Secure beamforming in reconfigurable intelligent surface (RIS)-assisted multiuser downlink systems is challenging due to high computational complexity and complex channel state information (CSI) estimation. This work proposes a pilot-driven beamforming network (PilotBeamNet) that jointly designs base-station (BS) transmit beamforming and quantized RIS phases directly from uplink pilot received signals with legitimate-user location cues to capture geometry. The framework avoids explicit channel estimation and slow iterative algorithms. A convolutional module reads each pilot frame, a long short term memory (LSTM) block with lightweight temporal attention aggregates them, and two simple heads output the beamformers and the discrete RIS phases. The location cues are embedded and fused with the features extracted from the pilot frames by the convolutional, LSTM, and temporal attention modules. Training maximizes ergodic secrecy rate (ESR) through Monte Carlo sampling of unknown eavesdropper channels, enabling robustness without requiring eavesdropper CSI. Once trained, PilotBeamNet performs single-pass inference with latency determined only by network depth. Across all tested conditions, PilotBeamNet achieves 10%–30% ESR improvement depending on the signal-to-noise ratio (SNR), pilot length, and RIS size, while reducing inference latency by more than an order of magnitude compared to alternating optimization (AO) and outperforming multilayer perceptron (MLP) baselines. It also maintains consistent performance under phase quantization and delivers higher secrecy rates across all evaluated configurations. • PilotBeamNet enables end-to-end secure RIS beamforming from uplink pilots without explicit channel estimation. • CNN-LSTM-attention predicts BS beamforming and quantized RIS phases with low latency. • Outperforms AO/MLP across SNRs and RIS sizes and remains robust to quantization.

Cell-free massive multiple-input multiple-output (CF-mMIMO) is a promising architecture for 6G wireless networks through distributed access point (AP) cooperation. In large-scale deployments where user demand exceeds system capacity, effective user admission control (UAC) is essential to select users while meeting quality-of-service (QoS) requirements. The UAC problem in CF-mMIMO is inherently challenging, involving both discrete user selection and continuous power allocation variables. To address this challenge, we propose a Graphormer-enhanced Monte Carlo Tree Search (GE-MCTS) framework that integrates a Graphormer-based neural network (NN) with Monte Carlo Tree Search (MCTS). This framework leverages the Graphormer’s capability to model the graph-structured AP–user topology and MCTS’s planning proficiency to efficiently explore the vast decision space. Furthermore, to accommodate users initially unadmitted due to system constraints, we introduce a complementary AP deployment problem. By adapting the GE-MCTS framework, we optimize the placement of additional APs to achieve full user admission with the minimal number of new APs required. Simulation results demonstrate the effectiveness of our proposed framework. For UAC, with low computational complexity, GE-MCTS consistently admits 26.3–41.7% more users compared to baseline methods across various network scales. For AP deployment, our framework requires 45–73% fewer additional APs to achieve full user admission, highlighting its efficiency and scalability.

Split learning (SL) aims to protect user data privacy by distributing deep models between the client-server and keeping private data locally. In SL training with multiple clients, the local model weights are shared among the clients for the local model updates. This paper first reveals data privacy leakage exacerbated by local weight sharing among the clients in SL through model inversion attacks. Then, to reduce the data privacy leakage issue, we propose and analyze privacy-enhanced SL (P-SL) (or SL without local weight sharing). We further propose a parallelized P-SL to expedite the training process by duplicating multiple server-side model instances without compromising accuracy. Finally, we explore P-SL with late participating clients and devise a server-side cache-based training method to address the forgetting phenomenon in SL when late clients join. The experiment results demonstrate that P-SL helps reduce up to$50\%$of client-side data leakage, which essentially achieves a better privacy-accuracy trade-off than the current trend by using differential privacy mechanisms. Moreover, P-SL and its cache-based version achieve comparable accuracy to baseline SL under various data distributions, while incurring lower costs for computation and communication. Additionally, caching-based training in P-SL mitigates the negative effect of forgetting, stabilizes learning, and enables practical and low-complexity training in a dynamic environment with late-arriving clients.

Creating and maintaining the Metaverse requires enormous resources that have never been seen before, especially computing resources for intensive data processing to support the Extended Reality, enormous storage resources, and massive networking resources for maintaining ultra high-speed and low-latency connections. Therefore, this work aims to propose a novel framework, namely MetaSlicing, that can provide a highly effective and comprehensive solution in managing and allocating different types of resources for Metaverse applications. In particular, by observing that Metaverse applications may have common functions, we first propose grouping applications into clusters, called MetaInstances. In a MetaInstance, common functions can be shared among applications. As such, the same resources can be used by multiple applications simultaneously, thereby enhancing resource utilization dramatically. To address the real-time characteristic and resource demand's dynamic and uncertainty in the Metaverse, we develop an effective framework based on the semi-Markov decision process and propose an intelligent admission control algorithm that can maximize resource utilization and enhance the Quality-of-Service for end-users. Extensive simulation results show that our proposed solution outperforms the Greedy-based policies by up to 80% and 47% in terms of long-term revenue for Metaverse providers and request acceptance probability, respectively.

By executing offloaded tasks from mobile users, edge computing augments mobile devices with computing/communications resources from edge nodes (ENs), thus enabling new services/applications (e.g., real-time gaming, virtual/augmented reality). However, despite being more resourceful than mobile devices, allocating ENs' computing/communications resources to a given favorable set of users (e.g., closer to edge nodes) may block other devices from their services. This is often the case for most existing task offloading and resource allocation approaches that only aim to maximize the network social welfare or minimize the total energy consumption but do not consider the computing/battery status of each mobile device. This work develops an energy-based proportionally fair task offloading and resource allocation framework for a multi-layer cooperative edge computing network to serve all user equipments (UEs) while considering both their service requirements and individual energy/battery levels. The resulting optimization involves both binary (offloading decisions) and continuous (resource allocation) variables. To tackle the NP-hard mixed integer optimization problem, we leverage the fact that the relaxed problem is convex and propose a distributed algorithm, namely the dynamic branch-and-bound Benders decomposition (DBBD). DBBD decomposes the original problem into a master problem (MP) for the offloading decisions and multiple subproblems (SPs) for resource allocation. To quickly eliminate inefficient offloading solutions, the MP is integrated with powerful Benders cuts exploiting the ENs' resource constraints. We then develop a dynamic branch-and-bound algorithm (DBB) to efficiently solve the MP considering the load balance among ENs. The SPs can either be solved for their closed-form solutions or be solved in parallel at ENs, thus reducing the complexity. The numerical results show that the DBBD returns the optimal solution in maximizing the proportional fairness among UEs. The DBBD has higher fairness indexes, i.e., Jain's index and min-max ratio, in comparison with the existing ones that minimize the total consumed energy.

Multivariate time-series anomaly detection is critically important in many applications, including retail, transportation, power grid, and water treatment plants. Existing approaches for this problem mostly employ either statistical models which cannot capture the nonlinear relations well or conventional deep learning (DL) models e.g., convolutional neural network (CNN) and long short-term memory (LSTM) that do not explicitly learn the pairwise correlations among variables. To overcome these limitations, we propose a novel method, correlation-aware spatial-temporal graph learning (termed ), for time-series anomaly detection. explicitly captures the pairwise correlations via a correlation learning (MTCL) module based on which a spatial-temporal graph neural network (STGNN) can be developed. Then, by employing a graph convolution network (GCN) that exploits one-and multihop neighbor information, our STGNN component can encode rich spatial information from complex pairwise dependencies between variables. With a temporal module that consists of dilated convolutional functions, the STGNN can further capture long-range dependence over time. A novel anomaly scoring component is further integrated into to estimate the degree of an anomaly in a purely unsupervised manner. Experimental results demonstrate that can detect and diagnose anomalies effectively in general settings as well as enable early detection across different time delays. Our code is available at https://github.com/huankoh/CST-GL.

This article introduces a novel lightweight framework using ambient backscattering communications to counter eavesdroppers. In particular, our framework divides an original message into two parts. The first part, i.e., the active-transmit message, is transmitted by the transmitter using conventional RF signals. Simultaneously, the second part, i.e., the backscatter message, is transmitted by an ambient backscatter tag that backscatters upon the active signals emitted by the transmitter. Notably, the backscatter tag does not generate its own signal, making it difficult for an eavesdropper to detect the backscattered signals unless they have prior knowledge of the system. Here, we assume that without decoding/knowing the backscatter message, the eavesdropper is unable to decode the original message. Even in scenarios where the eavesdropper can capture both messages, reconstructing the original message is a complex task without understanding the intricacies of the message-splitting mechanism. A challenge in our proposed framework is to effectively decode the backscattered signals at the receiver, often accomplished using the maximum likelihood (MLK) approach. However, such a method may require a complex mathematical model together with perfect channel state information (CSI). To address this issue, we develop a novel deep meta-learning-based signal detector that can not only effectively decode the weak backscattered signals without requiring perfect CSI but also quickly adapt to a new wireless environment with very little knowledge. Simulation results show that our proposed learning approach, without requiring perfect CSI and complex mathematical model, can achieve a bit error ratio close to that of the MLK-based approach. They also clearly show the efficiency of the proposed approach in dealing with eavesdropping attacks and the lack of training data for deep learning models in practical scenarios.

This work proposes a novel framework to dynamically and effectively manage and allocate different types of resources for Metaverse applications, which are forecasted to demand massive resources of various types that have never been seen before. Specifically, by studying functions of Metaverse applications, we first propose an effective solution to divide applications into groups, namely MetaInstances, where common functions can be shared among applications to enhance resource usage efficiency. Then, to capture the real-time, dynamic, and uncertain characteristics of request arrival and application departure processes, we develop a semi-Markov decision process-based framework and propose an intelligent algorithm that can gradually learn the optimal admission policy to maximize the revenue and resource usage efficiency for the Metaverse service provider and at the same time enhance the Quality-of-Service for Metaverse users. Extensive simulation results show that our proposed approach can achieve up to 120% greater revenue for the Metaverse service providers and up to 178.9% higher acceptance probability for Metaverse application requests than those of other baselines.

Integrated communications and sensing (ICS) has recently emerged as an enabling technology for ubiquitous sensing and IoT applications. For ICS application to autonomous vehicles (AVs), optimizing the waveform structure is one of the most challenging tasks due to strong influences between sensing and data communication functions. Specifically, the preamble of a data communication frame is typically leveraged for the sensing function. As such, the higher number of preambles in a coherent processing interval (CPI) is, the greater sensing task’s performance is. In contrast, communication efficiency is inversely proportional to the number of preambles. Moreover, surrounding radio environments are usually dynamic with high uncertainties due to their high mobility, making the ICS’s waveform optimization problem even more challenging. To that end, this article develops a novel ICS framework established on the Markov decision process and recent advanced techniques in deep reinforcement learning. By doing so, without requiring complete knowledge of the surrounding environment in advance, the ICS-AV can adaptively optimize its waveform structure (i.e., number of frames in the CPI) to maximize sensing and data communication performance under the surrounding environment’s dynamic and uncertainty. Extensive simulations show that our proposed approach can improve the joint communication and sensing performance up to 46.26% compared with other baseline methods.

Split learning (SL) enables data privacy preservation by allowing clients to collaboratively train a deep learning model with the server without sharing raw data. However, SL still has limitations such as potential data privacy leakage and high computation for clients. In this paper, we propose to binarize the SL local layers for faster computation (up to 17.5 times less forward-propagation time in both training and inference phases on mobile devices) and reduced memory usage (up to 32 times less memory and bandwidth requirements). More importantly, the binarized SL (B-SL) model can reduce privacy leakage from SL smashed data with merely a small degradation in model accuracy. To further enhance privacy preservation, we also propose two novel approaches: 1) training with additional local leak loss and 2) applying differential privacy, which could be integrated separately or concurrently into the B-SL model. Experimental results with different datasets have affirmed the benefits of the B-SL models compared with several benchmark models. The effectiveness of B-SL models against feature-space hijacking attack (FSHA) is also illustrated. Our results have demonstrated B-SL models are promising for lightweight IoT/mobile applications with high privacy-preservation requirements such as mobile healthcare applications.

Anomaly detection from graph data has drawn much attention due to its practical significance in many critical applications including cybersecurity, finance, and social networks. Existing data mining and machine learning methods are either shallow methods that could not effectively capture the complex interdependency of graph data or graph autoencoder methods that could not fully exploit the contextual information as supervision signals for effective anomaly detection. To overcome these challenges, in this paper, we propose a novel method, Self-Supervised Learning for Graph Anomaly Detection (SL-GAD). Our method constructs different contextual subgraphs (views) based on a target node and employs two modules,generative attribute regressionandmulti-view contrastive learningfor anomaly detection. While thegenerative attribute regressionmodule allows us to capture the anomalies in the attribute space, themulti-view contrastive learningmodule can exploit richer structure information from multiple subgraphs, thus abling to capture the anomalies in the structure space, mixing of structure, and attribute information. We conduct extensive experiments on six benchmark datasets and the results demonstrate that our method outperforms state-of-the-art methods by a large margin.

Coded distributed computing (CDC) has recently emerged to be a promising solution to address the straggling effects in conventional distributed computing systems. By assigning redundant workloads to the computing nodes, CDC can significantly enhance the performance of the whole system. However, since the core idea of CDC is to introduce redundancies to compensate for uncertainties, it may lead to a large amount of wasted energy at the edge nodes. It can be observed that the more redundant workload added, the less impact the straggling effects have on the system. However, at the same time, the more energy is needed to perform redundant tasks. In this work, we develop a novel framework, namely CERA, to elastically allocate computing resources for CDC processes. Particularly, CERA consists of two stages. In the first stage, we model a joint coding and node selection optimization problem to minimize the expected processing time for a CDC task. Since the problem is NP-hard, we propose a linearization approach and a hybrid algorithm to quickly obtain the optimal solutions. In the second stage, we develop a smart online approach based on Lyapunov optimization to dynamically turn off straggling nodes based on their actual performance. As a result, wasteful energy consumption can be significantly reduced with minimal impact on the total processing time. Simulations using real-world datasets have shown that our proposed approach can reduce the system’s total processing time by more than 200% compared to that of the state-of-the-art approach, even when the nodes’ actual performance is not known in advance. Moreover, the results have shown that CERA’s online optimization stage can reduce the energy consumption by up to 37.14% without affecting the total processing time.

By executing offloaded tasks from mobile users, edge computing augments mobile devices with computing/communications resources from edge nodes (ENs), enabling new services/applications (e.g., real-time gaming, virtual/augmented reality). However, despite being more resourceful than mobile devices, allocating ENs’ computing/communications resources to given favorable sets of users may block other devices from their service. This is often the case for most existing task offloading and resource allocation approaches that only aim to maximize the network social welfare (e.g., minimizing the total energy consumption) but not consider the computing/battery status of each mobile device. This work develops a proportional fair task offloading and resource allocation framework for a multi-layer cooperative edge computing network to serve all user equipment (UEs) while considering both their service requirements and individual energy/battery levels. The resulting optimization involves both binary (offloading decisions) and real variables (resource allocations), making it NP-hard. To tackle it, we leverage the fact that the relaxed problem is convex and propose a distributed algorithm, namely the dynamic branchand-bound Benders decomposition (DBBD). DBBD decomposes the original problem into a master problem (MP) for the offloading decision and subproblems (SPs) for resource allocation. The SPs can either find their closed-form solutions or be solved in parallel at ENs, thus help reduce the complexity. The numerical results show that the DBBD returns the optimal solution of the problem maximizing the fairness between UEs. The DBBD has higher fairness indexes, i.e., Jain’s index and min-max ratio, in comparing with the existing ones that minimize the total consumed energy.

The coronavirus of 2019 (COVID-19) was declared a global pandemic by World Health Organization in March 2020. Effective testing is crucial to slow the spread of the pandemic. Artificial intelligence and machine learning techniques can help COVID-19 detection using various clinical symptom data. While deep learning (DL) approach requiring centralized data is susceptible to a high risk of data privacy breaches, federated learning (FL) approach resting on decentralized data can preserve data privacy, a critical factor in the health domain. This paper reviews recent advances in applying DL and FL techniques for COVID-19 detection with a focus on the latter. A model FL implementation use case in health systems with a COVID-19 detection using chest X-ray image data sets is studied. We have also reviewed applications of previously published FL experiments for COVID-19 research to demonstrate the applicability of FL in tackling health research issues. Last, several challenges in FL implementation in the healthcare domain are discussed in terms of potential future work.

This article considers a dual-hop Internet of Things communications network where sensor nodes transmit data to a gateway either directly or via other nodes using dual-hop communications. Each node employs separate transmission buffers to store its own sensing data and data received from other nodes. End-to-end delay quality-of-service constraints in terms of the maximum acceptable delay-outage probabilities are imposed. We investigate energy-efficient adaptive resource allocation problems (i.e., joint link scheduling, rate, and power allocation) to support minimum data rates of the nodes. A novel approach is proposed exploiting asymptotic delay analysis to first determine the achieved delay exponents of the queue length tail distributions to satisfy the delay-outage constraints. Next, the relation between the delay exponents and resource allocation variables are derived. Last, the solutions to the resulting constrained optimization problems are obtained using the Lagrangian approach and convex optimization. Illustrative examples demonstrate the effects of the rate requirements and delay constraint stringency on the power consumption and routing configuration.

Orthogonal time frequency space (OTFS) modulation was shown to provide significant error performance advantages over orthogonal frequency division multiplexing (OFDM) over delay-Doppler channels. The channel impulse response is needed at the receiver to perform OTFS detection. In this work, we analyze OTFS-based channel estimation using a pilot symbol embedded in the data frame: the pilot symbol with a number of guard zero-symbols is suitably located on the delay-Doppler grid containing the information symbols. Different symbol arrangements are proposed depending on whether the channel has integer or fractional Doppler paths relative to an integer grid. The channel information is first estimated from a group of received symbols using a simple threshold method. The estimated information is then used for data detection within the same frame, via a message passing (MP) algorithm. Numerical results compare the error performance of the proposed schemes and the OTFS scheme with ideal channel estimation under similar spectral and energy efficiency. Moreover, our results show that OTFS with non-ideal channel estimation can still outperform OFDM with ideal channel estimation.

This paper develops novel transmission schemes to support secure dual-hop Alice-Ray-Bob relaying communication in the presence of a passive eavesdropper (Eve). Due to unknown eavesdropper channel conditions, data transmissions from Alice (to Ray) and from Ray (to Bob) are required to satisfy the secrecy constraint in terms of maximum acceptable secrecy outage probability (SOP). The throughput maximization problem is studied for two scenarios: 1) fixed (Alice and Ray) power allocation; and 2) adaptive power allocation. The resulting constrained optimization problems are solved using the Lagrangian approach. In each frame, either Alice or Ray or neither can be scheduled for transmission depending on the instantaneous main channel conditions. Numerical results demonstrate the effectiveness of the proposed schemes over the existing schemes under various secrecy constraint and signal-to-noise power ratio (SNR) regimes.

We elaborate on the recently proposed orthogonal time frequency space (OTFS) modulation technique, which provides significant advantages over orthogonal frequency division multiplexing (OFDM) in Doppler channels. We first derive the input-output relation describing OTFS modulation and demodulation (mod/demod) for delay-Doppler channels with arbitrary number of paths, with given delay and Doppler values. We then propose a low-complexity message passing (MP) detection algorithm, which is suitable for large-scale OTFS taking advantage of the inherent channel sparsity. Since the fractional Doppler paths (i.e., not exactly aligned with the Doppler taps) produce the inter Doppler interference (IDI), we adapt the MP detection algorithm to compensate for the effect of IDI in order to further improve performance. Simulations results illustrate the superior performance gains of OTFS over OFDM under various channel conditions.

In this paper, we develop novel transmission schemes for secure dual-hop Alice-Ray-Bob relaying communication over fading channels in the presence of a passive eavesdropper (Eve). To control the risk of secrecy outage under unknown eavesdropper channel conditions, we impose secrecy constraint in terms of maximum allowable secrecy outage probability. We study the throughput-optimal buffer-aided adaptive relaying problem for two scenarios: 1) fixed (Alice and Ray) power allocation and 2) adaptive power allocation. The resulting constrained optimization problems are solved using Lagrangian approach and convex optimization. In each frame, either Alice or Ray or neither is scheduled for transmission depending on the main (Alice-Ray and Ray-Bob) channel conditions. Since the transmission schemes can result in unboundedly large (queuing) delay at Ray's buffer, we next study the transmission schemes guaranteeing the bounded average delay. The optimal transmission problem is formulated as an infinite horizon average reward constrained Markov decision process. Subsequently, by relying on a novel state value function approach, we show that in each frame, the solution can be obtained by solving a concave maximization problem, taking into account both the main channel conditions and the buffer state. An online transmission algorithm is developed to iteratively update the state value function, which converges to the optimal solution without requiring a-priori statistical information on the fading channels. The simulation results demonstrate the effectiveness of the proposed schemes over benchmark schemes under various secrecy constraints and signal-to-noise power ratio regimes.

The recently proposed orthogonal time-frequency-space (OTFS) modulation technique was shown to provide significant error performance advantages over orthogonal frequency division multiplexing (OFDM) over delay-Doppler channels. In this paper, we first derive the explicit input-output relation describing OTFS modulation and demodulation (mod/demod). We then analyze the cases of: 1) ideal pulse-shaping waveforms that satisfy the bi-orthogonality conditions and 2) rectangular waveforms which do not. We show that while only inter-Doppler interference (IDI) is present in the former case, additional inter-carrier interference (ICI) and inter-symbol interference (ISI) occur in the latter case. We next characterize the interferences and develop a novel low-complexity yet efficient message passing (MP) algorithm for joint interference cancellation (IC) and symbol detection. While ICI and ISI are eliminated through appropriate phase shifting, IDI can be mitigated by adapting the MP algorithm to account for only the largest interference terms. The MP algorithm can effectively compensate for a wide range of channel Doppler spreads. Our results indicate that OTFS using practical rectangular waveforms can achieve the performance of OTFS using ideal but non-realizable pulse-shaping waveforms. Finally, simulation results demonstrate the superior error performance gains of the proposed uncoded OTFS schemes over OFDM under various channel conditions.

We investigate multiple input multiple output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems that operate with insufficient cyclic prefix (CP). Using a CP shorter than the channel delay spread can enable a significant improvement in bandwidth utilization or range extension for OFDM networks at the cost of increased intersymbol interference (ISI) and inter-carrier interference (ICI). We first analyze the effect of ICI and ISI on the received signal. A bi-directional M-algorithm (BDMA) is then proposed for high performance trellis-based equalization to construct an iterative interference mitigation and detection process. Simulations show that, after only 2 iterations, the bit error rate (BER) of the proposed equalization scheme can converge to that of a sufficient-CP system even when the channel delay spread is 6 times longer than the insufficient CP.

This paper studies stochastic power control problems over a fading channel, where the transmitter randomly harvests renewable energies from environment and stores them in a battery for future data transmissions. Moreover, data packets are assumed to arrive at the data queue of transmitter with constant rate μ. To incorporate delay quality-of-service guarantees, two delay constraint models are separately considered, namely average delay model with maximum average delay constraint and statistical delay model with maximum delay outage probability constraint. Under each delay constraint model, the stochastic power control problem aims at maximizing μ considering the randomness of channel fading and energy harvesting (EH) processes. The resulting optimization problems can be formulated as infinite-horizon Markov decision processes. Under average delay model, the optimal power control policy needs to keep track of current data queue-length state in addition to the battery state. On the other hand, under statistical delay model, a sufficiently large queue-length region is assumed, hence, the optimal policy does not depend on the data queue-length state. We study various structural properties of the optimal control policies and develop online power control algorithms that converge to the optimal solutions without requiring statistical knowledge of channel fading and EH processes. By defining and learning the so-called post-decision state-value functions, the proposed learning algorithms require less complexity and converge faster than the conventional reinforcement learning algorithms. Numerical results demonstrate the effectiveness of the online learning algorithms for different delay constraint models and EH settings.

We consider a three-node buffer-aided relaying network with statistical quality-of-service (QoS) constraint in terms of maximum acceptable end-to-end queue-length bound outage probability. In particular, we study the adaptive link selection relaying problem that aims to maximize the constant supportable arrival rate μ to the source (i.e., the effective capacity). Fixed and adaptive source and relay power allocation are investigated. By employing asymptotic delay analysis, we first convert the QoS constraint into minimum QoS exponent constraints at the source and relay queues. We then derive the link selection and power allocation solutions as functions of the instantaneous link conditions and QoS exponents using Lagrangian approach. Solutions for various special cases of link conditions and QoS constraints are presented. Moreover, we compare the effective capacities of the proposed relaying schemes and other existing schemes under different link conditions and QoS constraints. Illustrative results indicate that the proposed schemes offer substantial performance gains, and power adaption outperforms fixed power allocation at low signal-to-noise power ratio (SNR) region or under loose QoS constraints.

This paper studies optimal stochastic power control problem for a time-varying communication link, where the transmitter randomly harvests renewable energies from the environment. The harvested energies are stored in an energy buffer (or battery). Packets arrive at the transmitter data buffer with a constant rate μ. The objective is to maximize μ under the statistical delay and energy harvesting (EH) constraints. In order to study the optimal power control policy, we reformulate the problem as an infinite-horizon Markov decision process (MDP) using asymptotic delay analysis. The optimal policy and its structural properties are studied by employing the post-decision framework approach. We then propose an online power control algorithm, which converges to the optimal solution without requiring the statistical knowledge of the channel fading and EH processes. Numerical results demonstrate the effectiveness of the online algorithm for different delay constraints and EH settings.

This work considers the relay selection and resource allocation problem (i.e., link scheduling, and rate allocation) for multi-source, multi-relay dual-hop wireless networks. The relays employ buffers to store the received data from the sources for future transmissions. End-to-end (E2E) delay of each traffic flow originated from a source or a relay is constrained in terms of maximum allowable delay-outage probability. To solve this problem, we first study the resource allocation problem to maximize the constant supportable arrival rate of a non-prioritized source under minimum rate requirements of the prioritized sources and relays for a given relay selection solution. Then, the optimal relay selection can be determined to support the largest rate of the non-prioritized source among all possible relay selection solutions. We derive the resource allocation solutions using asymptotic delay analysis and convex optimization techniques. We also develop an online allocation algorithm which does not require the knowledge of the fading statistics by using stochastic approximation theory. Numerical results are presented to demonstrate the usefulness of the proposed resource allocation design for relay selection under different delay and rate constraint regimes.

This paper considers a 3-node buffer-aided relaying network with statistical delay quality-of-service (QoS) constraints imposed at the source and relay. To exploit the relay buffering capability and link fading diversity, an adaptive link selection relaying scheme is proposed. In a time slot, the relay (R) can adaptively select to receive from the source (S) or to transmit to the destination (D) based on the instantaneous conditions of the S-R and R-D links. The selection scheme aims to maximize the constant supportable arrival rate to the source, i.e., the effective capacity in consideration of the link fading distributions and the average signal-to-noise power ratios (SNRs) as well as the QoS constraints. We compare the capacities of the adaptive relaying and the fixed relaying where the relay employs fixed transmission and reception schedule, demonstrating the gain of the former, especially under loose QoS constraints. The capacities of the buffer-aided relaying and non-buffer relaying under similar end-to-end delay QoS constraint are also compared, showing the benefits of using buffer-aided relaying to support delay-sensitive applications.

We study the stochastic user scheduling and power control problem for an uplink multi-user network over time-varying channels, where the users randomly harvest renewable energies from the environment. For each user, the renewable energies and arriving data packets with a constant rate are stored in energy (battery) and data buffers, respectively. Users have statistical packet delay constraints in terms of maximum acceptable delay-outage probabilities. We classify the users as prioritized and non-prioritized users. Our goal is to maximize the arrival rate of the non-prioritized user while supporting the minimum data rate requirements for the prioritized users. We reformulate the problem as an infinite-horizon Markov decision process (MDP) using asymptotic delay analysis and study the optimal scheduling and power control policy. Since the optimal policy requires centralized processing with high computational complexity, we develop a reduced-complexity distributed algorithm, which can be implemented at each individual user. Online algorithm is devised, which does not require the statistical knowledge of the channel fading and energy harvesting (EH) processes. Numerical results demonstrate the effectiveness of the centralized and distributed schemes for different delay constraints and EH settings.

This paper considers a buffer-aided full-duplex (FD) relaying network assuming imperfect self-interference (SI) cancellation. The residual SI level is modeled to be proportional to the relay transmit power. Moreover, for quality-of-service (QoS) provisioning, the end-to-end queue-length is statistically constrained in terms of maximum acceptable outage probability. We derive the optimal power allocation solution for the source and relay to maximize the constant supportable arrival rate μ to the source (i.e., the effective capacity) and study its properties. Illustrative examples are performed to compare the effective capacities of the proposed FD relaying scheme and other benchmark schemes under different settings and QoS constraints.

This paper presents an analysis of the achievable effective capacity of a dual-hop network with buffer-aided source and relays using a concurrent relay selection protocol. The source and relays are subject to constraints on the maximum allowable delay violation probabilities. The effective capacity under such statistical delay constraints is derived as a function of the source and relay signal-to-noise power ratios (SNRs), delay exponents, number of relays, and fading distributions. Illustrative results show the effective capacity under various SNR regions and statistical delay constraints. It is seen that more stringent delay constraints significantly reduce the effective capacity of dual-hop networks.

This work studies the effective capacities of dualhop networks with relay selection. The source uses buffer to store the arrival packets. For non-buffer relays, they forward the packets received from the source to the destination in the next time slot in the same transmission frame. For buffer-aided relays, they can store the received packets and forward them in future transmission frame(s). Two different buffer-aided relays can be selected for packet reception and packet forwarding in a frame to exploit the relay selection diversity in both relay and access links. As a result, higher throughput can be achieved as compared to the case of non-buffer relays at the expense of increased buffering delay. To provision delay QoS guarantees, the source and buffer-aided relays operate under statistical delay guarantees in terms of maximum delay violation probabilities. The effective capacities (i.e., maximum constant arrival rates to the source) are characterized as function of the signal-to-noise ratios (SNRs) of the source and relays, delay parameters, and fading distributions.

This work studies the joint scheduling - admission control (SAC) problem over a fading channel. In particular, the optimal trade-off between maximizing the throughput and minimizing the queue size (or average congestion) is investigated. The SAC problem is formulated as a constrained Markov decision process (MDP) to maximize a utility defined as a function of the throughput and the queue size. The structural properties of the optimal policies are subsequently derived. When the statistical knowledge of the traffic arrival and channel processes is not available, we propose an online learning algorithm for the optimal policies. The analysis and algorithm development are relied on the reformulation of the Bellman's optimality dynamic programming equation using suitably defined value functions which can be learned using online time-averaging.

This work studies the dynamic scheduling problems in wireless networks with delay-sensitive loss-tolerant users. The users' traffic satisfies some statistical delay constraints. Moreover, the traffic can be dropped but the dropping rates do not exceed some thresholds. We consider two scheduling scenarios. First, we study the problem to minimize the total transmission power while maintaining the minimum rates for the users. Then, we study the problem to maximize the minimum rate(s) of the users while constraining the maximum total power. We derive the optimal solutions for both scheduling problems. When the fading statistics are available, using the dual-gradient method, the optimal policies can be computed. When the fading statistics are unknown, this work proposes online scheduling algorithms using online time-averaging. The convergence and optimality of the proposed algorithm are guaranteed by the results in stochastic approximation theory.

This work studies the power optimal dynamic scheduling problem in multi-channel multi-user wireless access networks. Users have quality-of-service (QoS) requirements on the minimum rates with statistical delay guarantees. Only one user is allowed to transmit over a channel in a given time slot. This work considers two scenarios: homogeneous and heterogeneous users. For the former scenario, the optimal scheduling policy can be derived, and an online scheduling algorithm for the optimal policy is proposed using online time-averaging without requiring a-priori known fading statistics. For the latter scenario, the optimal scheduling problem is combinatorially hard; hence, even when the fading statistics are available, computing the optimal policy is intractable. Consequently, this work develops a sub-optimal online scheduling algorithm with linear complexity which does not require a-priori known fading statistics. Moreover, the scheduling algorithm satisfies the QoS constraints for the users. Illustrative results demonstrate the performance of the proposed scheduling algorithms in various settings.

This work studies the joint scheduling- admission control (SAC) problem for a single user over a fading channel. Specifically, the SAC problem is formulated as a constrained Markov decision process (MDP) to maximize a utility defined as a function of the throughput and queue size. The optimal throughput- queue size trade-off is investigated. Optimal policies and their structural properties (i.e., monotonicity and convexity) are derived for two models: simultaneous and sequential scheduling and admission control actions. Furthermore, we propose online learning algorithms for the optimal policies for the two models when the statistical knowledge of the time-varying traffic arrival and channel processes is unknown. The analysis and algorithm development are relied on the reformulation of the Bellman's optimality equations using suitably defined state-value functions which can be learned online, at transmission time, using time-averaging. The learning algorithms require less complexity and converge faster than the conventional Q-learning algorithms. This work also builds a connection between the MDP based formulation and the Lyapunov optimization based formulation for the SAC problem. Illustrative results demonstrate the performance of the proposed algorithms in various settings.

Distributed medium access control (MAC) protocols are essential for the proliferation of low-cost, decentralized wireless local area networks (WLANs). Most MAC protocols are designed with the presumption that nodes comply with prescribed rules. However, selfish nodes have natural motives to manipulate protocols in order to improve their own performance. This often degrades the performance of other nodes as well as that of the overall system. In this paper, we propose a class of protocols that limit the performance gain from selfish manipulation while incurring only a small efficiency loss. The proposed protocols are based on the idea of a review strategy, with which nodes collect signals about the actions of other nodes over a period of time, use a statistical test to infer whether or not other nodes are following the prescribed behavior, and trigger a punishment if a deviation is inferred. We consider the cases of private and public signals and provide analytical and numerical results to demonstrate the properties of the proposed protocols.

It is well-known that medium access control (MAC) protocols are vulnerable to the selfish behavior of nodes, which often results in inefficient use of resources. In this work, we aim to overcome this inefficiency by constructing a class of defection-proof MAC protocols in the context of slotted multiple access communications. The operation of the proposed protocols can be divided into a review phase and a reciprocation phase. In a review phase, nodes cooperate and collect signals on the behavior of other nodes. At the end of a review phase, nodes perform a statistical test independently to determine whether there has been a defecting node in the system. In a reciprocation phase, a node cooperates if it concludes that no defection has occurred and carries out a punishment otherwise. We provide sufficient conditions for protocols to be defection-proof against a constant defection strategy and to achieve an arbitrarily small efficiency loss. We analyze an example of a statistical test based on which we can build protocols that satisfy the sufficient conditions.

In this paper, we study the joint power allocation and relay selection problem for multi-user amplify-and-forward (AF) cooperative networks. To increase the system's spectral efficiency under the orthogonal transmission assumption, each source-destination pair is constrained to be assisted by a small subset of a set of available relays. The aim of this work is to establish a framework that determines which relays to help which users and with how much power. In particular, we propose the joint schemes under two design criteria: i) maximization of user rates, and ii) minimization of the total transmit power at the relays. As the original problem formulations are shown to be nonconvex integer optimization problems, and thus, are combinatorially hard, we also propose an efficient convex relaxation approach to solve the problems with low complexity. Numerical results demonstrate the effectiveness of the proposed approaches.

Optimal power allocation for multi-user amplify- and-forward wireless relay networks in which multiple source-destination pairs are assisted by a set of relays is investigated. Two relay power allocation strategies based on maximization of either i) the minimum rate among all users or ii) the weighted sum of rates are developed. A distributed implementation of the maximum weighted-sum-rate power allocation strategy is also studied. Numerical results demonstrate the efficiency of the proposed strategies and reveal their interesting throughput-fairness tradeoff in resource allocation.

In this letter, the use of adaptive source transmission with amplify-and-forward relaying is proposed. Three different adaptive techniques are considered: (i) optimal simultaneous power and rate adaptation; (ii) constant power with optimal rate adaptation; (iii) channel inversion with fixed rate. The capacity upper bounds of these adaptive protocols are derived for the amplify-and-forward cooperative system over both independent and identically distributed (i.i.d.) Rayleigh fading and non-i.i.d. Rayleigh fading environments. The capacity analysis is based on an upper bound on the effective received signal-to-noise ratio (SNR). The tightness of the upper bound is validated by the use of a lower bound and by Monte Carlo simulation. It is shown that at high SNR the optimal simultaneous power and rate adaptation and the optimal rate adaptation with constant power provide roughly the same capacity. Channel inversion is shown to suffer from a deterioration in capacity relative to the other adaptive techniques.

In this paper, we consider an amplify-and-forward wireless relay system where multiple source nodes communicate with their corresponding destination nodes with the help of relay nodes. Conventionally, each relay equally distributes the available resources to its relayed sources. This approach is clearly sub-optimal since each user experiences dissimilar channel conditions, and thus, demands different amount of allocated resources to meet its quality-of-service (QoS) request. Therefore, this paper presents novel power allocation schemes to i) maximize the minimum signal-to-noise ratio among all users; ii) minimize the maximum transmit power over all sources; iii) maximize the network throughput. Moreover, due to limited power, it may be impossible to satisfy the QoS requirement for every user. Consequently, an admission control algorithm should first be carried out to maximize the number of users possibly served. Then, optimal power allocation is performed. Although the joint optimal admission control and power allocation problem is combinatorially hard, we develop an effective heuristic algorithm with significantly reduced complexity. Even though theoretically sub-optimal, it performs remarkably well. The proposed power allocation problems are formulated using geometric programming (GP), a well-studied class of nonlinear and nonconvex optimization. Since a GP problem is readily transformed into an equivalent convex optimization problem, optimal solution can be obtained efficiently. Numerical results demonstrate the effectiveness of our proposed approach.

In this paper, we consider an amplify-and-forward (AF) wireless relay system where multiple source nodes communicate with their corresponding destination nodes with the help of relay nodes. While each user is assisted by one relay, one relay can assist many users. Conventionally, each relay node is assumed to equally distribute the available bandwidth and power resources to all sources for which it helps to relay information. Realizing the sub-optimality of this approach, in this paper, we present efficient power allocation schemes to i) maximize the minimum end-to-end signal-to-noise ratio among all users; ii) minimize the total transmit power over all sources; iii) maximize the system throughput. Our approach is based on geometric programming (GP), a well-studied class of nonlinear and nonconvex optimization. Since a GP problem is readily transformed into an equivalent convex optimization problem, optimal power allocation can be obtained efficiently. Numerical results demonstrate the effectiveness of our proposed approach.

The use of constant-power, rate-adaptive M-QAM transmission with an amplify-and-forward cooperative system is proposed. The upper bound expressions are derived for the outage probability, achievable spectral efficiency, and error rate performance for the amplify-and-forward cooperative system over both independent and identically distributed (i.i.d.) and non-i.i.d. Rayleigh fading environments. The analysis is based on an accurate upper bound on the total effective signal-to- noise ratio SNR at the destination. Adaptive continuous rate M-QAM achieves a capacity that comes within a constant gap of the Shannon capacity of the channel, but adaptive discrete rate M-QAM suffers additional performance penalties.

It is a challenging task for multi-hop wireless networks to support multimedia applications with quality-ofservice (QoS) requirements. This letter presents a joint crosslayer optimization approach, i.e., joint medium access control, routing, and energy distribution. User satisfaction represented by user utility is maximized within the required network lifetime, given the constraints on the total available energy in the network and the minimum user rates. Although the resulting optimization problem is nonlinear and nonconvex, we prove that it is approximately equivalent to a two-step convex problem. Furthermore, we prove that the problem of maximizing network utility within achievable network lifetime is quasiconvex

Despite their high spectral efficiencies, multiple-input multiple-output (MIMO) systems suffer from high cost and complexity due to multiple radio frequency chains at both link ends. A possible solution is to select a subset of the available antennas at transmitter and/or receiver based on maximal capacity or minimal error rates. This paper proposed a receive antenna selection algorithm to minimize the union bound on the vector error rate. By relaxing the antenna selection variables from discrete to continuous, the authors formulate the problem as a convex optimization problem. An efficient iterative method can be used to obtain the solution.

Despite their high spectral efficiencies, multiple-input multiple-output (MIMO) systems suffer from high cost and complexity due to multiple radio frequency chains at both link ends. A possible solution is to select a subset of the available antennas at transmitter and/or receiver based on maximal capacity or minimal error rates. In this letter, we propose a receive antenna selection algorithm based on the minimization of the union bound on the vector error rate. By relaxing the antenna selection variables from discrete to continuous, we arrive at a convex optimization problem. Efficient numerical methods such as interior-point algorithms can be applied to solve this optimization problem with polynomial complexity.