairments. The output of the ESN can then be utilized to extract the secret key between e-health endpoints. Numerical experiments verify the performance gain of our proposed echo-based approach, resulting in 50% less required inference time compared with a fully-connected neural network (FCNN). Moreover, a performance gain of about 32% in terms of mean-square error (MSE) is achieved compared with a conventional PHY-only scheme.

Abstract With the emergence of social networks and improvements in the internet speed, the video data has become an ever‐increasing portion of the global internet traffic. Besides the content, the quality of a video sequence is an important issue at the user end which is often affected by various factors such as compression. Therefore, monitoring the quality is crucial for the video content and service providers. A simple monitoring approach is to compare the raw video content (uncompressed) with the received data at the receiver. In most practical scenarios, however, the reference video sequence is not available. Consequently, it is desirable to have a general reference‐less method for assessing the perceived quality of any given video sequence. In this paper, a no‐reference video quality assessment technique based on video features is proposed. In particular, a long list of video features (21 sets of features, each consisting of 1 to 216 features) is considered and all possible combinations () for training an Extra Trees regressor is examined. This choice of the regressor is wisely selected and is observed to perform better than other common regressors. The results reveal that the top 20 performing feature subsets all outperform the existing feature‐based assessment methods in terms of the Pearson linear correlation coefficient (PLCC) or the Spearman rank order correlation coefficient (SROCC). Specially, the best performing regressor achieves on the test data over the KonVid‐1k dataset. It is believed that the results of the comprehensive comparison could be potentially useful for other feature‐based video‐related problems. The source codes of the implementations are publicly available.

In this paper, a learning-aided content-based image transmission scheme is proposed, where a multi-antenna source wishes to securely deliver an image to a legitimate destination in the presence of randomly-distributed passive eavesdroppers (Eves). We take into account the fact that not all regions of an image have the same importance from the security perspective. Hence, we employ a hybrid method to realize both the error-free data delivery of public regions—containing less-important pixels; and an artificial noise (AN)-aided transmission scheme for securing the confidential packets. To reinforce system’s security, fountain-based packet delivery is also adopted, where the source node encodes images into fountain-like packets prior to sending them over the air. The secrecy is achieved when the legitimate destination correctly receives the entire source packets before Eves obtain the important regions, while conforming to the latency limits of the system. Accordingly, the secrecy performance of our scheme is characterized by deriving a closed-form expression for the quality-of-security (QoSec) violation probability. Moreover, our proposed image delivery scheme leverages a deep neural network (DNN) and learns to maintain optimized transmission parameters, while achieving a low QoSec violation probability. Simulation results are provided to illustrate that our proposed learning-assisted scheme outperforms the state-of-the-arts by achieving considerable gains in terms of security and delay requirement.

Wireless secret key generation (WSKG) allows efficient key agreement protocols for securing the sixth generation (6G) wireless networks. Nevertheless, due to external adversaries or internal impairments, WSKG schemes might become vulner-able during the randomness distillation, where the legitimate nodes try to observe their source of common randomness. In this paper, we investigate the WSKG scheme with legitimate parties suffering from hardware impairments (HIs), while an active adversary acts as a man-in-the-middle (MiM) via injecting fake pilot signals. We first utilize randomized pilots to overcome the MiM. We also leverage the concept of recurrent neural networks (RNNs) to further enhance the randomness distillation. More specifically, the long short-term memory networks (LSTMs)-as a well-established type of RNNs-are implemented to learn the long-term dependencies between the observations of legitimate parties. The achievable secret key rate (SKR) and the impact of MiM on system's performance are analyzed. Our numerical results verify the performance gain of our proposed learning-based approach compared with the state-of-the-art methods and provide useful insights on system design. We show that our RNN-based approach achieves 30% and 15% improvement in terms of observation mismatches compared with the naïve scheme and the fully-connected benchmarks, respectively.

Unmanned Aerial Vehicles (UAVs) have found compelling applications in intelligent logistics, search and rescue as well as in air-borne Base Station (BS). However, their communications are prone to both channel errors and eavesdropping. Hence, we investigate the max-min secrecy fairness of UAV-aided cellular networks, in which Cooperative Rate-Splitting (CRS) aided downlink transmissions are employed by each multi-antenna UAV Base Station (UAV-BS) to safeguard the downlink of a two-user Multi-Input Single-Output (MISO) system against an external multi-antenna Eavesdropper (Eve). Realistically, only Imperfect Channel State Information (ICSI) is assumed to be available at the transmitter. Additionally, we consider a realistic total power constraint and guarantee the specific Quality of Service (QoS) requirements of the legitimate users. To handle the worst-case channel uncertainty of the legitimate users and an external Eve, we conceive a robust secure resource allocation algorithm, which maximizes the minimum worst-case secrecy rate of the legitimate users. Based on the CRS principle, the transmitter splits and encodes the messages of legitimate users into common as well as private streams and the user having stronger CSI is asked to help the cell-edge user by opportunistically forwarding its decoded common message. In contrast to the existing schemes adopted in the literature for ensuring secure transmission of the first cooperative phase only, in our proposed solution the common message has a twin-fold mission. Explicitly, apart from serving as the desired message, it also acts as Artificial Noise (AN) for drowning out Eve without consuming extra power. This is in stark contrast to the conventional AN designs. In the second phase, the pure AN is directed towards the Eve, deploying a robust Maximum Ratio Transmitter (MRT) beamformer at the UAV-BS. To solve the resultant non-convex optimization problem we resort to the Sequential Parametric Convex Approximation (SPCA) method together with a bespoke initialization algorithm to avoid any failure due to infeasibility. Our simulation results confirm that the proposed secure transmission scheme outperforms the existing cooperative benchmarkers.

Detecting various types of cells in and around the tumor matrix holds a special significance in characterizing the tumor micro-environment for cancer prognostication and research. Automating the tasks of detecting, segmenting, and classifying nuclei can free up the pathologists' time for higher value tasks and reduce errors due to fatigue and subjectivity. To encourage the computer vision research community to develop and test algorithms for these tasks, we prepared a large and diverse dataset of nucleus boundary annotations and class labels. The dataset has over 46,000 nuclei from 37 hospitals, 71 patients, four organs, and four nucleus types. We also organized a challenge around this dataset as a satellite event at the International Symposium on Biomedical Imaging (ISBI) in April 2020. The challenge saw a wide participation from across the world, and the top methods were able to match inter-human concordance for the challenge metric. In this paper, we summarize the dataset and the key findings of the challenge, including the commonalities and differences between the methods developed by various participants. We have released the MoNuSAC2020 dataset to the public.

The colorization of gray-scale images has always been a challenging task in computer vision. Recently, novel approaches have been introduced for unsupervised image translation between two domains using Generative Adversarial Networks (GANs). Since one can consider the gray-scale and colorful images as two separate domains, we propose a two-stage cycle-consistent network architecture to produce convincible images. First, an intermediate image is generated with a relatively uncomplicated objective function at the output. Next, at the second stage, the intermediate image is enhanced via a residual network structure with a more complicated objective function. Furthermore, by employing two inverse networks, a cycle-consistent architecture is formed at both stages. The proposed model is trained on the ImageNet dataset, and the achieved outcomes demonstrate exceptional performance comparing with the state-of-the-art models.

Cooperative relaying can be introduced as a promising approach for data communication in the Internet of Things (IoT), where the source and the destination may be placed far away. In this paper, by taking a variety of realistic hardware imperfections (HWIs) and channels estimation errors (CEEs) into account, the secrecy performance of a three-hop cooperative network with a source, a destination and two consecutive amplify-and-forward (AF) relays is investigated. The relays are considered to be untrusted, i.e., while they are mandatory helpers for data transmission, they may overhear the received signals. We adopt the artificial noise injection scheme, to keep the source message secret from being captured by the untrusted relays. Given this system model, a novel closed-form expression is obtained in the high signal-to-noise ratio (SNR) regime for the ergodic secrecy rate (ESR) performance over Nakagami-m fading. Our simulation results highlight that the secrecy performance of the system is improved when the tolerable HWIs are distributed beneficially across the transmission and reception radio-frequency (RF) front-ends of each node. Our work reveals that unlike the ideal case, the realistic scenario of non-ideal hardware with CEEs faces with the secrecy rate ceiling. Finally, under a constraint on the total energy consumption which is applicable for battery-limited IoT equipment, we maximize the achievable secrecy rate. Our results highlight the importance of the destination's jamming cooperation and the first relay's role on the secrecy performance.

In this paper, we propose a light-weight jamming-resistant scheme for the Internet-of-Things (IoT) in 5G networks to ensure high-quality communication in a two-hop cooperative network. In the considered system model, a source communicates with a destination in the presence of an untrusted relay and a powerful multi-antenna adversary jammer. The untrusted relay is an authorized necessary helper who may wiretap the confidential information. Meanwhile, the jammer is an external attacker who tries to damage both the training and transmission phases. Different from traditional frequency hopping spread spectrum (FHSS) techniques that require a pre-determined pattern between communicating nodes, in our scheme, the source and destination enjoy the local observations of the two-hop channels. Then they exploit the measured channel as the source of common randomness to generate shared secret keys. By collecting multiple time slots into a frame, the sequence of channels observed in each frame is utilized to specify the adopted FHSS sequence in the next frame. Based on the derived FHSS sequence from the key generation phase, the source starts to transmit its message supporting by the the destination-assisted cooperative jamming (DACJ) technique which prevents the untrusted relay from discovering the secret message. For the mentioned system model, we present new closed-form expressions for characterizing the achievable secret key rate (SKR) and ergodic secrecy rate (ESR) to highlight the efficiency of our proposed scheme compared to the state-of-the-art. We next determine the optimal power allocation (OPA) between the pilot and data transmission phases that maximizes the ESR performance while escaping from jamming attack. Finally, several numerical examples and discussions are presented to gain engineering insights behind the studied communication scenario.

It is shown that the detection performance can be significantly improved using the recent technology of multiple‐input multiple‐output (MIMO) radar systems. This is a result of the spatial diversity in such systems due to the viewing of the target from different angles. On the other hand, the moving target indication (MTI) processing has long been known and applied in the traditional pulse radars to detect weak moving targets in the presence of strong clutter signals. The authors propose a procedure based on the compressive sensing idea, in order to apply the MTI processing in a MIMO radar with widely separated antennas. Although a clutter is included in the signal model and a different radar cross‐section value for each transmitter–receiver pair is considered which makes the problem more complex, the complexity dimension is preserved as low as possible by converting the block sparse problem into a regular sparse problem.

Lattice codes are known to outperform random codes for certain networks, especially in the Gaussian two-way relay channels (GTWRCs) where lattice codes are able to exploit their linearity. As an extension of the GTWRC, in this paper, we consider the asymmetric Gaussian two-way line network where two nodes exchange their messages through multiple relays. We first investigate the capacity region of the full-duplex two-way two-relay line network. The results can be extended to an arbitrary number of relays and to half-duplex scenarios. This channel consists of four nodes: 1 ↔ 2 ↔ 3 ↔ 4, where nodes 1 and 4 with the help of two full-duplex relays, i.e., nodes 2 and 3, exchange their messages with each other. Using lattice codes, we design a novel scheme that allows the relay nodes to send the data in both directions simultaneously under an asymmetric rate region. In the proposed scheme, each relay decodes the sum of lattice points and then re-encodes it into another lattice codeword (which satisfies the transmit power constraint at the relay). It is shown that the proposed scheme achieves the capacity region of asymmetric two-way line network within 0.5 bit independent of the number of relays.

This paper considers a many‐to‐one Gaussian interference channel with a fusion centre (FC) where there is a K ‐user interference channel in which only one relay (receiver) faces interference while the remaining K‐1 receivers are interference free. All the relays communicate information about their observed sequence to the FC through noiseless links at communication rate R 0 . First, by analysing traditional relaying schemes, i.e., Decode and Forward, Compress and Forward and Compute and Forward, three rate‐regions for this setting are derived. Then, based on nested lattice codes, a new achievable rate‐region is provided. Based on the proposed scheme, one can design a transmission scheme that can recover both integer and non‐integer linear combination of messages. Numerical examples show that if channel gains are integer, the proposed scheme performs similarly to the compute‐and‐forward scheme. In the case of non‐integer channel gains, the proposed scheme outperforms other relaying schemes at high signal‐to‐noise ratios. Finally, it is shown that if the channel gains are larger than one and if the rate of each relay‐to‐FC link equals to the capacity of an AWGN channel, then the proposed scheme can achieve the capacity region in high SNR regime.

In this paper, we consider the Ice-Wine problem: Two transmitters send their messages over the Gaussian Multiple-Access Channel (MAC) and a receiver aims to recover a linear combination of codewords. The best known achievable rate-region for this problem is due to [1], [2] as Ri≤ ½ log (½ + SNR) (i = 1, 2). In this paper, we design a novel scheme using lattice codes and show that the rate region of this problem can be improved. The main difference between our proposed scheme with known schemes in [1], [2] is that instead of recovering the sum of codewords at the decoder, a non-integer linear combination of codewords is recovered. Comparing the achievable rate-region with the outer bound, Ri≤ ½ log (1 + SNR) (i = 1, 2), we observe that the achievable rate for each user is partially tight. Finally, by applying our proposed scheme to the Gaussian Two Way Relay Channel (GTWRC), we show that the best rate region for this problem can be improved.

In this paper, we study transmission of a memoryless Laplacian source over three types of channels: additive white Laplacian noise (AWLN), additive white Gaussian noise (AWGN), and slow flat-fading Rayleigh channels under both bandwidth compression and bandwidth expansion. For this purpose, we analyze two well-known hybrid digital-analog (HDA) joint source-channel coding schemes for bandwidth compression and one for bandwidth expansion. Then we obtain achievable (absolute-error) distortion regions of the HDA schemes for the matched signal-to-noise ratio (SNR) case as well as the mismatched SNR scenario. Using numerical examples, it is shown that these schemes can achieve a distortion very close to the provided lower bound (for the AWLN channel) and to the optimum performance theoretically attainable bound (for AWGN and Rayleigh fading channels) on mean-absolute error distortion under matched SNR conditions. In addition, a non-linear analog coding scheme is analyzed, and its performance is compared to the HDA schemes for bandwidth compression under both matched and mismatched SNR scenarios. The results show that the HDA schemes outperform the non-linear analog coding over the whole CSNR region.

Deterministic approach of models the interaction between the bits that are received at the same signal level by the modulo 2 sum of the bits where the carry-overs that would happen with real addition are ignored. By this model in a multi-user setting, the receiver can distinguish most significant bits (MSBs) of the stronger user without any noise. A faithful implementation of the deterministic model requires one to “neutralize interference” from previous carry over digits. This paper proposes a new implementation of “interference neutralization” [2] using structured lattice codes. We first present our implementation strategy and then, as an application, apply this strategy to a symmetric half-duplex Gaussian butterfly network. In this network, two transmitters communicate their information to two destinations via a half-duplex relay. For duplexing factor 0.5 and under certain conditions, we show that our proposed scheme based on superposition of nested lattice codes can achieve the capacity region in high SNR. Also, regardless of all channel parameters, the gap between its achievable rate region and the outer bound is at most 0.293 bits/sec/Hz.

In this paper, we propose a modified joint source-channel coding (JSCC) scheme on the transmission of an analog Gaussian source over an additive white Gaussian noise (AWGN) channel in the presence of an interference, correlated with the source. This setting naturally generalizes the problem of sending a single Gaussian source over an AWGN channel, in the case of bandwidth-matched, and with uncorrelated interference in which separation-based scheme with Costa coding is optimal. We analyze the modifeied scheme to obtain achievable (mean-squared error) distortion-power tradeoff. For comparison, we also obtain a new outer bound for the achievable distortion-power tradeoff. Using numerical simulations, we demonstrate that, a two layered coding scheme consisting of analog and digital Costa coding layers, performs well compared to other provided JSCC schemes in the literature [1], [2].

In this work, we consider an additive state-dependent Gaussian interference channel (ASD-GIC) where there is a common additive interference, known to both transmitters but not to the receivers. First, we obtain an outer bound on the sum-capacity for the asymptotic case where the interference is assumed to be Gaussian variable with unbounded variance. This asymptotic case has been referred to as the “strong interference” in [1]. By applying a lattice-based coding scheme, an achievable rate-region is provided. Under some conditions, which depend on the noise variance and channel power constraints, this achievable sum-rate region meets the outer bound and therefore lattice codes can achieve the sum-capacity of an ASD-GIC in the asymptotic case.

In this paper, we consider transmission of a Gaussian source over a Gaussian channel under bandwidth compression in the presence of interference known only to the transmitter. We study hybrid digital-analog (HDA) joint source-channel coding schemes and propose two novel coding schemes that achieve the optimal mean-squared error (MSE) distortion. This can be viewed as the extension of results by Wilson et al. [1], originally proposed for sending a Gaussian source over a Gaussian channel in two cases: 1) Matched bandwidth with known interference only at the transmitter, 2) bandwidth compression where there is no interference in the channel. The proposed HDA codes can cancel the interference of the channel and obtain the “optimum performance theoretically attainable” (OPTA) of the AWGN channel with no interference in the case of bandwidth compression. We also provide performance analysis in the presence of signal-to-noise ratio (SNR) mismatch where we expect that HDA schemes perform better than strictly digital schemes.

We consider the problem of sending a bivariate Gaussian source S=(S1,S2) across a power-limited two-user Gaussian broadcast channel. User i (i=1,2) observes the transmitted signal corrupted by Gaussian noise with power σi2and desires to estimate Si. We study hybrid digital-analog (HDA) joint source-channel coding schemes and analyze the region of (squared-error) distortion pairs that are simultaneously achievable. Two cases are considered: 1) broadcasting with bandwidth compression, and 2) broadcasting with bandwidth expansion. We modify and adapt HDA schemes of Wilson et al. and Prabhakaran et al. , originally proposed for broadcasting a single common Gaussian source, in order to provide achievable distortion regions for broadcasting correlated Gaussian sources. For comparison, we also extend the outer bound of Soundararajan et al. from the matched source-channel bandwidth case to the bandwidth mismatch case.

We consider the transmission of a bivariate Gaussian source S = (S1, S2) across a power-limited two-user Gaussian broadcast channel. User i (i = 1, 2) observes the transmitted signal corrupted by Gaussian noise with power sigmai2and wants to estimate Si. We study hybrid digital-analog (HDA) joint source-channel coding schemes and analyze these schemes to obtain achievable (squared-error) distortion regions. Two cases are considered: 1) source and channel bandwidths are equal, 2) broadcasting with bandwidth compression. We adapt HDA schemes of Wilson et al. and Prabhakaran et al. to provide various achievable distortion regions for both cases. Using numerical examples, we demonstrate that for bandwidth compression, a three-layered coding scheme consisting of analog, superposition, and Costa coding performs well compared to the other provided HDA schemes. In the case of matched bandwidth, a three-layered coding scheme with an analog layer and two layers, each consisting of a Wyner-Ziv coder followed by a Costa coder, performs best.

We consider a specific type of data gathering sensor networks that can be modeled by a binary chief executive officer problem. We apply turbo codes to encode sensors observations and transmit them to a fusion center over independent binary symmetric channels. It is shown in the literature that the fusion center can exploit the correlation between sensors observations to design a soft-input soft-output (SISO) global decoder. Then the fusion center iterates extrinsic information between the global decoder and the SISO decoder of the applied error correcting code to jointly estimate the source. Since we consider turbo codes, the joint decoding problem is generalized to the problem of exchanging extrinsic information between three SISO modules. In this paper, we first apply the sum-product algorithm to derive the rules that update extrinsic information for the global decoder. Then, we apply extended-serial decoding that is the best known structure for decoders consisting of three concatenated SISO modules. We compare the bit error rate achieved by extended-serial decoding with the one achieved by a separate decoding strategy, where the fusion center separately decodes each sensor's observation and then decides based on the majority of the outputs. Our simulations show that extended-serial decoding performs significantly better than separate decoding.

We present necessary and sufficient conditions, similar to the recent results of Gastpar (2007), for the achievability of all power-distortion tuples (P,D) = (P1, P2, middot middot middot , PL,D) in an asymmetric Gaussian sensor network where L distributed sensors transmit noisy observations of a Gaussian source through a Gaussian multiple access channel to a fusion center. We show numerically that in general the gap between the provided upper bound and the lower bound of the distortion D is small. We also provide an optimal power allocation that minimizes the total power consumption, Pmacr = Sigmai=1LPi, for uncoded transmission scheme while satisfying a given distortion constraint D. Numerical evaluations show that by applying the optimal power allocation uncoded transmission can perform nearly optimal in an asymmetric sensor network subject to a sum-power constraint. In the symmetric case both bounds agree and provide the optimal power-distortion tradeoff (P,D); this agrees with result of (M. Gastpar, 2007). Thus, in the sense of achieving the optimal (P,D) tradeoff, uncoded transmission is optimal in the symmetric case and can be nearly-optimal in the asymmetric case.

We consider the Chief Executive Officer (CEO) problem in which agents encode their observations without collaborating with each other and send through rate constrained noiseless channels to a fusion center (FC).We apply the successive coding strategy into this problem and determine the closed-form expressions for optimal rates in order to achieve the minimum distortion under a sum-rate constraint. We show that the optimal sum-rate distortion performance for the Gaussian CEO problem is achievable using the successive coding strategy which is inherently a low complexity approach of obtaining a prescribed distortion. We also determine the optimal rate allocation region for the successively structured Gaussian CEO problem.

Abstract We consider themany‐help‐oneproblem, also calledm‐helperproblem, for the special case ofm = 1 where one source provides partial side information to the fusion center (FC) to help reconstruction of the other correlated source. Both correlated sources communicate information about their observations to the FC through an orthogonal multiple access channel (MAC) without cooperating with each other. First, we characterize the optimal tradeoff between the transmission cost, that is, power, and the distortion D. Then, we consider a joint optimization of source coding and power scheduling from an information theory perspective, where the power scheduling is verified using Shannon capacity formula and the source‐coding problem is analyzed using rate‐distortion theory. We show that the joint optimization in the Gaussianone‐helperproblem can be solved analytically. We provide closed‐form expressions for the optimal distortion and the optimal power scheduling in terms of the cost weights. Copyright © 2008 John Wiley & Sons, Ltd.

We consider the estimation of a Gaussian source by a Gaussian sensor network where L distributed sensors transmit noisy observations of the source through a fading Gaussian multiple access channel (MAC) to a fusion center (FC). Since sensor power is usually limited, our goal is to characterize the optimal tradeoff between the transmission cost, i.e., the power vector P = (P1, P2, ..., PL), and the average estimation distortion, D. We focus on asymmetric fading sensor networks in which the sensors have differing signal to noise ratios and transmission powers. We present necessary and sufficient conditions for the achievability of (L + 1)-tuples (P1, P2, ..., PL, D). For a symmetric Gaussian sensor network with deterministic and equal-magnitude fading, we derive the optimal power-distortion tradeoff. We also provide an achievable power-distortion region for the asymmetric sensor network with deterministic fading by analyzing the transmission of scaled versions of vector-quantized observations. We show that some of the power-distortion tuples achievable by this scheme are not achievable via an uncoded system.

We derive expressions for the achievable rate region of a collaborative coding scheme in a two-transmitter, two-receiver pairwise collaborative network (PCN) where one transmitter and receiver pair, namely relay pair, assists the other pair, namely the source pair, by partially decoding and forwarding the transmitted message to the intended receiver. The relay pair provides such assistance while handling a private message. We assume that users can use the past channel outputs and can transmit and receive at the same time and in the same frequency band. In this collaborative scheme, the transmitter of the source pair splits its information into two independent parts. Ironically, the relay pair employs the decode and forward coding to assist the source pair in delivering a part of its message and re-encodes the decoded message along with private message, which is intended to the receiver of the relay pair, and broadcasts the results. The receiver of the relay pair decodes both messages, retrieves the private message, re-encodes and transmits the decoded massage to the intended destination. We also characterize the achievable rate region for Gaussian PCN. Finally, we provide numerical results to study the rate trade off for the involved pairs. Numerical result shows that the collaboration offers gain when the channel gain between the users of the relay pair are strong. It also shows that if the channel conditions between transmitters or between the receivers of the relay and source pairs are poor, such a collaboration is not beneficial.

We consider the problem of joint decoding and data-fusion in data gathering sensor networks modeled by the Chief Executive Officer (CEO) problem. Correlation between sensorspsila data is known at the fusion center and is employed to update extrinsic information received from soft-in soft-out (SISO) decoders. It is shown in the literature that this scheme has a lower bit error rate compared with the schemes that separately decode data received from each sensor and then estimate the value of the source. Previous works consider correlated Gaussian sources and apply a single SISO decoder. We consider the binary CEO problem, where all sensors observe the same binary source corrupted by independent binary noises, and apply turbo codes to encode and transmit them to the fusion center. We show how extrinsic information is passed between SISO decoders and the vertical-decoding unit that updates extrinsic information using channel correlations. We illustrate the performance of the joint decoder for different correlations and rates. Simulation results show promising improvements compared with the separate decoding scheme. We also compare the bit error rates achieved by turbo codes with the ones achieved by convolutional codes and discuss the results.

We consider the joint power-distortion optimization in one-help-one problem, also called the 1-helper problem where one source provides partial side information to the fusion center (FC) to help reconstruction of the other correlated source. Both correlated sources communicate information about their observations to the FC through an orthogonal multiple access channel (MAC) without cooperating with each other. We investigate the joint optimization of source coding and power allocation from an information theory perspective, where the power allocation is verified using Shannon capacity formula and the source coding problem is analyzed using rate-distortion theory. We show that the joint optimization in the Gaussian 1-helper problem can be solved analytically. We provide closed-form expressions for the optimal distortion and the optimal power allocation in terms of the cost weights.

We consider 1-helper problem in which one source provides partial side information to the fusion center (FC) to help reconstruction of the main source signal. Both sources communicate information about their observations to the FC through an additive white Gaussian multiple access channel (MAC) without cooperating with each other. Two types of MAC are considered: orthogonal MAC and interfering (non-orthogonal) MAC. We characterize the tradeoff between the transmission cost, i.e., power, and the estimation distortion, D, using Shannon's separation source and channel coding theorem. We demonstrate that the separation-based coding strategy outperforms the uncoded transmission under an orthogonal MAC. However, in the symmetric case under an interfering MAC, below a certain signal- to-noise ratio (SNR) threshold, uncoded transmission outperforms the separation-based scheme. The threshold can be determined in terms of the correlation coefficient between the sources, p, and in fact is an increasing function of p. Finally, the optimal power scheduling to minimize the total power consumption in the network is derived.

We consider a distributed sensor network, modeled by the CEO problem, in which each sensor communicates its observation to the fusion center (FC) using limited transmission rate. Based on the successive coding strategy, we obtain the optimal rate allocation strategy for the Gaussian CEO problem which minimizes the average distortion in the source estimate produced by the FC. This strategy can be simplified in a general parallel sensor network withLsensors by assigning equal rates to sensors if the sum-rateRmacris very large given a fixedLor ifLis very large given a fixedRmacr.

Summary form only given. The side information aware coding strategy was employed to address the quadratic Gaussian chief executive officer (CEO) problem. By using this strategy, the CEO problem is decomposed into a sequence of data fusion encoding and decoding blocks. We obtain the optimal rate allocation scheme that achieves the minimum distortion under a sum-rate constraint. We also derive the optimal sum-rate distortion tradeoff based on the successive coding strategy and show that our result is indeed the sum-rate distortion function for the quadratic Gaussian CEO problem. Hence, the optimal sum-rate distortion performance for the CEO problem is achievable using the successive coding strategy which is essentially a less complex way to attain a prescribed distortion

We consider a distributed sensor network, modeled by the Chief Executive Officer (CEO) problem, in which sensors encode their observations without collaborating with each other and send through rate constrained noiseless channels to a fusion center (FC). We use the successive Wyner-Ziv coding strategy in this problem where sensors have differing quality of observations. We determine the optimal rate allocation scheme to obtain the minimum distortion under a sum-rate constraint. We show that the optimal sum-rate distortion performance for the Gaussian CEO problem is achievable using the successive coding strategy which is inherently a less complex way of obtaining a prescribed distortion. We also determine the achievable rate region and the optimal rate allocation region for the Gaussian CEO problem. We show that if the number of sensors tends to infinity while the sum-rate is finite, the performance of the successive coding strategy with equal rate sensors converges to the rate-distortion function. The same is true when the sum-rate tends to infinity with a finite number of sensors. Finally, we obtain the communication throughput of a K-relay network based on our results for the CEO problem.

In this paper, we investigate the power-distortion performance of the successive coding strategy in the so-called quadratic Gaussian CEO problem. In the CEO problem, L sensors will be deployed to observe independently corrupted versions of the source. They communicate information about their observations to the CEO through a Gaussian multiple access channel (MAC) without cooperating with each other. Two types of MAC are considered: orthogonal MAC and interfering (non-orthogonal) MAC. We address the problem from an information theoretic perspective and obtain the optimal tradeoff between the transmission cost, i.e., power, and the distortion D using Shannon's source-channel separation theorem. We also determine the optimal power allocation scheme based on the successive coding strategy to minimize the total power consumption in the sensor network

We evaluate the performance of the successive coding strategy for the problem of multiterminal lossy coding of correlated Gaussian sources. We consider the m-helper problem for the special case of m = 1 where one source provides partial side information to the decoder to help reconstruction of the main source signal. Our results reconfirm the fact that the successive coding strategy is an optimal strategy in sense of achieving the rate-distortion function of the 1-helper problem. Comparing the performance of the sequential coding with the performance of the successive coding, we show that there is no sum rate loss when the side information is not available at the encoder. Finally, based on the successive coding strategy, we provide an achievable rate-distortion region for the m-helper problem

In this paper, we consider a distributed sensor network in which sensors communicate their observations to a fusion center (FC) using limited transmission rate. Specifically, we investigate the case where the encoders are partially cooperating; they are connected by communication links with finite capacities. Hence, before the encoders encode and transmit their data, they exchange information to increase the reliability of their information. We address the problem from an information theoretic perspective and determine a lower bound for the sum- rate distortion function of the cooperative CEO problem. We also derive a lower bound for the rate-region of the problem.

We consider the m-helper problem for the special case of m = 1 where one source provides partial side information to the fusion center (FC) to help reconstruction of the main source signal. Both sources communicate information about their observations to the FC through an orthogonal multiple access channel (MAC) without cooperating with each other. We characterize the optimal tradeoff between the transmission cost, i.e., power, and the distortion D using Shannon's separation source and channel coding theorem. We show that the separation approach outperforms the analog forwarding approach in the 1- helper problem. We also determine the optimal power scheduling to minimize the total power consumption in the network.

We consider a distributed sensor network in which sensors communicate their observations to the CEO using limited transmission rate. We use successive coding strategy of S. C. Draper and G. W. Wornell (2004) and obtain the optimal distortion sum-rate tradeoff for L sensors with different noise levels. Our result is an extension of the result of S. C. Draper and G. W. Wornell (2004), where the optimal distortion sum-rate tradeoff for two equal-SNR sensors is derived. As the number of sensors increases, the achievable distortion decreases since the CEO accumulates more data and can obtain a better estimate of the source. The fraction of the total rate allocated to each sensor is approximately 1/L if the average rate per sensor node gets small or if the sum-rate _R is very large for a fixed L. Thus, we can simplify rate allocation problem in a general parallel sensor network with L sensors by assigning equal rates to sensors. We show that this scheme may not cause a large extra distortion compared with the minimum achievable distortion. Finally, we obtain a lower bound for the minimum achievable distortion in the Gaussian sensor network.

In this paper, we investigate the distortion sum-rate performance of the successive coding strategy in the so-called quadratic Gaussian CEO problem. In the CEO problem, the central unit or the CEO desires to obtain an optimal estimate of the source signal. Since the source cannot be observed directly, L sensors will be deployed to observe independently corrupted versions of the source. They communicate information about their observations to the CEO through rate constrained noiseless channels without cooperating with each other. We consider a distributed sensor network consisting of two sensors with different noise levels and derive the minimum achievable distortion under a sum-rate constraint using the successive coding strategy of S.C. Draper and G.W. Wornell (2004). We also demonstrate that the best way to achieve minimum distortion under a sum-rate constraint is to allocate more rate to the sensor with higher quality of observation in a generalized water-filling manner. The fractional rate allocation is approximately 1/2 if the sum-rate lowbarR is large. Thus, we can simplify rate allocation problem in a general parallel sensor network with L sensors by assigning equal rates to sensors, provided the average rate per sensor node is large. We show that this scheme may not cause a large extra distortion compared with the minimum achievable distortion. Finally, we consider the problem of combining source and channel coding in sensor networks. Two paradigms are considered, Shannon's separation paradigm and joint source-channel coding paradigm. We obtain the distortion-power tradeoffs for both coding paradigms in the Gaussian sensor network with multiple access channel

In this paper, we consider the application of low-density parity-check (LDPC) codes in multi-carrier frequency-hopping (MC-FH) CDMA systems. We evaluate the performance of the coded system in a slowly Rayleigh fading frequency-selective channel using different construction methods of regular LDPC codes. We then compare the results with those of the super-orthogonal coded system. Our simulation results show that the LDPC coded scheme significantly outperforms the uncoded and the super-orthogonal coded scheme. Finally, we propose a new semi-random construction of regular LDPC code and evaluate its performance in MC-FH-CDMA system. Our numerical results indicate that this new construction outperforms other regular constructions of LDPC codes.