Future wireless networks must support emerging applications where environmental awareness is as critical as data transmission. Integrated Sensing and Communication (ISAC) enables this vision by allowing base stations (BSs) to allocate bandwidth and power to mobile users (MUs) for communications and cooperative sensing. However, this resource allocation is highly challenging due to:(i)dynamic resource demands from MUs and resource supply from BSs, and(ii)the selfishness of MUs and BSs. To address these challenges, existing solutions rely on either real-time (online) resource trading, which incurs high overhead and failures, or static long-term (offline) resource contracts, which lack flexibility. To overcome these limitations, we propose theFuture Resource Bank for ISAC, a hybrid trading framework that integrates offline and online resource allocation through a level-wise client model, where MUs and their coalitions negotiate with BSs. We introduce two mechanisms:(i)Offline Role-Friendly Win-Win Matching (offRFW2M), leveraging overbooking to establish risk-aware, stable contracts, and(ii)Online Effective Backup Win-Win Matching (onEBW2M), which dynamically reallocates unmet demand and surplus supply. We theoretically prove stability, individual rationality, and weak Pareto optimality of these mechanisms. Through comprehensive experiments, we show that our framework improves social welfare, latency, and energy efficiency compared to existing methods.

Designing effective incentive mechanisms in mobile crowdsensing (MCS) networks is crucial for engaging distributed mobile users (workers) to contribute heterogeneous data for various applications (tasks). In this paper, we propose a novel stagewise trading framework to achieve efficient and stable task-worker matching, explicitly accounting for task diversity (e.g., spatio-temporal limitations) and network dynamics inherent in MCS environments. This framework integrates both futures and spot trading stages. In the former, we introduce the futures trading-driven stable matching and pre-path-planning mechanism (FT-SMP3), which enables long-term taskworker assignment and pre-planning of workers' trajectories based on historical statistics and risk-aware analysis. In the latter, we develop the spot trading-driven DQN-based path planning and onsite worker recruitment mechanism (ST-DP2WR), which dynamically improves the practical utilities of tasks and workers by supporting real-time recruitment and path adjustment. We rigorously prove that the proposed mechanisms satisfy key economic and algorithmic properties, including stability, individual rationality, competitive equilibrium, and weak Pareto optimality. Extensive experiements further validate the effectiveness of our framework in realistic network settings, demonstrating superior performance in terms of service quality, computational efficiency, and decision-making overhead.

This paper investigates an unlicensed millimeter-wave (mmWave) coexistence system, where the new radio-based access to unlicensed spectrum (NR-U) network and the incumbent Wireless Gigabit (WiGig) network share the same spectrum resources to transmit data packets. The total data rate of NR-U is maximized by jointly optimizing the user equipment (UE) scheduling and hybrid beamforming, subject to the constraints of the quality-of-service (QoS) requirements of all UEs and the maximum transmit power at the gNB. Besides, NR-U needs to avoid excessive interference to WiGig, without acquiring any prior information about WiGig. To circumvent this problem, we put forth a model-free joint optimization scheme, referred to as Deep Q-Policy Gradient (DQPG), based on the deep reinforcement learning (DRL) technique. Specifically, a hybrid DRL framework is first proposed to support DQPG, where the deep double Q-network (D2QN) and double-critic-based deep deterministic policy gradient (D3PG) algorithms are invoked to optimize UE scheduling in the discrete action domain and hybrid beamforming in the continuous action domain, respectively. Thereafter, a new reward function and a scaled action selection policy are judiciously designed for DQPG to satisfy various constraints. To capture the complex coupling relationship between different optimization variables, we further propose a parallel experience replay mechanism to maintain training synchronization between D2QN and D3PG. In addition, a transfer learning approach is introduce to accelerate the convergence of DQPG. Simulation results demonstrate that while satisfying the QoS requirements of all UEs, compared with other coexistence schemes, DQPG (i) attains a higher total data rate of NR-U, (ii) causes less interference to WiGig, and (iii) converges faster. Furthermore, under different numbers of UEs and QoS requirements of UEs, DQPG is more robust than benchmarks.

This paper studies an intelligent omni-surface (IOS)-aided integrated sensing and communication (ISAC) system, where a base station (BS) provides simultaneous target sensing and communication services with an IOS under outdated and imperfect channel state information (CSI). Both the communication sum-rate and sensing signal-to-noise ratio are maximized through joint optimization of BS beamforming and IOS configuration. To address this problem, we propose an intelligent joint optimization scheme called meta multi-objective hybrid deep reinforcement learning (meta-MHDRL). Specifically, the meta-MHDRL framework first introduces a hybrid deep reinforcement learning (DRL) approach that integrates double-critic-based deep deterministic policy gradient with deep double Q-network algorithms, enabling parallel optimization of both continuous-domain variables (i.e., BS beamforming, IOS reflecting phase shift, and IOS reflecting/refracting amplitudes) and the discrete-domain variable (i.e., IOS refracting phase shift). Thereafter, an objective-preference weight is incorporated into the hybrid DRL framework, such that meta-MHDRL can capture the trade-off between communication and sensing performance. To address the complex coupling relationships among different optimization variables, we further put forth a synchronized experience replay mechanism for meta-MHDRL, which maintains training synchronization among different neural networks. In addition, a meta-learning approach is developed to enhance the generalization ability of meta-MHDRL across different objective-preference weights. Simulation results show that meta-MHDRL attains more Pareto-efficient solutions than other schemes under outdated and imperfect CSI while maintaining stronger robustness across various simulation setups. Besides, we demonstrate the generalization ability of meta-MHDRL for unseen tasks

This paper studies an integrated sensing and communications (ISAC) system for low-altitude economy (LAE), where a ground base station (GBS) provides communication and navigation services for authorized unmanned aerial vehicles (UAVs), while sensing the low-altitude airspace to monitor the unauthorized mobile target. The expected communication sum-rate over a given flight period is maximized by jointly optimizing the beamforming at the GBS and UAVs’ trajectories, subject to the constraints on the average signal-to-noise ratio requirement for sensing, the flight mission and collision avoidance of UAVs, as well as the maximum transmit power at the GBS. Typically, this is a sequential decision-making problem with the given flight mission. Thus, we transform it to a specific Markov decision process (MDP) model called episode task. Based on this modeling, we propose a novel LAE-oriented ISAC scheme, referred to as Deep LAE-ISAC (DeepLSC), by leveraging the deep reinforcement learning (DRL) technique. In DeepLSC, a reward function and a new action selection policy termed constrained noise-exploration policy are judiciously designed to fulfill various constraints. To enable efficient learning in episode tasks, we develop a hierarchical experience replay mechanism, where the gist is to employ all experiences generated within each episode to jointly train the neural network. Besides, to enhance the convergence speed of DeepLSC, a symmetric experience augmentation mechanism, which simultaneously permutes the indexes of all variables to enrich available experience sets, is proposed. Simulation results demonstrate that compared with benchmarks, DeepLSC yields a higher sum-rate while meeting the preset constraints, achieves faster convergence, and is more robust against different settings.

Intelligent reflecting surface (IRS) is a promising paradigm to reconfigure the wireless environment for enhanced communication coverage and quality. However, to compensate for the double pathloss effect, massive IRS elements are required, raising concerns on the scalability of cost and complexity. This paper introduces a new architecture of quasi-static IRS (QS-IRS), which tunes element phases via mechanical adjustment or manually re-arranging the array topology. A simple divide-and-assemble (DnA) approach is further proposed, which enables massive production/assembly of purely passive elements without diodes/controllers/bias networks, and thus is suitable for ultra low-cost and large-scale deployment to enhance long-term coverage. To achieve this end, an IRS-aided area coverage problem is formulated, which explicitly considers the element radiation pattern (ERP), with the newly introduced shape masks for the mainlobe, and the sidelobe constraints to reduce energy leakage. An alternating optimization (AO) algorithm based on the difference-of-convex (DC) and successive convex approximation (SCA) procedure is proposed, which achieves shaped beamforming with power gains close to that of the joint optimization algorithm, but with significantly reduced computational complexity.

Uncoordinated random-access protocols are attractive for underwater acoustic (UWA) networks due to their simplicity and low overhead, especially for data collection applications in scuba diving. However, the performance is limited by severe collisions and the challenging UWA channel, including rich multipath and time-varying channel (caused by Doppler effects and user movements). Existing UWA physical-layer waveforms struggle to resolve collisions while maintaining high data rates. This paper presents ZCMod, a Zadoff-Chu (ZC) sequence-based modulation that assigns unique ZC sequences to users to mitigate interference and encodes multiple bits via cyclic shifts for high data rates. To address UWA-specific challenges, ZCMod introduces two key designs: 1) shape-based demodulation, which tracks channel response shifts to combat multipath effects; 2) auxiliary modulation, where each symbol is modulated with two ZC sequences—one for channel estimation and the other for data transmission—to handle fast time-varying channels. Experiments and simulations demonstrate that a) ZCMod achieves more robust BER performance and eliminates error floors compared to state-of-the-art (SOTA) methods in slight time-varying channels; and b) ZCMod maintains stable throughput in fast time-varying channels, while SOTA approaches suffer significant degradation.

Orthogonal Time Frequency Space (OTFS) modulation has demonstrated advantages in addressing the doubly selective underwater acoustic (UWA) channels. In this paper, we focus on channel estimation in OTFS-based underwater acoustic communications (UAC), where large Doppler effects in wideband systems increase estimation challenge. We first present an OTFS-UAC system based on the discrete Zak transform, incorporating fractional effects, and the scaling impact induced by the Doppler shifts in wideband systems. To address the superimposing of fractional effects and Doppler scaling, we analyze these characteristics in the time-domain and then map them into the delay-Doppler (DD) domain. Specifically, we formulate channel estimation as a compressed sensing problem and design a refined grid-based sensing matrix through the timedomain effective channel matrix. Utilizing the sparsity of the UWA channel impulse response, we propose a Sparse Bayesian Learning (SBL)-based algorithm that avoids DD domain channel spreading. Simulation and experimental results demonstrate that the proposed algorithm outperforms the comparison algorithms by at least 3dB signal-to-noise ratios (SNR) gain under bit error rate (BER) of 10−2on simulation channels, and by at least 5dB SNR gain under BER of 10−1on experiment channels.

Cell-free massive MIMO (CF mMIMO) systems have become an attractive technology for enhancing the spectral efficiency of next-generation wireless communication systems due to their ability to effectively eliminate inter-cell interference. In the millimeter wave (mmWave) band, hybrid precoding technology can improve spectral efficiency while limiting the number of radio frequency (RF) chains per access point (AP), thereby reducing system power consumption. This paper focuses on the design of hybrid precoding for the downlink in mmWave CF mMIMO systems. We aim to maximize the system's weighted sum-rate and propose a partially distributed hybrid precoding framework. This framework solves each AP's hybrid precoding matrix by deploying a weighted minimum mean square error (WMMSE) algorithm at the central processing unit (CPU) and a matrix decomposition algorithm at each AP. Simulation results show that our proposed method achieves system weighted sumrate performance comparable to that of centralized algorithms, while offering lower computational complexity and reduced signaling overhead. Additionally, compared with distributed noncooperative precoding methods, our approach improves the sumrate by more than 50 %.

Aerial base stations (ABSs) mounted on unmanned aerial vehicles (UAVs) are capable of extending wireless connectivity to ground users (GUs) across a variety of scenarios. However, it is an NP-hard problem with exponential complexity in M and N, in order to maximize the coverage rate (CR) of M GUs by jointly placing N ABSs with limited coverage range. The complexity of the problem escalates in environments where the signal propagation is obstructed by localized obstacles such as buildings, and is further compounded by the dynamic GU positions. In response to these challenges, this paper focuses on the optimization of a multi-ABS movement problem, aiming to improve the mean CR for mobile GUs within a site-specific environment. Our proposals include 1) introducing the concept of global connectivity map (GCM) which contains the connectivity information between given pairs of ABS/GU locations; 2) partitioning the ABS movement problem into ABS placement sub-problems and formulate each sub-problem into a binary integer linear programming (BILP) problem based on GCM; 3) and proposing a fast online algorithm to execute (one-pass) projected stochastic subgradient descent within the dual space to rapidly solve the BILP problem with near-optimal performance. Numerical results demonstrate that our proposed method achieves a high CR performance close to the upper bound obtained by the open-source solver (SCIP), yet with significantly reduced running time. Moreover, our method also outperforms common benchmarks in the literature such as the K-means initiated evolutionary algorithm or the ones based on deep reinforcement learning (DRL), in terms of CR performance and/or time efficiency.

The upcoming IEEE 802.11be standard, termed WiFi 7, introduces multi-link operation (MLO), enabling devices to establish multiple simultaneous connections utilizing different frequencies and channels. While MLO has the potential to boost network throughput, optimizing channel allocation in WiFi 7 networks introduces many challenges. In this paper, we propose a best-arm identification-enabled Monte Carlo tree search (BAI-MCTS) algorithm for efficient channel allocation in WiFi 7 networks. Specifically, we first employ an efficient mechanism to calculate the network throughput by capturing the essential features of the CSMA protocol. We then formulate this channel allocation problem as a multi-armed bandit (MAB) problem. However, solving this MAB problem induces high sample complexity due to the large-arm space. To overcome this challenge, we introduce BAI-MCTS by combining the BAI and MCTS techniques. Notably, BAI-MCTS has a fast convergence rate and low sample complexity. Simulation results demonstrate that the proposed algorithm outperforms the baseline algorithms in terms of the convergence rate, which is about 42.40% faster than the UCT algorithm when reaching 95% of the optimal value.

Underwater acoustic networks are typically distributed in nature and have been attracting much research interest recently. Such networks are characterized by long propagation delays, which pose challenges for the medium access control (MAC) protocol design in underwater acoustic networks. In this paper, instead of considering long propagation delay as a negative effect, we exploit it as an advantage. We propose a multi-agent reinforcement learning (MARL)-based MAC protocol without requiring acknowledgement feedback, named Delay-Aware Multi-Agent Reinforcement Learning Multiple Access (DA-MARLA), which leverages propagation delays to achieve higher throughput. Furthermore, the throughput achieved can exceed that of systems with zero propagation delay. In developing DA-MARLA, we introduce a novel MARL algorithm, termed delay-aware multi-agent proximal policy optimization (DA-MAPPO). Specifically, to leverage the long propagation delays, we propose two period-based mechanisms that coordinate nodes’ transmission schedules to reduce collisions and balance cooperation and competition among nodes. To ensure reliable operation, we incorporate a sequential policy update mechanism. This mechanism offers accurate performance evaluation for each node and establishes update sequences during centralized training. Simulation results show that our method consistently outperforms baseline methods across various network topologies while maintaining robustness, demonstrating that the propagation delays can be effectively utilized to enhance network efficiency and providing a new avenue for performance improvement in underwater acoustic networks.

Due to the harsh channel conditions and operational difficulties in battery recharging, energy-efficient transmission is critical in underwater acoustic communications (UACs). This paper investigates a new link adaptation technique for UACs that jointly optimizes transmission frequency, power, and rate to maximize energy efficiency. Conventional optimization-based approaches typically require real-time and perfect channel state information and have high computational complexity, making them difficult to implement in realistic systems. To circumvent this problem, we put forth MetaDT, a model-free link adaptation technique combining deep reinforcement learning with meta-learning. To enable powerful reasoning and fast decision-making, we further propose a dueling echo state network (ESN) with separate output architecture for incorporation into MetaDT. Besides, to enable MetaDT to quickly adapt to diverse new/unseen environments, a low-complexity meta-learning is developed to find the optimal meta-parameters of the dueling ESN architecture. Numerical results show that compared to various benchmarks, MetaDT attains significant energy efficiency gains and is more robust against different transmission distances and numbers of multi-paths. In comparison to conventional neural networks, dueling ESN shortens the run-time of MetaDT by more than 89.58% and is more efficient for temporal inference. In addition, we demonstrate the generalization capability of MetaDT with meta-learning to new/unseen environment configurations.

This paper investigates the joint modulation-coding scheme (MCS) adaptation and beamforming design for multi-user multi-input single-output (MISO) systems, where one base station serves multiple user equipments (UEs) under imperfect and outdated channel state information (CSI). The sum-rate of the system is maximized while satisfying all UEs’ data rate requirements and the maximum transmit power constraint at the BS. Most existing beamforming designs overlooked that only a finite number of MCSs can be supported in practical communication systems. Moreover, previous works rely on perfect and real-time CSI for decision-making, neglecting processing delays and channel estimation errors. To circumvent the above issues, this paper puts forth an intelligent joint optimization scheme based on deep reinforcement learning (DRL) techniques. Specifically, a new DRL framework, termed constrained hybrid DRL (CHDRL), is first proposed, which incorporates Lagrangian primal-dual optimization theory and a constrained action selection policy into conventional DRL to tackle various constraints. By integrating deep Q-network (DQN) and deep deterministic policy gradient algorithms, CHDRL is capable of simultaneously optimizing MCS in the discrete action domain and beamforming in the continuous action domain. In addition, to handle the large discrete action space of DQN, we develop an action branch architecture for CHDRL to enable independent and concurrent MCS decisions at different UEs. Finally, a multi-parameter experience replay mechanism is designed to synchronously train Lagrangian multipliers and neural network parameters. Simulation results demonstrate that under imperfect and outdated CSI, CHDRL outperforms other benchmark schemes by (i) achieving a significantly higher sum-rate, (ii) meeting more UEs’ data rate requirements, and (iii) being more robust against different CSI delays and numbers of UEs.

Intelligent omni-surface (IOS) is a promising technique to enhance the capacity of wireless networks, by reflecting and refracting the incident signal simultaneously. Traditional IOS configuration schemes, relying on all subchannels’ channel state information and user equipments’ mobility, are difficult to implement in complex realistic systems. Existing works attempt to address this issue employing deep reinforcement learning (DRL), but this method requires a lot of trial-and-error interactions with the external environment for efficient results and thus cannot satisfy the real-time decision making. To enable model-free and real-time IOS control, this article puts forth a new framework that integrates DRL and digital twins. As a first step, deep reinforcement learning IOS (DeepIOS), a DRL based IOS configuration scheme with the goal of maximizing the sum data rate, is developed to jointly optimize the phase-shift and amplitude of IOS in multiuser multiple-input-multiple-output (MU-MIMO) systems. Thereafter, in order to further reduce the computational complexity, DeepIOS introduces an action branch architecture, which decides two optimization variables in parallel in a separate fashion. Finally, a digital twin module is constructed through supervised learning as a preverification platform for DeepIOS, such that the decision making’s real-time can be guaranteed. The formulated framework is a closed-loop system, in which the physical space provides data to establish and calibrate the digital space, while the digital space generates a large number of experience samples for DeepIOS training and sends the trained parameters to the IOS controller for configurations. Numerical results show that compared with random and MAB schemes, the proposed framework attains a higher data rate and is more robust to different settings. Furthermore, the action branch architecture reduces DeepIOS’s computational complexity, and the digital twin module improves DeepIOS’s convergence speed and run-time.

WiFi networks have achieved remarkable success in enabling seamless communication and data exchange worldwide. The IEEE 802.11be standard, known as WiFi 7, introduces Multi-Link Operation (MLO), a groundbreaking feature that enables devices to establish multiple simultaneous connections across different bands and channels. While MLO promises substantial improvements in network throughput and latency reduction, it presents significant challenges in channel allocation, particularly in dense network environments. Current research has predominantly focused on performance analysis and throughput optimization within static WiFi 7 network configurations. In contrast, this paper addresses the dynamic channel allocation problem in dense WiFi 7 networks with MLO capabilities. We formulate this challenge as a combinatorial optimization problem, leveraging a novel network performance analysis mechanism. Given the inherent lack of prior network information, we model the problem within a Multi-Armed Bandit (MAB) framework to enable online learning of optimal channel allocations. Our proposed Best-Arm Identification-enabled Monte Carlo Tree Search (BAI-MCTS) algorithm includes rigorous theoretical analysis, providing upper bounds for both sample complexity and error probability. To further reduce sample complexity and enhance generalizability across diverse network scenarios, we put forth LLM-BAI-MCTS, an intelligent algorithm for the dynamic channel allocation problem by integrating the Large Language Model (LLM) into the BAI-MCTS algorithm. Numerical results demonstrate that the BAI-MCTS algorithm achieves a convergence rate approximately 50.44% faster than the state-of-the-art algorithms when reaching 98% of the optimal value. Notably, the convergence rate of the LLM-BAI-MCTS algorithm increases by over 63.32% in dense networks.

Uncoordinated random-access protocols are well-suited for underwater acoustic (UWA) networks due to their simplicity and low overhead. However, their performance is hindered by severe collisions and the challenging characteristics of UWA channels such as rich multipath and Doppler effect. Existing UWA physical layer waveforms struggle to resolve collisions while maintaining high data rates. This paper introduces ZCMod, a high-rate waveform allowing uncoordinated concurrent random access in UWA networks. ZCMod employs a Zadoff–Chu (ZC) sequence-based modulation that assigns unique ZC sequences to users to minimize inter-user interference and encodes multiple bits through cyclic shifts of the sequences to improve data rates. ZCMod further addresses the unique challenges of UWA channels via two new designs: 1) a shape-based demodulation approach that estimates the data-induced shift of channel response shape between the preamble and data symbols to handle rich multipath, and 2) an auxiliary modulation approach that modulates each data symbol with two ZC sequences, one for extracting current channel response shape and the other for data modulation, to handle the fast time-varying channel. Experimental results in a lake and a swimming pool and extensive simulation results show that a) ZCMod achieves around 100% higher throughput compared with the state-of-the-art (SOTA) approaches in quasi-static channels, and b) ZCMod maintains comparable throughput in fast time-varying channels as in quasi-static conditions, where the SOTA approaches experience significant degradation.

Hierarchical Federated Learning (HFL) extends conventional Federated Learning (FL) by introducing intermedi ate aggregation layers, enabling distributed learning in geograph ically dispersed environments, particularly relevant for smart IoT systems, such as remote monitoring and battlefield operations, where cellular connectivity is limited. In these scenarios, UAVs serve as mobile aggregators, dynamically connecting terrestrial IoT devices. This paper investigates an HFL architecture with energy-constrained, dynamically deployed UAVs prone to communication disruptions. We propose a novel approach to minimize global training costs by formulating a joint optimization problem that integrates learning configuration, bandwidth allocation, and device-to-UAV association, ensuring timely global aggregation before UAV disconnections and redeployments. The problem accounts for dynamic IoT devices and intermittent UAV con nectivity and is NP-hard. To tackle this, we decompose it into three subproblems: (i) optimizing learning configuration and bandwidth allocation via an augmented Lagrangian to reduce training costs; (ii) introducing a device fitness score based on data heterogeneity (via Kullback-Leibler divergence), device-to UAV proximity, and computational resources, using a TD3-based algorithm for adaptive device-to-UAV assignment; (iii) developing a low-complexity two-stage greedy strategy for UAV redeployment and global aggregator selection, ensuring efficient aggregation despite UAV disconnections. Experiments on diverse real-world datasets validate the approach, demonstrating cost reduction and robust performance under communication disruptions.

This paper investigates an intelligent omni-surface (IOS)-assisted integrated sensing and communication (ISAC) system, where a base station provides both target sensing and communication services with an IOS. The sensing signal-to-noise ratio (SNR) is maximized while satisfying the communication requirement by optimizing IOS configurations. Conventional approaches typically need real-time and accurate channel state information (CSI) and have high computational complexity, making them difficult to implement in realistic systems. To circumvent this problem, this paper puts forth a new framework based on deep reinforcement learning (DRL) with knowledge transfer. In particular, an online learning scheme called Deep reinforcement learning IOS-ISAC (DeepOSC), is first proposed to optimize the reflecting and refracting coefficients of the IOS. Thereafter, to enable powerful reasoning and fast decision-making, we incorporate an echo state network (ESN) with separate output into DeepOSC. To further accelerate convergence, two transfer learning approaches, namely staged policy reuse (SPR) and staged policy distillation (SPD), are developed to guide the learning process of a newly deployed agent by leveraging policies of pre-trained agents. Numerical results show that compared to various benchmarks, DeepOSC attains significant sensing and communication performance gains and is more robust against outdated CSI coefficients. In addition, in comparison to conventional neural networks, ESN shortens the run-time of DeepOSC by more than ten times and is more efficient for temporal inference. Besides, we demonstrate the capabilities of SPR and SPD in accelerating the convergence of DeepOSC.

Affine Frequency Division Multiplexing (AFDM) is a brand new chirp-based multi-carrier (MC) waveform for high mobility communications, with promising advantages over Orthogonal Frequency Division Multiplexing (OFDM) and other MC waveforms. Existing AFDM research focuses on wireless communication at high carrier frequency (CF), which typically considers only Doppler frequency shift (DFS) as a result of mobility, while ignoring the accompanied Doppler time scaling (DTS) on waveform. However, for underwater acoustic (UWA) communication at much lower CF and propagating at speed of sound, the DTS effect could not be ignored and poses significant challenges for channel estimation. This paper analyzes the channel frequency response (CFR) of AFDM under multi-scale multi-lag (MSML) channels, where each propagating path could have different delay and DFS/DTS. Based on the newly derived input-output formula and its characteristics, two new channel estimation methods are proposed, i.e., AFDM with iterative multi-index (AFDM-IMI) estimation under low to moderate DTS, and AFDM with orthogonal matching pursuit (AFDM-OMP) estimation under high DTS. Numerical results confirm the effectiveness of the proposed methods against the original AFDM channel estimation method. Moreover, the resulted AFDM system outperforms OFDM as well as Orthogonal Chirp Division Multiplexing (OCDM) in terms of channel estimation accuracy and bit error rate (BER), which is consistent with our theoretical analysis based on CFR overlap probability (COP), mutual incoherent property (MIP) and channel diversity gain under MSML channels.

Mobile edge cloud (MEC) has emerged as a critical technology for enabling low-latency and real-time mobile device applications. However, an efficient resource allocation framework for improving the user experience in MEC with heterogeneous users is still missing, especially considering the recent sparks of AI-generated content applications. This paper proposes a double-closed-loop online resource allocation (DORA) framework for user experience improvement. This framework employs inner and outer loops to construct the optimal online allocation strategy and recommend a suitable strategy for different types of users, respectively. Based on the DORA framework, we put forth OR2A-HetU, an Online Resource Recommendation and Allocation algorithm for Heterogeneous Users, to solve this resource allocation problem. The OR2A-HetU algorithm proceeds sequentially and can converge to the optimal solution when the time horizon is sufficiently large. The numerical results show that the proposed algorithm outperforms the baseline algorithms, and the user complaint rate decreases from 48.8% to 27.5% when the available resources increase.

The 5G New Radio (5G NR) standard proposes beam management and beam tracking mechanisms, aiming to solve the blockage and beam misalignment in millimeter-wave (mmWave) communication. However, these usually become unrealistic due to the excessive time overhead. To address this issue and minimize the need for frequent beam training in high-speed scenarios, we aim to find a high accuracy and low complexity method for beam tracking. In particular, we propose a deep learning (DL)-based beam tracking algorithm, in which long short-term memory (LSTM) is cascaded with sparse transformer. LSTM extracts user equipment (UE) movement features from the received signal vector, and the sparse transformer predicts the optimal beam for future time slots in parallel. By incorporating adversarial training and a specifically designed loss function, our algorithm can be extended to handle more complex scenarios. The experimental results show that our algorithm could improve the top-1 accuracy (top-1 acc) by 3.65% when the velocity of UE is 20 m/s, and the model parameter numbers and the giga floating-point operations per second (GFLOPs) are only 16.3% and 11.98% of the baseline. Our scheme significantly reduces the model parameters while effectively improving the accuracy of beam tracking.

Linear chirp-based underwater acoustic communication has been widely used due to its reliability and long-range transmission capability. However, unlike the counterpart chirp technology in wireless - LoRa, its throughput is severely limited by the number of modulated chirps in a symbol. The fundamental challenge lies in the underwater multi-path channel, where the delayed signal may cause inter-symbol and intra-symbol interfere. In this paper, we present UWLoRa+, a system that realizes the same chirp modulation as LoRa with higher data rate, and address the multi-path challenge via the following new designs: a) we replace the linear chirp used by LoRa with the non-linear chirp to reduce the signal interference range and the collision probability; b) we design an algorithm that first demodulates each path and then combines the demodulation results of detected paths; and c) we replace the Hamming codes used by LoRa with the non-binary LDPC codes to mitigate the impact of the inevitable collision. Experiment results show that the new designs improve the bit error rate (BER) by 3 times, and the packet error rate (PER) significantly, compared with the LoRa's naive design. Compared with an state-of-the-art system for decoding underwater LoRa chirp signal, UWLoRa+ improves the throughput by up to 50 times.

Intelligent Reflecting Surface (IRS) is a promising technology for next generation wireless networks. Despite substantial research in IRS-aided communications, the assumed antenna and channel models are typically simplified without considering site-specific characteristics, which in turn critically affect the IRS deployment and performance in a given environment. In this paper, we first investigate the link-level performance of active or passive IRS taking into account the IRS element radiation pattern (ERP) as well as the antenna radiation pattern of the access point (AP). Then the network-level coverage performance is evaluated/optimized in site-specific multi-building scenarios, by properly deploying multiple IRSs on candidate building facets to serve a given set of users or Points of Interests (PoIs). The problem is reduced to an integer linear programming (ILP) based on given link-level metrics, which is then solved efficiently under moderate network sizes. Numerical results confirm the impact of AP antenna/IRS element pattern on the link-level performance. In addition, it is found that active IRSs, though associated with higher hardware complexity and cost, significantly improve the site-specific network coverage performance in terms of average ergodic rate and fairness among the PoIs as well as the range of serving area, compared with passive IRSs that have a much larger number of elements.

Link Adaptation (LA) that dynamically adjusts transmission parameters to accommodate time-varying channels is a critical technology in the Long-Term Evolution/New Radio system. Previous deep reinforcement learning (DRL)-based LA techniques directly select the Modulation and Coding Schemes (MCS) for each transmission. However, the frequent inference results in a high computational resource cost, leading to significant decision delays and potentially compromising the overall performance. To address these challenges, we present a new algorithm named TD3-OLLA, building upon separating the frequent selection of MCS from the time-consuming DRL model inference process. In particular, we introduce a two-level control framework that makes the real-time MCS selection using the traditional Outer Loop Link Adaptation (OLLA) algorithm and employs the DRL algorithm to tune OLLA's parameters. Our algorithm adopts the advanced Twin Delayed Deep Deterministic Policy Gradient (TD3) model and methods like classified experience replay to enhance the performance against rapidly changing link conditions. The simulation results demonstrate that TD3-OLLA achieves higher throughput than state-of-the-art LA techniques with an ultra-low overhead—the computation time is reduced by 80% compared to other DRL-based algorithms. Furthermore, it exhibits a high tolerance for model decision delays, making it well-suited for practical communication systems with strict latency requirements.

This article investigates the joint codebook selection and modulation-coding-scheme (MCS) adaptation issue for the enhanced mobile broadband (eMBB) service in millimeter-wave (mmWave) cellular systems. The proposed scheme guarantees efficient mmWave eMBB service through an intelligent joint codebook selection and MCS adaptation scheme that exploits deep reinforcement learning (DRL), referred to as DeepCM. DeepCM’s objective maximizes the transmission data rate while satisfying a target block error rate (BLER) constraint. A first step formulates this joint problem into a two-time-scale system that performs MCS adaptation on a small-time scale, whereas a second step optimizes the codebook on a large-time scale. DeepCM introduces a new DRL algorithm, termed dual-deep Q-network (DQN), by incorporating the operations on two time scales into the original DQN. Dual-DQN essentially enables the operations on different time scales to benefit from each other, through closed-loop decision guidance and reward evaluation. Thereafter, to fulfill the preset BLER constraint, DeepCM uses a constrained$\epsilon $-greedy strategy for decision-making and further modifies the conventional DRL training mechanism. Basically, DeepCM continuously adjusts the agent’s feasible-action space toward the system objective. With the constrained dual-DQN, DeepCM can attain its goal even without any prior network information. Simulation results show that DeepCM, compared with Thompson Sampling-DRL, DRL-OLLA, and TS2 schemes, guarantees the target BLER requirement while yielding a much higher data rate. Various simulations demonstrate the powerful robustness of DeepCM under miscellaneous scenarios. Furthermore, DeepCM can handle well dynamic target-BLER change.

In this article, we study the latency minimization problem for a wireless federated learning (FL) system with heterogeneous computation capability, where different edge devices perform different numbers of local model updates in each communication round. We formulate a total latency minimization problem with probabilistic device selection, taking into account both the communication and computation latency in the whole FL procedure. However, it is highly challenging to optimally solve this problem due to the coupling issues of model convergence and latency minimization problem caused by the heterogeneity of local model updates. Through convergence analysis, we reveal that decoupling the resource allocation variables from the model convergence is essential to reduce the problem to a single-round latency minimization problem. To solve this simplified problem, we propose an alternating optimization scheme to jointly consider communication and computation resource allocation and mitigate the straggler effect. We prove that the resulting subproblems, i.e., bandwidth and computation capacity allocation, are both convex and can be optimally solved in closed form, respectively. Simulation results show that compared with the baseline scheme that allocates the communication and computation resources equally across edge devices, the proposed scheme can achieve up to 47.04% single-round latency reduction.

Federated learning (FL) is an appealing paradigm for learning a global model among distributed clients while preserving data privacy. Driven by the demand for high-quality user experiences, evaluating the well-trained global model after the FL process is crucial. In this paper, we propose a closed-loop model analytics framework that allows for effective evaluation of the trained global model using clients' local data. To address the challenges posed by system and data heterogeneities in the FL process, we study agoal-directedclient selection problem based on the model analytics framework by selecting a subset of clients for the model training. This problem is formulated as a stochastic multi-armed bandit (SMAB) problem. We first put forth a quick initial upper confidence bound (Quick-Init UCB) algorithm to solve this SMAB problem under the federated analytics (FA) framework. Then, we further propose a belief propagation-based UCB (BP-UCB) algorithm under the democratized analytics (DA) framework. Moreover, we derive two regret upper bounds for the proposed algorithms, which increase logarithmically over the time horizon. The numerical results demonstrate that the proposed algorithms achieve nearly optimal performance, with a gap of less than 1.44% and 3.12% under the FA and DA frameworks, respectively.

Restless multi-armed bandits (RMABs) have been widely utilized to address resource allocation problems with Markov reward processes (MRPs). Existing works often assume that the dynamics of MRPs are known prior, which makes the RMAB problem solvable from an optimization perspective. Nevertheless, an efficient learning-based solution for RMABs with unknown system dynamics remains an open problem. In this paper, we fill this gap by investigating a cooperative resource allocation problem with unknown system dynamics of MRPs. This problem can be modeled as a multi-agent online RMAB problem, where multiple agents collaboratively learn the system dynamics while maximizing their accumulated rewards. We devise a federated online RMAB framework to mitigate the communication overhead and data privacy issue by adopting the federated learning paradigm. Based on this framework, we put forth a Federated Thompson Sampling-enabled Whittle Index (FedTSWI) algorithm to solve this multi-agent online RMAB problem. The FedTSWI algorithm enjoys a high communication and computation efficiency, and a privacy guarantee. Moreover, we derive a regret upper bound for the FedTSWI algorithm. Finally, we demonstrate the effectiveness of the proposed algorithm on the case of online multi-user multi-channel access. Numerical results show that the proposed algorithm achieves a fast convergence rate of$\mathcal {O}(\sqrt{T\log (T)})$and better performance compared with baselines. More importantly, its sample complexity reduces sublinearly with the number of agents.

Joint modulation-coding scheme (MCS) adaptation and resource block (RB) allocation is an effective approach to guarantee different quality of service (QoS) requirements of all UEs under dynamic network environments. In this article, we consider a fifth generation (5G) cellular network with time-varying wireless channels, in which the BS serves multiple user equipments (UEs) under limited available RBs. We aim to minimize the total RB consumption subject to the rigorous constraints of each UE's QoS requirement. To attain this objective, this paper puts forth an online learning technique, referred to as integrated Deep Reinforcement learning and stable Matching (DeepRM), in the sense that the MCS adaptation and RB allocation decisions are conducted without acquiring the real-time channel quality indicator (CQI) feedback. DeepRM is a closed-loop framework, where the output of deep reinforcement learning (DRL) is imported into the stable matching to guide optimal RB allocation whilst the output of stable matching is fed into the DRL framework to assist efficient MCS decision-making. Specifically, in DeepRM, we first develop a powerful DRL algorithm, termed as Action-and-Reward Branching Deep Q-network (ARBDQ), by incorporating the action branch architecture into conventional DRL and modifying the traditional deep neural network training mechanism, to perform judicious MCS decisions on different links in parallel. Then, a new many-to-one stable matching algorithm, called adaptive deferred acceptance, is exploited to dynamically adjust the RB quota of each UE in a computationally efficient fashion. Simulation results demonstrate that compared with ACO-HM, OLLA-ADA, and ARBDQ-Random algorithms, DeepRM induces much less RB consumption while guaranteeing the QoS requirements of all UEs in various network scenarios. Furthermore, under miscellaneous QoS requirement, number of UEs, and CQI reporting period setups, DeepRM is more robust than other baselines.

Unlicensed millimeter-wave (mmWave) communication is a promising technique for the New Radio-based access to Unlicensed spectrum (NR-U) network to guarantee the ever-increasing data rate demand. A critical challenge of NR-U in unlicensed mmWave bands is to maintain equitable and harmonious coexistence with the original Wireless Gigabit (WiGig) network. In this article, we develop an intelligent joint codebook selection and user equipment (UE) scheduling scheme for mmWave NR-U and WiGig coexistence networks. Specifically, we first formulate the joint problem as a two-time scale system, wherein the codebook selection is performed on the large-time scale whilst the UE scheduling is optimized on the small-time scale. To address the multi-time scale issue, we put forth a new deep reinforcement learning (DRL) algorithm that enables operations on different time scales to benefit each other towards the target system objective, referred to as layered deep Q-network (L-DQN). Thereafter, with the judicious definitions of the state, action, and reward in L-DQN paradigms, we propose the Deep reinforcement learning based CodeBook selection and UE scheduling (DeepCBU) scheme. DeepCBU aims to attain different trade-offs between two conflicting goals, i.e., i) maximizing the total data rate of NR-U with as little interference to WiGig as possible and ii) guaranteeing the fairness among UEs, e.g., the quality of service (QoS) requirement of each UE. To fulfill this mission, we modify the conventional deep neural network architecture of DeepCBU by introducing the target branch for each objective. The gist is that different target branches evaluate the contribution of DeepCBU's strategy to different goals, and the decision of DeepCBU is determined by all target branches in a weighted fashion. Simulation results demonstrate that compared with DRL-dirLBT, TS-dirLBT, and TS-DRL schemes, DeepCBU is more Pareto efficient even without any prior network knowledge, e.g., UE mobility, random channel fading, and transmissions of WiGig, in terms of the data rate of NR-U, the data rate of WiGig, and the number of satisfied UEs. Furthermore, DeepCBU is robust to miscellaneous QoS requirement setups.

This paper presents the first orthogonal frequency-division multiplexing(OFDM)-based digital over-the-air computation (AirComp) system for wireless federated edge learning, where multiple edge devices transmit model data simultaneously using non-orthogonal OFDM subcarriers, and the edge server aggregates data directly from the superimposed signal. Existing analog AirComp systems often assume perfect phase alignment via channel precoding and utilize uncoded analog transmission for model aggregation. In contrast, our digital AirComp system leverages digital modulation and channel codes to overcome phase asynchrony, thereby achieving accurate model aggregation for phase-asynchronous multi-user OFDM systems. To realize a digital AirComp system, we develop a medium access control (MAC) protocol that allows simultaneous transmissions from different users using non-orthogonal OFDM subcarriers, and put forth joint channel decoding and aggregation decoders tailored for convolutional and LDPC codes. To verify the proposed system design, we build a digital AirComp prototype on the USRP software-defined radio platform, and demonstrate a real-time LDPC-coded AirComp system with up to four users. Trace-driven simulation results on test accuracy versus SNR show that: 1) analog AirComp is sensitive to phase asynchrony in practical multi-user OFDM systems, and the test accuracy performance fails to improve even at high SNRs; 2) our digital AirComp system outperforms two analog AirComp systems at all SNRs, and approaches the optimal performance when SNR$\geq$6 dB for two-user LDPC-coded AirComp, demonstrating the advantage of digital AirComp in phase-asynchronous multi-user OFDM systems.

This paper presents a Long Range (LoRa) data aggregation system (LoRaPDA) that aggregates data (e.g., sum, average, min, max) directly in the physical layer. In particular, after coordinating a few nodes to transmit their data simultaneously, the gateway leverages a new multi-packet reception (MPR) approach to compute aggregate data from the phase-asynchronous superimposed signal. Different from the analog approach which requires additional power synchronization and phase synchronization, our MRP-based digital approach is compatible with commercial LoRa nodes and is more reliable. Different from traditional MPR approaches that are designed for the collision decoding scenario, our new MPR approach allows simultaneous transmissions with small packet arrival time offsets, and addresses a new co-located peak problem through the following components: 1) an improved channel and offset estimation algorithm that enables accurate phase tracking in each symbol, 2) a new symbol demodulation algorithm that finds the maximum likelihood sequence of nodes’ data, and 3) a soft-decision packet decoding algorithm that utilizes the likelihoods of several sequences to improve decoding performance. Trace-driven simulation results show that the symbol demodulation algorithm outperforms the state-of-the-art MPR decoder by 5.3$\times$in terms of physical-layer throughput, and the soft decoder is more robust to unavoidable adverse phase misalignment and estimation error in practice. Moreover, LoRaPDA outperforms the state-of-the-art MPR scheme by at least 2.1$\times$for all SNRs in terms of network throughput, demonstrating quick and reliable data aggregation.

As the cloud is pushed to the edge of the network, resource allocation for user experience improvement in mobile edge clouds (MEC) is increasingly important and faces multiple challenges. This paper studies quality of experience (QoE)-oriented resource allocation in MEC while considering user diversity, limited resources, and the complex relationship between allocated resources and user experience. We introduce a closed-loop online resource allocation (CORA) framework to tackle this problem. It learns the objective function of resource allocation from the historical dataset and updates the learned model using the online testing results. Due to the learned objective model is typically non-convex and challenging to solve in real-time, we leverage the Lyapunov optimization to decouple the long-term average constraint and apply the prime-dual method to solve this decoupled resource allocation problem. Thereafter, we put forth a data-driven optimal online queue resource allocation (OOQRA) algorithm and a data-driven robust OQRA (ROQRA) algorithm for homogenous and heterogeneous user cases, respectively. Moreover, we provide a rigorous convergence analysis for the OOQRA algorithm. We conduct extensive experiments to evaluate the proposed algorithms using the synthesis and YouTube datasets. Numerical results validate the theoretical analysis and demonstrate that the user complaint rate is reduced by up to 100% and 18% in the synthesis and YouTube datasets, respectively.

Unmanned aerial vehicles (UAVs) can be utilized as aerial base stations (ABSs) to provide wireless connectivity for ground users (GUs) in various emergency scenarios. However, it is a NP-hard problem with exponential complexity in M and N, in order to maximize the coverage rate of M GUs by jointly placing N ABSs with limited coverage range. The problem is further complicated when the coverage range becomes irregular due to site-specific blockages (e.g., buildings) on the air-ground channel, and/or when the GUs are moving. To address the above challenges, we study a multi-ABS movement optimization problem to maximize the average coverage rate of mobile GUs in a site-specific environment. The Spatial Deep Learning with Multi-dimensional Archive of Phenotypic Elites (SDL-ME) algorithm is proposed to tackle this challenging problem by 1) partitioning the complicated ABS movement problem into ABS placement sub-problems each spanning finite time horizon; 2) using an encoder-decoder deep neural network (DNN) as the emulator to capture the spatial correlation of ABSs/GUs and thereby reducing the cost of interaction with the actual environment; 3) employing the emulator to speed up a quality-diversity search for the optimal placement solution; and 4) proposing a planning-exploration-serving scheme for multi-ABS movement coordination. In particular, the locations of ABSs/GUs are converted into grid pattern representations, whose dimension and associated DNN complexity are invariant with arbitrarily large M and/or N. Moreover, the virtual emulator-planning combined with the actual site-deployment effectively compensates for the prediction errors due to model approximation. Numerical results demonstrate that the proposed approach significantly outperforms the benchmark Deep Reinforcement Learning (DRL)-based method and other two baselines in terms of average coverage rate, training time and/or sample efficiency. Moreover, with one-time training, our proposed method can be applied in scenarios where the number of ABSs/GUs dynamically changes on site and/or with different/varying GU speeds, which is thus more robust and flexible compared with conventional DRL-based methods.

This paper investigates the coexistence of the New Radio-based access to Unlicensed spectrum (NR-U) network and the Wireless Gigabit (WiGig) network in unlicensed millimeter-wave (mmWave) bands. To enable the NR-U network to achieve equitable and harmonious spectrum sharing with WiGig systems, we develop two new classes of user equipment (UE) scheduling schemes by exploiting the deep reinforcement learning (DRL) technique. Specifically, we first propose the distributed deep reinforcement learning scheduling (DeepDS) scheme, wherein multiple deep neural networks (DNNs) are used to make decisions for different panels in an independent fashion. Thereafter, to reduce the computational cost of adopting multiple DNNs, we design the centralized deep reinforcement learning scheduling (DeepCS) scheme that introduces the shared DNN framework to perform decisions for all panels in parallel at one time. The objective of both DeepDS and DeepCS is to maximize the total data rate of the NR-U network with as little interference to WiGig systems as possible, while satisfying the quality of service (QoS) requirement for each UE. We first formulate this problem into the constrained Markov decision process framework. To address the multi-constraint issue, we put forth a new DRL algorithm that incorporates the Lagrangian primal-dual optimization into the deep Q-network framework, referred to as adaptive multi-constraint deep Q-network (AMC-DQN). With AMC-DQN, both DeepDS and DeepCS can achieve their goals even without acquiring prior operations about the WiGig network. Simulation results show that compared with the state-of-the-art omniLBT and dirLBT, both DeepDS and DeepCS yield significant performance benefits in terms of the total network data rate. We also demonstrate the ability of DeepDS and DeepCS to satisfy the QoS requirements of different UEs and their robustness against various simulation setups. Furthermore, compared with DeepDS, DeepCS can save a large amount of computational cost although at the expense of a slightly lower data rate.

Full-duplex (FD) technology has the potential to achieve high-rate transmission in millimeter wave (mmWave) communication. Hybrid precoding has the capability to improve the spectral efficiency in the FD mmWave system. In this paper, we consider jointly designing the downlink hybrid precoding and uplink transmit power in an FD mm Wave system. First, we build an iterative framework based on the penalty dual decomposition (PDD) method. However, the computational complexity of the iterative PDD algorithm will increase due to matrix inversion operation and the number of iterations. Therefore, we propose a deep unfolding-aided approach termed DUPFD to unfold the PDD method into a layered neural network in the FD mm Wave system. In DUPFD, we introduce trainable parameters and replace matrix inversion operations with matrix multiplication. The Lagrangian dual learning is utilized to satisfy power constraints dynamically. Simulation results indicate that the proposed DUPFD achieves comparable spectral efficiency to the PDD method with significantly reduced computational complexity. Moreover, compared with half-duplex systems, the DUPFD improves the spectral efficiency by nearly 54%.

Intelligent reflecting surface (IRS) is regarded as a revolutionary paradigm that can reconfigure the wireless propagation environment for enhancing the desired signal and/or weakening the interference, and thus improving the quality of service (QoS) for communication systems. In this paper, we propose an IRS-aided sectorized BS design where the IRS is mounted in front of a transmitter (TX) and reflects/reconfigures signal towards the desired user equipment (UE). Unlike prior works that address link-level analysis/optimization of IRS-aided systems, we focus on the system-level three-dimensional (3D) coverage performance in both single-/multiple-cell scenarios. To this end, a distance/angle-dependent 3D channel model is considered for UEs in the 3D space, as well as the non-isotropic TX beam pattern and IRS element radiation pattern (ERP), both of which affect the average channel power as well as the multi-path fading statistics. Based on the above, a general formula of received signal power in our design is obtained, along with derived power scaling laws and upper/lower bounds on the mean signal/interference power under IRS passive beamforming or random scattering. Numerical results validate our analysis and demonstrate that our proposed design outperforms the benchmark schemes with fixed BS antenna patterns or active 3D beamforming. In particular, for aerial UEs that suffer from strong inter-cell interference, the IRS-aided BS design provides much better QoS in terms of the ergodic throughput performance compared with benchmarks, thanks to the IRS-inherent double pathloss effect that helps weaken the interference.

This paper presents Deep Reinforcement Learning (DRL)-based channel allocation algorithms for Wi-Fi networks with channel bonding capability. In particular, the proposed DRL algorithms allocate the primary channel and the maximal bonding bandwidth for each access point (AP). Existing DRL-based channel allocation algorithms assume a pre-known static interference model between APs, which cannot be accurately obtained in the hidden terminal scenario and the hidden channel scenario where APs have different sensing capabilities depending on the used channels. In contrast, our proposed DRL algorithm leverages the observed throughput as a reward to learn the interference relationship automatically, and implement centralized and distributed algorithms based on Proximal Policy Optimization (PPO) to learn and optimize channel allocation policies for improved performance. Simulation results show that the proposed methods outperform traditional methods in terms of network throughput in scenarios with hidden terminals and channels, and also perform well in scenarios with dynamic traffic loads. The proposed algorithms are more suitable for practical applications since no prior system knowledge is required.

In this paper, we study the latency minimization problem for a wireless federated learning (FL) system with heterogeneous computation capability, where different edge devices perform different numbers of local updates in each communication round. We formulate a total latency minimization problem, taking into account both the communication and computation latency in the whole FL procedure. We reveal that decoupling the resource allocation variables from the model convergence is essential to reduce the problem to a single-round latency minimization problem. To solve this simplified problem, we propose an alternating optimization scheme to jointly consider communication and computation resource allocation and mitigate the straggler effect. We prove that the resulting sub-problems, i.e., bandwidth and computation capacity allocation, are both convex and can be optimally solved in closed form, respectively. Simulations show that compared with the baseline scheme that allocates the communication and computation resources equally across edge devices, the proposed scheme can achieve single-round latency reduction.

Wireless cut-through transmission, based on in-band full-duplex (FD) technique, has a great potential to improve the end-to-end throughput of a multi-hop wireless network via enabling simultaneous multi-hop relaying transmissions in the same frequency band. Different from single-hop relaying transmission, cut-through transmission has multiple relaying transmission modes, which makes it more difficult to achieve the maximum end-to-end throughput via improving the spatial reuse in a multi-hop wireless network. The goal of this paper is to improve the end-to-end throughput in a multi-hop wireless network with cut-through capability. In particular, we first establish an effective analytical model and find that the achievable end-to-end throughput is mainly determined by the cut-through mode, the spatial reuse factor, the protocol overhead, and the channel rate. Then, we give a comprehensive analysis for these factors in a string-topology multi-hop wireless network. We design a low-overhead distributed medium access control (MAC) protocol, and propose a transmit-delay mechanism to improve spatial reuse and link scheduling. Furthermore, we design an algorithm to adaptively select the optimal transmission parameters, including the cut-through mode, the spatial reuse factor, and the channel rate. Extensive simulations show that the proposed MAC design can effectively improve the end-to-end throughput by more than 55 percent, compared with the state-of-the-art protocol.

Due to the harsh propagation environment, limited bandwidth, and constrained battery life, transmission efficiency is a crucial issue for underwater acoustic (UWA) communications. This paper studies the link adaptation problem of a single UWA link by jointly selecting the transmission frequency and data rate. Since the current UWA channel lacks a universal model, we formulate this joint selection problem as a model-based stochastic multi-armed bandit (SMAB) problem. Thereafter, we propose three algorithms to solve this model-based SMAB problem under the settings of the stationary channel, non-stationary channel, and large arm (i.e., frequency and rate pair) space. For the stationary channel, we propose a unimodal objective-based Thompson sampling (UO-TS) algorithm by exploiting the unimodal feature of the objective function. For the non-stationary channel, we put forth a hybrid change detection UO-TS (HCD-UO-TS) algorithm based on the features of the unimodal objective function and non-stationary channel. For the large arm space, we propose an iterative boundary-shrinking TS (IBS-TS) algorithm by using the logistic regression-based arm classification model. These algorithms are all model-based and have low complexity and a fast convergence rate. In addition, we derive an upper regret bound for the UO-TS algorithm. Numerical results show that the proposed algorithms outperform the state-of-the-art bandit algorithms and are not sensitive to the arm space.

This paper considers a mobile edge cloud (MEC) system where several distributed users collaboratively learn a global model by exploiting the over-the-air computing (AirComp) aided federated learning (FL) mechanism. Previous works focus on the FL training process, ignoring the generalization ability of the trained global model. To overcome this, we propose a novel AirComp-aided FL and federated analytics (FL&FA) framework to improve the generalization ability by making full use of user data and opinions. We first formulate this problem as an online user selection problem. Then, we further model it as a stochastic multi-armed bandit (SMAB) framework, where arms are the decentralized users and rewards are user opinions in FA. To tackle the decentralized feature among users, we put forth a belief propagation-based upper confidence bound (BP-UCB) algorithm to solve this SMAB problem. In addition, we derive an upper regret bound for the BP-UCB algorithm, which increases logarithmically over time. Simulation results demonstrate that the proposed algorithm is close to the optimal solution by less than 3.0% and has a fast convergence rate among existing methods.

This paper presents the first broadband digital over-the-air computation (AirComp) system for phase asynchronous OFDM-based federated edge learning systems. Existing analog AirComp systems often assume perfect phase alignment via channel precoding and utilize uncoded analog modulation for model aggregation. In contrast, our digital AirComp system leverages digital modulation and channel codes to overcome phase asynchrony, thereby achieving accurate model aggregation in the asynchronous multi-user OFDM systems. To realize a digital AirComp system, we propose a non-orthogonal multiple access protocol that allows simultaneous transmissions from multiple edge devices, and present a full-state joint channel decoding and aggregation (Jt-CDA) decoder. To reduce the computation complexity, we further present a reduced-complexity Jt-CDA decoder, and its arithmetic sum bit error rate performance is similar to that of the full-state joint decoder for most signal-to-noise ratio (SNR) regimes. Simulation results on test accuracy of CIFAR10 dataset versus SNR show that: 1) analog AirComp systems are sensitive to phase asynchrony under practical setup, and the test accuracy performance exhibits an error floor even at high SNR regime; 2) our digital AirComp system outperforms an analog AirComp system by at least 1.5 times when SNR≥9dB, demonstrating the advantage of digital AirComp in asynchronous multi-user OFDM systems.

This paper considers the coexistence of the New Radio-based access to unlicensed spectrum (NR-U) network and the Wireless Gigabit (WiGig) network in unlicensed millimeter-wave (mmWave) bands. We aim to design a new scheduling scheme for the NR-U network to maximize its total data rate while satisfying the quality of service (QoS) requirement for each user equipment (UE). Specifically, we first formulate this problem into the constrained Markov decision process (CMDP) framework. Then the Lagrangian duality method is applied to relax the hard constraints in CMDP into the soft constraints. To address the multi-constraint issue, we put forth a new deep reinforcement learning (DRL) algorithm that incorporates the constraints into the DRL framework, referred to as adaptive multi-constraint deep Q-network (AMC-DQN). A prominent advantage of AMC-DQN is that it enables the NR-U network to access the shared spectrum without acquiring prior information about the WiGig network. Simulation results show that compared with the omnidirectional listen-before-talk (omniLBT) and directional LBT (dirLBT), the AMC-DQN based scheduling scheme yields the total data rate gain of the NR-U network by 158% and 38%, respectively. The results also demonstrate the ability of AMC-DQN to satisfy the QoS requirements of different UEs. Furthermore, AMC-DQN brings less interference to the WiGig network in comparison to baselines.

This paper presents a Long Range (LoRa) physical-layer data aggregation system (LoRaPDA) that aggregates data (e.g., sum, average) directly in the physical layer. In particular, after coordinating a few nodes to transmit their data simultaneously, the gateway leverages a new multi-packet reception (MPR) approach to compute aggregate data from the asynchronous superimposed signal. Different from traditional MPR approaches that are designed for the uncoordinated collision decoding scenario, our MPR approach allows simultaneous transmissions with small packet arrival time offsets, and addresses the new co-located peak problem through the following components: 1) an improved channel and offset estimation algorithm that enables accurate phase tracking in each symbol; 2) a new symbol demodulation algorithm that finds the maximum likelihood sequence of nodes' data; and 3) a soft-decision packet decoding algorithm that keeps the likelihoods of several sequences to improve decoding performance. Trace-driven simulation results show that the symbol demodulation algorithm outperforms a state-of-the-art MPR decoder by 5.4× in terms of physical-layer throughput, and the soft decoder is more robust to unavoidable adverse phase misalignment and estimation error in practice. Moreover, LoRaPDA outperforms a state-of-the-art MPR scheme by at least 2.1× for all SNRs in terms of network throughput, demonstrating quick and reliable data aggregation.

LoRa networks are pivotally enabling Long Range connectivity to low-cost and power-constrained user equipments (UEs) in a wide area, whereas a critical issue is to effectively allocate wireless resources to support potentially massive UEs while resolving the prominent near–far fairness issue, which is challenging due to the lack of tractable analytical model and the practical requirement for low-complexity and low-overhead design. Leveraging on stochastic geometry, especially the Poisson rain model, we derive (semi-) closed-form formulas for the aggregate interference distribution, packet success probability, and hence, system throughput in both single-cell and multicell setups with frequency reuse, by accounting for channel fading, random UE distribution, partial packet overlapping, and/or multi-gateway (GW) packet reception. The analytical formulas require only average channel statistics and spatial UE distribution, which enable tractable network performance evaluation and incubate our proposed iterative balancing (IB) method that quickly yields high-level policies of joint spreading factor (SF) allocation, power control, and duty-cycle adjustment for gauging the average max–min UE throughput or supported UE density with rate requirements. Numerical results validate the analytical formulas and the effectiveness of our proposed optimization scheme, which greatly alleviate the near–far fairness issue and reduces the spatial power consumption, while significantly improving the cell-edge throughput as well as the spatial (sum) throughput for the majority of UEs, by adapting to the UE/GW densities.

This paper considers a resource allocation problem where several Internet-of-Things (IoT) devices send data to a base station (BS) with or without the help of the reconfigurable intelligent surface (RIS) assisted cellular network. The objective is to maximize the sum rate of all IoT devices by finding the optimal RIS and spreading factor (SF) for each device. Since these IoT devices lack prior information of the RISs or the channel state information (CSI), a distributed resource allocation framework with low complexity and learning features is required to achieve this goal. Therefore, we model this problem as a two-stage multi-player multi-armed bandit (MPMAB) framework to learn the optimal RIS and SF sequentially. Then, we put forth an exploration and exploitation boosting (E2Boost) algorithm to solve this two-stage MPMAB problem by combining the$\epsilon $-greedy algorithm, Thompson sampling (TS) algorithm, and non-cooperation game method. We derive an upper regret bound for the proposed algorithm, i.e.,$\mathcal {O}(\log ^{1+\delta }_{2} T)$, increasing logarithmically with the time horizon$T$. Numerical results show that the E2Boost algorithm has the best performance among the existing methods and exhibits a fast convergence rate. More importantly, the proposed algorithm is not sensitive to the number of combinations of the RISs and SFs thanks to the two-stage allocation mechanism, which can benefit the high-density networks.

Long propagation delay that causes throughput degradation of underwater acoustic networks (UWANs) is a critical issue in the medium access control (MAC) protocol design in UWANs. This paper develops a deep reinforcement learning (DRL) based MAC protocol for UWANs, referred to as delayed-reward deep-reinforcement learning multiple access (DR-DLMA), to maximize the network throughput by judiciously utilizing the available time slots resulted from propagation delays or not used by other nodes. In the DR-DLMA design, we first put forth a new DRL algorithm, termed asdelayed-reward deep Q-network (DR-DQN). Then we formulate the multiple access problem in UWANs as a reinforcement learning (RL) problem by defining state, action, and reward in the parlance of RL, and thereby realizing the DR-DLMA protocol. In traditional DRL algorithms, e.g., the original DQN algorithm, the agent can get access to the “reward” from the environment immediately after taking an action. In contrast, in our design, the “reward” (i.e., the ACK packet) is only available after twice the one-way propagation delay after the agent takes an action (i.e., to transmit a data packet). The essence of DR-DQN is to incorporate the propagation delay into the DRL framework and modify the DRL algorithm accordingly. In addition, in order to reduce the cost of online training deep neural network (DNN), we provide a nimble training mechanism for DR-DQN. The optimal network throughputs in various cases are given as a benchmark. Simulation results show that our DR-DLMA protocol with nimble training mechanism can: (i) find the optimal transmission strategy when coexisting with other protocols in a heterogeneous environment; (ii) outperform state-of-the-art MAC protocols (e.g., slotted FAMA and DOTS) in a homogeneous environment; and (iii) greatly reduce energy consumption and run-time compared with DR-DLMA with traditional DNN training mechanism.

Age-of-information (AoI) based minimization problems have been widely considered in Internet-of-Things (IoT) networks with the settings of multi-source single-channel systems and multi-source multi-channel systems. Most existing works are limited to either the case of identical multi-channel or independent sources. In this paper, we study this problem under the identical and non-identical multi-channel, as well as the correlated sources setting. This correlation defines the case when updating a source’s AoI; others correlated to this one will also reveal partial information. To tackle this AoI-based minimization problem, we formulate it as a correlated restless multi-armed bandit (CRMAB) problem. By decoupling the CRMAB problem into$N$independent single-armed bandit problems, we derive the closed-form expressions of the generalized Whittle index (GWI) and the generalized partial Whittle index (GPWI) under the identical channel and the non-identical channel settings, respectively. Then, we put forth the GWI-based and GPWI-based scheduling policies to solve this AoI-based minimization problem. In addition, we provide two lower numerical performance bounds for the proposed policies by solving the relaxed Lagrange problem of the decoupled CRMAB. Numerical results show that the proposed policies can achieve these lower bounds and outperform the state-of-the-art scheduling policies. Compared with the case of independent sources, the performance of the proposed policies in the case of correlated sources improves significantly, especially in high-density networks.

Over-the-air computation (AirComp) based federated learning (FL) is capable of achieving fast model aggregation by exploiting the waveform superposition property of multiple-access channels. However, the model aggregation performance is severely limited by the unfavorable wireless propagation channels. In this paper, we propose to leverage intelligent reflecting surface (IRS) to achieve fast yet reliable model aggregation for AirComp-based FL. To optimize the learning performance, we present the convergence analysis of our proposed IRS-assisted AirComp-based FL system, based on which we propose to maximize the number of scheduled devices of each communication round under certain mean-squared error (MSE) requirements. To tackle the formulated highly-intractable problem, we propose a two-step optimization framework. Specifically, we induce the sparsity of device selection in the first step, followed by solving a series of MSE minimization problems to find the maximum feasible device set in the second step. We then propose an alternating optimization framework, supported by the difference-of-convex programming for low-rank optimization, to efficiently design the aggregation beamformers at the BS and phase shifts at the IRS. Simulation results demonstrate that our proposed algorithm and the deployment of an IRS can achieve a higher FL prediction accuracy than the baseline schemes.

This paper investigates a new medium access control (MAC) protocol for multi-channel heterogeneous networks (HetNets) based on deep reinforcement learning (DRL), referred to as multi-channel deep-reinforcement learning multiple access (MC-DLMA). Specifically, we consider a HetNet where different radio networks adopt different MAC protocols to transmit data packets to a common access point on different wireless channels. Three key challenges for the MC-DLMA node are (i) no environmental knowledge is known in advance; (ii) the channels in HetNets are allocated to nodes using different MAC protocols; (iii) the capacities of different channels may be different. The main goal of MC-DLMA is to find an optimal access policy to transmit on those pre-allocated channels and expedite more efficient spectrum utilization. Due to the complex temporal correlation of spectrum states in HetNets, the traditional DRL technique, e.g., original deep Q-network (DQN) algorithm, is no longer applicable to our problem. In our MC-DLMA design, an advanced class of recurrent neural network, termed as Gated Recurrent Unit (GRU), is embedded into the original DQN technique to aggregate observations over time and reason the underlying temporal feature in multi-channel HetNets. Furthermore, we analytically give the optimal spectrum access patterns and derive the optimal throughputs in various HetNet scenarios. With judicious definitions of the state, action, and reward function in the parlance of the DRL framework, simulation results show that MC-DLMA can (i) find the optimal spectrum access strategies in various HetNets, (ii) outperform the random access policy, the whittle index policy, and the original DQN, (iii) perform cooperative transmission in a fully distributed manner in the presence of multiple agents, and (iv) adapt well to the environmental changes.

Unmanned aerial vehicles (UAVs), especially fixed-wing ones that withstand strong winds, have great potential for oceanic exploration and research. This paper studies a UAV-aided maritime data collection system with a fixed-wing UAV dispatched to collect data from marine buoys. We aim to minimize the UAV’s energy consumption in completing the task by jointly optimizing the communication time scheduling among the buoys and the UAV’s flight trajectory subject to wind effect. The conventional successive convex approximation (SCA) method can provide efficient sub-optimal solutions for collecting small/moderate data volume, whereas the solution heavily relies on trajectory initialization and has not explicitly considered wind effect, while the computational/trajectory complexity both become prohibitive for the task with large data volume. To this end, we propose a new cyclical trajectory design framework with tailored initialization algorithm that can handle arbitrary data volume efficiently, as well as a hybrid offline-online (HO2) design that leverages convex stochastic programming (CSP) offline based on wind statistics, and refines the solution by adapting online to real-time wind velocity. Numerical results show that our optimized trajectory can better adapt to various setups with different target data volume and buoys’ topology as well as various wind speed/direction/variance compared with benchmark schemes. In particular, our proposed HO2 design can effectively adapt to random wind variations with feasible and robust online operation, and proactively exploit the wind for further energy savings in both single-buoy and multi-buoy scenarios.

In this paper, we consider a full-duplex enabled cellular system operated in the unlicensed mmWave band. In particular, the full-duplex gNB is equipped with multiple antennas and applies hybrid beamforming for downlink transmissions, meanwhile mobile users send uplink traffic to the gNB with an omni-directional antenna. In the unlicensed bands, the cellular networks need to ensure a fair coexistence with other systems, such as WiGig. Therefore, a sum rate maximization problem is formulated to improve the throughput of the cellular system meanwhile suppressing its interference to the WiGig system. The formulated problem is non-convex and of high complexity. In order to efficiently address it, the sum rate maximization problem is first reformulated into a weighted mean square error minimization problem, and then the penalty dual decomposition method is applied to jointly optimize the uplink transmit power and downlink hybrid beamforming. Numerical results show that the performance of the proposed unlicensed full-duplex enabled cellular network degrades by nearly 29% in order to fairly coexist with the WiGig system. However, compared with half-duplex systems, applying full-duplex technology to our proposed network can improve the sum rate by nearly 56%.

Intelligent reflecting surface (IRS) is a new and revolutionary technology capable of reconfiguring the wireless propagation environment by controlling its massive low-cost pas-sive reflecting elements. Different from prior works that focus on optimizing IRS reflection coefficients or single-IRS placement, we aim to maximize the minimum throughput of a single-cell mul-tiuser system aided by multiple IRSs, by joint multi-IRS placement and power control at the access point (AP), which is a mixed-integer non-convex problem with drastically increased complexity with the number of IRSs/users. To tackle this challenge, a ring-based IRS placement scheme is proposed along with a power control policy that equalizes the users' non-outage probability. An efficient searching algorithm is further proposed to obtain a close-to-optimal solution for arbitrary number of IRSs/rings. Numerical results validate our analysis and show that our proposed scheme significantly outperforms the benchmark schemes without IRS and/or with other power control policies. Moreover, it is shown that the IRSs are preferably deployed near AP for coverage range extension, while with more IRSs, they tend to spread out over the cell to cover more and get closer to target users.

As the cloud is pushed to the edge of the network to promote the development of mobile applications, resource allocation for user experience improvement in mobile edge clouds is facing multiple challenges. In order to serve mobile users, resource allocation in mobile edge clouds needs to be adjusted continuously. Considering that the available resources in mobile edge clouds are generally limited, we propose to optimize resource allocation by minimizing the time-average complaining probability. Specifically, we advocate a feedback-based online resource allocation system, which can effectively improve the overall user experience in mobile edge clouds through online resource allocation. The system runs in a closed-loop fashion: we establish a classifier model to predict user experience, use the prediction results as the target of resource allocation model to guide the resource allocation explicitly, and then adjust the classifier model based on the updated overall dataset. The online resource allocation problem turns out to be a stochastic problem, for which we design an online queue resource allocation algorithm (OQRAA) supported by Lyapunov optimization technique. The proposed system enables us to explore the quantitative relationship between user experience and feature values and leverage this relationship to find the optimal resource allocation policy immediately upon users’ arrival. The numerical results show that the proposed system has an improvement of 200% in reducing the time-average complaining probability compared with the baseline algorithm.

Full-duplex operation can enhance the transmission capacity of the communication systems, and energy harvest is a promising technique to prolong the lifetime of wireless nodes by utilizing the radio-frequency signals. In this paper, we investigate the throughput performance of a full-duplex two-way energy harvesting (EH) capable relay system. In particular, we focus on the time switching-based relaying (TSR) protocol for EH and amplify-and-forward for information transmission. The full-duplex system has two different ways of applying TSR protocol, named time switching-based full-duplex relaying I (TS-FDR-I) protocol and time switching-based full-duplex relaying II (TS-FDR-II) protocol. We consider both protocols and successfully obtain the outage probability and the system throughput for each protocol. The throughput of the system with TS-FDR-I protocol is a function of time-switching (TS) ratio, and we prove that the upper bound function of it is a log-concave function so that the close-to-optimal throughput and TS ratio could be computed efficiently. Meanwhile, the system with TS-FDR-II protocol has a fixed TS ratio, and its throughput is only related with signal-to-noise ratio (SNR). Simulation results show that the throughput function for TS-FDR-I protocol is log-concave and the performance of TS-FDR-I protocol is better than TS-FDR-II protocol when SNR is higher than 22dB.

This article studies the network-level throughput of a full-duplex (FD)-enabled CSMA network, considering the link's transmit power (TP) control, carrier-sensing threshold (CST) adjustment, and logarithm access intensity (LAI) adaptation. With the FD technique, a transmitter-receiver pair can transmit and receive simultaneously in the same frequency band. We aim to find the best combination of TP, CST, and LAI for each link to maximize the FD-CSMA network throughput. However, adjusting each link's TP and CST will change the network's carrier-sensing relation or contention graph, consequently leading to a computationally intractable network optimization problem. On the other hand, it is difficult to jointly optimize these three parameters in a fully distributed network. To overcome these, we first decompose this network optimization problem into two subproblems: 1) a joint control and scheduling problem in the transport- and media access control (MAC)-layer and 2) a parameter selection problem in the PHY-layer. Then, the multiplayer multiarmed bandit (MPMAB) framework has been introduced to address this problem by solving the two subproblems alternately. We put forth a fully distributed algorithm, named the stochastic and adversarial optimal FD-CSMA (SAO-FD-CSMA) algorithm, to solve the MPMAB problem by taking advantage of the optimization tool and the bandit theory. The numerical results show that the proposed algorithm outperforms the state-of-the-art bandit algorithms and can improve the network throughput by 43% compared with the random selection method.

This paper considers the problem of efficient spectrum utilization in heterogeneous wireless networks (HetNets), wherein different radio networks adopt different medium access control (MAC) protocols to transmit data packets to a common access point on different wireless channels. To allow emerging radio nodes to transmit on those pre-allocated channels and to expedite more efficient spectrum utilization, we exploit the advanced deep reinforcement learning technique to develop a new generation of MAC protocols, referred to as multi-channel deep-reinforcement learning multiple access (MC-DLMA). The emerging radio nodes that adopt MC-DLMA can make full use of the underutilized spectrum resource and maximize the sum throughput of the overall HetNet by learning the transmission patterns of the existing radio nodes. For benchmarking, we derive the optimal throughputs analytically and demonstrate that MC-DLMA can achieve the near-optimal results. Moreover, compared with other baselines (e.g., the Whittle Index policy and the random access policy), our MC-DLMA can significantly improve the sum throughput of the HetNet in various scenarios.

Unmanned aerial vehicles (UAVs), especially fixed-wing ones that withstand strong winds, have great potential for oceanic exploration and research. This paper studies a UAVaided maritime data collection system with a fixed-wing UAV dispatched to collect data from marine buoys. We aim to minimize the UAV's energy consumption in completing the task by jointly optimizing the communication time scheduling among the buoys and the UAV's flight trajectory subject to wind effect, which is a non-convex problem and difficult to solve optimally. Existing techniques such as the successive convex approximation (SCA) method provide efficient sub-optimal solutions for collecting small/moderate data volume, whereas the solution heavily relies on the trajectory initialization and has not explicitly considered the wind effect, while the computational complexity and resulted trajectory complexity both become prohibitive for the task with large data volume. To this end, we propose a new cyclical trajectory design framework that can handle arbitrary data volume efficiently subject to wind effect. Specifically, the proposed UAV trajectory comprises multiple cyclical laps, each responsible for collecting only a subset of data and thereby significantly reducing the computational/trajectory complexity, which allows searching for better trajectory initialization that fits the buoys' topology and the wind. Numerical results show that the proposed cyclical scheme outperforms the benchmark oneflight-only scheme in general. Moreover, the optimized cyclical 8-shape trajectory can proactively exploit the wind and achieve lower energy consumption compared with the case without wind.

Unmanned aerial vehicles (UAVs) can be utilized as aerial base stations (ABSs) to assist terrestrial infrastructure for keeping wireless connectivity in various emergency scenarios. To maximize the coverage rate of N ground users (GUs) by jointly placing multiple ABSs with limited coverage range is known to be a NP-hard problem with exponential complexity in N. The problem is further complicated when the coverage range becomes irregular due to site-specific blockage (e.g., buildings) on the air-ground channel in the 3-dimensional (3D) space. To tackle this challenging problem, this paper applies the Deep Reinforcement Learning (DRL) method by 1) representing the state by a coverage bitmap to capture the spatial correlation of GUs/ABSs, whose dimension and associated neural network complexity is invariant with arbitrarily large N; and 2) designing the action and reward for the DRL agent to effectively learn from the dynamic interactions with the complicated propagation environment represented by a 3D Terrain Map. Specifically, a novel two-level design approach is proposed, consisting of a preliminary design based on the dominant line-of-sight (LoS) channel model, and an advanced design to further refine the ABS positions based on site-specific LoS/non-LoS channel states. The double deep Q-network (DQN) with Prioritized Experience Replay (Prioritized Replay DDQN) algorithm is applied to train the policy of multi-ABS placement decision. Numerical results show that the proposed approach significantly improves the coverage rate in complex environment, compared to the benchmark DQN and K-means algorithms.

Due to limited acoustic bandwidth and constrained battery life, transmission efficiency is a crucial issue in underwater acoustic communication (UAC). This paper studies the problem of joint frequency and transmission rate selection of a single link in UAC so as to maximize the link's average throughput. To handle this problem, we first describe this problem as a traditional optimization form and show the challenges located in solving it. Then we resort to the online learning theory by modeling this problem as a multi-armed bandit (MAB) framework. Through taking full advantage of the unimodality feature of the problem structure, we propose an algorithm called UOTS (unimodal objective based Thompson sampling algorithm) to solve this MAB problem. A finite-time analysis of the upper regret bound has been derived for the proposed algorithm. Several numerical results are also provided to verify the proposed algorithm and demonstrate that UOTS outperforms the current state-of-the-art algorithms. It is interesting that the performance loss of UOTS does not depend on the number of available pairs of frequency and rate, which can be much useful in the practical implementation.

In-band full-duplex (FD) technique can efficiently improve the end-to-end throughput of a multi-hop network via enabling multi-hop FD amplify-and-forward relaying (cut-through) transmission. This paper investigates the optimal hop size of a cut-through transmission and spatial reuse to achieve the maximum achievable end-to-end throughput of a multi-hop network. In particular, we consider spatial reuse and establish an interference model for a string-topology multi-hop network with x-hop cut-through transmission, and show that the maximum achievable end-to-end throughput is a function of x and the spatial separation between two concurrently active cut-through transmissions. Through extensive numerical studies, we show that the achievable date rate of a cut-through transmission drastically decreases along with the increase of the hop size x. Furthermore, we find that the 2-hop cut-through transmission mode can always achieve the maximum end-to-end throughput using Shannon Capacity formula if the spatial reuse is properly addressed. On the other hand, the results show that the 5-hop cut-through transmission mode can obtain the maximum end-to-end throughput with discrete channel rates when the self-interference cancellation is perfect and the hop distance is small.

Network resource allocation is an important issue for designing energy-harvesting wireless sensor networks (EH-WSNs). This article considers the capacity assignment problem in EH-WSNs with the interference channel for fixed data and energy flow topologies. We focus on the optimal data rates, power allocations, and energy transfers, minimizing the total network delay for the network. We first consider a simplified model where the data flow is fixed on each data link and optimizes transmit power at each sensor node for a single energy harvest in a time slot. However, the optimization problem is nonconvex, making it difficult to find the optimal solution. Unlike the most traditional methods that approximate the original optimization problem as a convex optimization problem by considering the relatively high signal-to-interference-plus-noise ratio (SINR), this article aims to directly solve the original nonconvex formulation by employing a powerful evolutionary algorithm, i.e., negatively correlated search (NCS). Then, we investigate the joint optimization problem of capacity and flow for the entire EH-WSNs, and develop a novel multiobjective NCS algorithm (MOEA/D-NCS) to deal with the complicated nonlinear constraints and optimize the data rates, power allocations, and energy transfer simultaneously, so as to minimize the total network delay. The numerical results demonstrate that solving the nonconvex problem with approximated approach is a good alternative for solving the approximated convex problem with accurate optimization approaches; the joint optimization of capacity and flow is a good solution for EH-WSNs; and the scheme of partial transmission for data flow is an advantage in respect of decreasing the network delay. The solution of this article could also be beneficial to other complex optimization problems in the wireless network design.

The in-band full-duplexing is a promising technique to boost wireless network throughput by allowing a node to transmit and receive simultaneously. This paper provides a comprehensive investigation on the hidden-node problem that arises in the full-duplex (FD) enabled carrier-sensing multiple-access (CSMA) networks. In particular, we first provide the fundamental conditions that guarantee successful receptions for all the FD transmission cases, and propose an ellipse interference model and an ellipse carrier-sensing model to capture the interference relations and the carrier-sensing mechanism in FD CSMA networks, respectively. We further establish the hidden-node-free design in FD CSMA networks. Specifically, we show the sufficient conditions on the carrier-sensing power threshold that can eliminate hidden-node collisions. We show that compared with half-duplex CSMA networks, the FD CSMA network needs a much smaller carrier-sensing power threshold to prevent hidden-node collisions, which leads to poor network spatial reuse. This motivates us to further propose a new medium access control (MAC) protocol with Full-duplex Enhanced Carrier-Sensing (FECS) mechanism. The FECS-MAC enables the secondary carrier-sensing before starting the secondary transmission. We show that with the secondary carrier-sensing design, the required carrier-sensing powerthreshold can be increased while keeping the network hidden-node free. Therefore, the network spatial reuse and throughput can be significantly improved. Simulation results demonstrate that the FECS-MAC can improve the throughput of dense three-node FD networks by more than 30 percent, compared with relay full-duplex (RFD) MAC protocol proposed in [1].

In this paper, we investigate the energy minimization problem for a cognitive capacity harvesting network (CCHN), where secondary users (SUs) without cognitive radio (CR) capability communicate with CR routers via device-to-device (D2D) transmissions, and CR routers connect with base stations (BSs) via CR links. Different from traditional D2D networks that D2D transmissions share the resource of cellular transmissions in the same cell, we consider the scenario that D2D transmissions share the uplink cellular frequency bands (CFBs) of neighbor cells. To ensure that the transmissions from SUs do not affect the transmissions for the cellular users (CUs) in the neighbor cells, an inter-cell handshake process is proposed. We formulate the energy minimization problem for SUs as a mixed integer non-linear programming (MINLP). To solve this problem, we decompose it into two nested subproblems: a transmit power optimization subproblem and a CR router and uplink CFB selection subproblem. For the first subproblem, it is proved to be convex, and thus can be efficiently solved. For the second subproblem, we propose a two-level nested game theoretic approach to finding its solution. Simulation results show that the proposed algorithms can significantly improve the performance. With the help of CR routers/the neighbor resource sharing, the energy consumption for SUs can be saved around 30%-37% on average.

This paper investigates the network-level throughput of a full-duplex (FD) enabled CSMA network, considering transmit power (TP) control and carrier-sensing threshold (CST) adjustment. With the FD technique, a transmitter-receiver pair can transmit and receive simultaneously in the same frequency band. The motivation is to find an optimal combination of TP and CST for each link so as to maximize the network throughput. The challenge is that adjusting each link's TP and CST will change the network's carrier-sensing relation and interference relation, which consequently leads to a computationally intractable network optimization problem. To overcome the complexity challenge, we model this network throughput maximum problem within a multi-player multi-armed bandit (MP-MAB) framework, in which the players are the FD-enabled links and the arms are the combinations of TP and CST. The proposed framework can also be viewed as an adversarial MP-MAB due to the hostile contention among links. Furthermore, we propose a refined Exponential-weight algorithm for Exploration and Exploitation (Exp3) to solve this adversarial MP-MAB problem. The refined Exp3 algorithm proceeds in epochs and starts with some prior knowledge. The numerical results show that the proposed method can improve the network throughput by more than 42%, compared with the random selection method. Meanwhile, the proposed algorithm exhibits a fast convergence rate in random network scenario.

Optimal energy-delay scheduling for capacity assignment problem in energy harvesting wireless sensor networks (EH-WSNs) with interference channel is addressed for fixed data flows and energy topologies. We formulate the optimization problem for a single time slot and multiple time slots, respectively. We focus on the optimal data rates, power allocations and energy transfers for the optimization problem. The objective is to minimize the total network delay. However, the optimization problem is non-convex, making it difficult to find the optimal solution. Unlike the most traditional methods that approximate the original optimization problem as a convex optimization problem by considering the relatively high Signal-to-Interference-plus-Noise Ratio (SINR), this paper aims to directly solve the original non-convex formulation by employing a powerful evolutionary algorithm, i.e., Negatively Correlated Search (NCS). The simulations under both no-energy-transfer scenario and energy-transfer scenario are carried out, demonstrating that solving the non-convex problem with approximated approach is a good alternative to solving the approximated convex problem with accurate optimization approaches. This idea could also be beneficial to other complex optimization problems in the wireless networks design.

This paper investigates the throughput performance of a two-way energy harvesting (EH) relaying system with analog network coding (ANC) scheme. ANC scheme can improve the transmission efficiency of the two- way relay network. Energy harvest is an emerging solution for prolonging the lifetime of wireless node by availing the ambient RF signals. In partic⌉ular, we focus on the energy harvesting system with the time switching-based relaying (TSR) protocol. We successfully derive the analytical expressions for both the outage probability and the achievable throughput. The analytical result matches with the practical system throughput well. Furthermore, we derive a closed-form approximation of throughput and show that it is a log-concave function of the time-switching (TS) ratio. Therefore, the system throughput optimization can be mathematically for⌉mulated as a quasi-convex optimization problem which can be solved efficiently. Simulation results show that the closed-form expression of throughput is log-concave and the gap between the approximated throughput with the practical optimal throughout is less than 10%.

This paper studies the throughput performance of a two-way energy harvesting relaying system. Network Coding and Energy Harvesting are promising techniques that can improve the transmission efficiency and the energy efficiency of wireless systems, respectively. In particular, we focus on the energy harvesting system with the power splitting-based relaying (PSR) protocol, and consider both the amplify-and-forward (AF) relaying and decode-and-forward (DF) relaying methods for the network coding. We successfully derive the expressions for both the outage probability and the system throughput for each case. It can be shown that DF relaying system outperforms AF relaying system in terms of both the outage probability and the throughput. Furthermore, simulations show that the throughput gain brought by network coding highly depends on the signal-to-noise ratio (SNR) at the receiver. The throughput gain can be up to 33% in the high SNR region.

In this paper, we investigate the sum-rate maximization in the Device-to-Device (D2D) communication underlaying cellular networks, where many cellular users (CUs) share the uplink resource with the D2D pairs. We show that the system sum- rate maximization problem can be formulated as a mixed integer non-linear programming problem, which is NP-hard in general. We circumvent this difficulty by applying the optimization decomposition: 1) Given a resource allocation policy, we derive the optimal Signal to Interference plus Noise Ratio (SINR) threshold to maximize the system sum-rate. 2) We then propose a coalition game approach to further optimize the resource allocation policy, and prove that the proposed coalition game approach can converge to the Nash-stable partition in finite time. Simulation results show that 1) the performance of the coalition game is close to the exhaustive search, but its run-time is much shorter than the exhaustive search; 2) compared with several other resource allocation policies, the coalition game can achieve an average sum-rate improvement of 13%-173%, and has the best resource sharing fairness.

Device-to-device (D2D) communication is a promising technology for improving the performance of proximity-based services. This paper demonstrates how the integration of D2D communication in cellular systems operating under dynamic time division duplex (TDD) can improve energy efficiency. We perform joint optimization of mode selection, uplink/downlink transmission period, and power allocation to minimize the transmission energy consumption while satisfying a traffic requirement. Solutions are developed for two scenarios: with and without interference among D2D communications. Both formulations are expressed as mixed-integer nonlinear programming problems, which are NP-hard in general. We exploit problem structure to develop efficient solutions for both scenarios. For the interference-free case, we design algorithms that find the optimal solution in polynomial time. When considering interference, we propose a customized solver based on branch-and-bound that reduces the search complexity by taking advantage of the problem-specific proprieties. We complement this solver by a more practical heuristic algorithm. Simulation results demonstrate that D2D communications in dynamic TDD systems can yield significant energy savings and improved spectral efficiency compared with the traditional cellular communication. Furthermore, we give analytical characterizations of the receiver locations relative to a given transmitter where D2D communication is optimal. These regions can be surprisingly large and not necessarily circular.

In this paper, we investigate the energy minimization problem with and without network coding (NC) while satisfying the transmission rate requirements in a bidirectional cellular relay network, where a group of mobile users communicate with a base station across a relay node. In particular, we consider the Rayleigh fading channel model and adopt a comprehensive power consumption model in the radio frequency transmission. We show that the problem of minimizing the energy consumption of the bidirectional cellular relay network in NC and non-NC (NNC) schemes can be formulated as a unified sum of fractional programming problem, which is of high complexity to solve in general. Fortunately, we derive the sufficient condition under which the problem is a convex optimization problem, and thus can be solved quite efficiently. In the case that the energy minimizing problem is not convex, we decompose it into two subproblems, and propose an iterative algorithm to solve it. Simulation results show that in NNC and NC schemes, under all configurations of power parameters, the performance of the iterative algorithm is close to the exhaustive search method; but its running time is much shorter than the exhaustive search method. Furthermore, compared with the maximum power transmission policy, the iterative algorithm achieves a maximum energy reduction of 75%-82%. Last but not least, we compare the energy performance of NNC and NC schemes and discuss the effect of the iteration number and the relay node placement.

In this paper, we investigate the throughput capacity of physical-layer network coding (PNC) in a non-all-inclusive carrier-sensing network with IEEE 802.11 distributed coordination function (DCF). In particular, we consider the many-to/from-one bidirectional networks in which a common center node exchanges packets with many other nodes through multihop transmissions. We first analyze the canonical networks with equal-link-length (ELL) and variable-link-length (VLL), respectively, and derive the corresponding analytical network capacity. Simulations show that the throughput capacities are reasonably tight. We further maximize the network capacity by properly selecting the signal-to-interference-plus-noise ratio (SINR) threshold/transmission rate through numerical calculation. Last but not least, we identify the optimal number of hops that has the maximum network throughput. In particular, the four-hop canonical networks have the maximum network throughput, which indicates that in a many-to/from-one network with five or more hops, it is preferable to transmit the packets across the four-hop nodes to make full use of the PNC scheme. Simulation results show that the throughput gain of PNC scheme with and without considering the synchronization cost can, respectively, reach upto 291.7% and 340.6%, compared with the traditional IEEE 802.11 multihop networks without network coding.

In this paper, we investigate the throughput performance of the network coding (NC) schemes under the slotted ALOHA protocol. We consider the all-inclusive-interfering unbalanced network in which two client groups with different numbers of nodes communicate with each other through a relay node. We derive the closed-form expressions of the network throughput under the physical-layer network coding (PNC), traditional high-layer network coding (HNC), and non-network-coding (NNC), respectively. We also show the necessary and sufficient condition to make the relay node unsaturated. From the analytical results, we find that although PNC has better transmission efficiency in the two-way relay channel (TWRC); it does not always have better network throughput when the network has multiple client nodes. To further improve the network throughput, we propose the hybrid NC scheme, which allows the relay node to turn to HNC scheme if it fails to explore the PNC transmission. We further obtain the closed-form expression of the network throughput and the necessary and sufficient condition to make the relay node unsaturated in the hybrid NC scheme. Simulation results show that the hybrid NC scheme has better throughput performance than the PNC, HNC, and NNC schemes. Moreover, we optimize the network throughput of the hybrid NC scheme in terms of the transmission probability of the relay node. Last but not least, we evaluate the throughput performance of hybrid NC scheme through simulations.

Network-assisted Device-to-Device (D2D) communication is a promising technology for improving the performance of proximity-based services. This paper demonstrates how D2D communication can be used to improve the energy-efficiency of cellular networks, leading to a greener system operation and a prolonged battery life of the mobile devices. Assuming a flexible TDD system, we develop optimal mode selection policies for minimizing the energy cost (either from the system or from the device perspective) while guaranteeing a certain rate requirement. The jointly optimal transmit power and time allocation, as well as the optimal mode selection, is found by solving a small convex optimization problem. Special attention is given to the geometrical interpretation of the obtained results. We show that when network energy is the primary concern, D2D mode is preferable in a large portion of the cell. When the device energy consumption is most important, on the other hand, the area where D2D mode is preferable shrinks and becomes close to circular. Finally, we investigate how network parameters affect the range where direct communication is preferred.

We investigate the percolation connectivity of wireless ad hoc networks with directional antennas (called DIR networks). One of major concerns is to derive bounds on the number of edge-disjoint directed paths (or highways). However, it is non-trivial to obtain bounds on the number of directed highways in DIR networks since the conventional undirected percolation theory cannot be directly used in DIR networks. In this paper, we exploit the directed percolation theory to derive bounds on the number of directed highways. In particular, we make new constructions in bond directed percolation model. We show that with high probability there are at least Ω(√n/log log √n) directed highways in a network with n nodes, which is much tighter than the existing results in DIR networks.

We investigate energy-efficient communications for time-division multiple access (TDMA) multiple-input multiple-output (MIMO) cognitive radio (CR) networks operating in underlay mode. In particular, we consider the joint optimization over both the time resource and the transmit precoding matrices to minimize the overall energy consumption of a single cell secondary network with multiple secondary users (SUs), while ensuring their quality of service (QoS). The corresponding mathematical formulations turn out to be non-convex, and thus of high complexity to solve in general. We give a comprehensive treatment of this problem, considering both the cases of perfect channel state information (CSI) and statistical CSI of the channels from the SUs to the primary receiver. We tackle the non-convexity by applying a proper optimization decomposition that allows the overall problem to be efficiently solved. In particular, we show that when the SUs only have statistical CSI, the optimal solution can be found in polynomial time. Moreover, if we consider additional integer constraints on the time variable which is usually a requirement in practical wireless system, the overall problem becomes a mixed-integer non-convex optimization which is more complicated. By exploring the special structure of this particular problem, we show that the optimal integer time solution can be obtained in polynomial time with a simple greedy algorithm. When the SUs have perfect CSI, the decomposition based algorithm is guaranteed to find the optimal solution when the secondary system is under-utilized. Simulation results show that the energy-optimal transmission scheme adapts to the traffic load of the secondary system to create a win-win situation where the SUs are able to decrease the energy consumption and the PUs experience less interference from the secondary system. The effect is particularly pronounced when the secondary system is under-utilized.

In this paper, we study energy-efficient transmissions for multiple-input multiple-output (MIMO) cognitive radio (CR) networks in which the secondary unlicensed users coexist with the primary licensed users. We want to optimize the time allocations and beamforming vectors for the secondary users (SUs), in order to minimize the energy consumption of the SUs while satisfying the SUs' rate requirements and the primary receivers' interference constraints. Compared with the tradition MIMO networks, the challenge here is that the SUs may not always be able to obtain the channel state information (CSI) to the primary receivers. We are interested in two different scenarios. The first is when the SUs have the luxury of knowing the CSI to the primary receivers, and the second is when the SUs do not have such an luxury. The corresponding optimization formulations involve joint time scheduling and beamforming, which are non-convex and are complicated to solve. Fortunately, we show that when the SUs are not able to obtain the CSI, the optimal time allocation and the optimal beamforming vectors can be found very efficiently in polynomial-time through a proper decomposition. When the SUs have perfect knowledge about the CSI, we show that the optimal solutions can still be obtained in polynomial time when the secondary system is under-utilized. If the traffic load to the secondary system is heavy, we propose a polynomial-time heuristic to generate a near-optimal solution. The simulation results show that our proposed energy-optimal-transmission algorithms can achieve an energy-saving of 30% to 91%, compared with the simplistic maximum-rate transmission policy, depending on the secondary system's traffic load.

This paper proposes the concept of safe carrier-sensing range under the cumulative interference model that guarantees interference-safe (also known as hidden-node-free) transmissions in CSMA networks. Compared with a previous related concept of safe carrier-sensing range under the commonly assumed but less realistic pairwise interference model, we show that the safe carrier-sensing range under the cumulative interference model is larger by a constant multiplicative factor. For example, the factor is 1.4 if the SINR requirement is 10 dB and the path-loss exponent is 4 in a noiseless case. We further show that the concept of a safe carrier-sensing range, although amenable to elegant analytical results, is inherently not compatible with the conventional power-threshold carrier-sensing mechanism (e.g., that used in IEEE 802.11). Specifically, the absolute power sensed by a node in the conventional carrier-sensing mechanism does not contain enough information for the node to derive its distances from other concurrent transmitting nodes. We show that, fortunately, a new carrier-sensing mechanism called Incremental-Power Carrier-Sensing (IPCS) can realize the carrier-sensing range concept in a simple way. Instead of monitoring the absolute detected power, the IPCS mechanism monitors every increment in the detected power. This means that IPCS can separate the detected power of every concurrent transmitter, and map the power profile to the required distance information. Our extensive simulation results indicate that IPCS can boost spatial reuse and network throughput by up to 60 percent relative to the conventional carrier-sensing mechanism under the same carrier-sensing power thresholds. If we compare the maximum throughput in the interference-free regime, the throughput improvement of IPCS is still more than 15 percent. Last but not least, IPCS not only allows us to implement the safe carrier-sensing range, but also ties up a loose end in many other prior theoretical works that implicitly used a carrier-sensing range (interference-safe or otherwise) without an explicit design to realize it.

In this paper, we investigate the throughput performance of \rev{physical-layer network coding} (PNC) under the IEEE 802.11 distributed coordination function (DCF). We consider the wireless network that two client groups communicate with each other across one relay node, and focus on the unsaturated network case. The difficulty in modeling the relay systems under the IEEE 802.11 DCF is that the minimum contention window sizes of the client nodes and the relay node may be different, which makes the traditional throughput analysis methods for the non-relay wireless networks inapplicable. Fortunately, we find that the relay system can be decomposed into four parts and respectively modeled. Analytical results show that the throughput gain of PNC scheme is heavily affected by the probability that a transmitted network-coding (NC) packet contains the information of two packets. The implication is that the throughput benefit of PNC is more significant for bidirectional isochronous traffic with rate requirements. \rev{We further derive an approximate closed-form solution of the optimal transmission probability of client nodes that maximizes the PNC network throughput.} We validate our analytical model through extensive simulations and discuss the relationship between the PNC network throughput and other system parameters, such as the minimum contention window sizes of both the client nodes and the relay node.

This paper investigates the problem of link scheduling to meet traffic demands with minimum airtime in a multi-transmit-receive (MTR) wireless network. MTR networks are a new class of networks, in which each node can simultaneously transmit to a number of other nodes, or simultaneously receive from a number of other nodes. The MTR capability can be enabled by the use of multiple directional antennas or multiple channels. Potentially, MTR can boost the network capacity significantly. However, link scheduling that makes full use of the MTR capability must be in place before this can happen. We show that optimal link scheduling can be formulated as a linear program (LP). However, the problem is NP-hard because we need to find all the maximal independent sets in a graph first. We propose two computationally efficient algorithms, called Heavy-Weight-First (HWF) and Max-Degree-First (MDF) to solve this problem. Simulation results show that both HWF and MDF can achieve superior performance in terms of runtime and optimality.

This paper studies the problem of energy conservation of mobile terminals in a multi-cell TDMA network supporting real-time sessions. The corresponding optimization problem involves joint scheduling, rate control, and power control, which is often highly complex to solve. To reduce the solution complexity, we decompose the overall problem into two sub-problems: intra-cell energy optimization and inter-cell interference control. The solution of the two subproblems results in a "win-win" situation: both the energy consumptions and inter-cell interference are reduced simultaneously. We simulate our decomposition method with the typical parameters in WiMAX system, and the simulation results show that our decomposition method can achieve an energy reduction of more than 70% compared with the simplistic maximum transmit power policy. Furthermore, the inter-cell interference power can be reduced by more than 35% compared with the maximum transmit power policy. We find that the interference power stays largely constant throughout a TDMA frame in our decomposition method. Based on this premise, we derive an interesting decoupling property: if the idle power consumption of terminals is no less than their circuit power consumption, or when both are negligible, then the energy-optimal transmission rates of the users are independent of the inter-cell interference power.

This paper proposes and investigates the concept of a safe carrier-sensing range that guarantees interference-safe (also termed hidden-node-free) transmissions in CSMA networks under the cumulative interference model. Compared with the safe carrier-sensing range under the commonly assumed but less realistic pairwise interference model, we show that the safe carrier-sensing range required under the cumulative interference model is larger by a constant multiplicative factor. For example, the factor is 1:4 if the SINR requirement is 10dB and the pathloss exponent is 4. We further show that the concept of a safe carrier-sensing range, although amenable to elegant analytical results, is inherently not compatible with the conventional power-threshold carrier-sensing mechanism (e.g., that used in IEEE 802.11). Specifically, the absolute power sensed by a node in the conventional mechanism does not contain enough information for it to derive its distances from other concurrent transmitter nodes. We show that, fortunately, a carrier-sensing mechanism called Incremental-Power Carrier-Sensing (IPCS) can realize the carrier-sensing range concept in a simple way. Instead of monitoring the absolute detected power, the IPCS mechanism monitors every increment in the detected power. This means that IPCS can separate the detected power of every concurrent transmitter, and map the power profile to the required distance information. Our extensive simulation results indicate that IPCS can boost spatial reuse and network throughput by more than 60% relative to the conventional carrier-sensing mechanism. Last but not least, IPCS not only allows us to implement our safe carrier-sensing range, it also ties up a loose end in many other prior theoretical works that implicitly assume the use of a carrier-sensing range (safe or otherwise) without an explicit design to realize it.

This paper studies the problem of finding a minimum-length schedule of a power-controlled wireless network subject to traffic demands and SINR (signal-to-interference-plus-noise ratio) constraints. We propose a column generation based algorithm that finds the optimal schedules and transmit powers. The column generation method decomposes a complex linear optimization problem into a restricted master problem and a pricing problem. We develop a new formulation of the pricing problem using the Perron-Frobenius eigenvalue condition, which enables us to integrate link scheduling with power control in a single framework. This new formulation reduces the complexity of the pricing problem, and thus improves the overall efficiency of the column generation method significantly - for example, the average runtime is reduced by 99.86% in 18-link networks compared with the traditional column generation method. Furthermore, we propose a branch-and-price method that combines column generation with the branch-and-bound technique to tackle the integer constraints on time slot allocation. We develop a new branching rule in the branch-and-price method that maintains the size of the pricing problem after each branching. Our branch-and-price method can obtain optimal integer solutions efficiently for example, the average runtime is reduced by 99.72% in 18-link networks compared with the traditional branch-and-price method. We further suggest efficient heuristic algorithms based on the structure of the optimal algorithms. Simulation results show that the heuristic algorithms can reach solutions within 10% of optimality for networks with less than 30 links.

We study the joint power control and minimum-frame-length scheduling problem in wireless networks, under the physical interference model and subject to consecutive transmission constraints. We start by investigating the complexity of the problem and present the first NP-completeness proof in the literature. We propose a polynomial-time approximation algorithm, called guaranteed and greedy scheduling (GGS) algorithm, to tackle this problem. We prove a bounded approximation ratio of the proposed algorithm relative to the optimal scheduling algorithm. Moreover, the proposed algorithm significantly outperforms the state-of-the-art related algorithm. Interestingly, our algorithm together with its bounded approximation ratio is applicable even when the consecutive transmission constraint is relaxed. To the best of our knowledge, the proposed algorithm is the first known polynomial-time algorithm with a proven bounded approximation ratio for the joint power control and scheduling problem under the physical interference model. We further demonstrate the performance and advantages of our algorithm through extensive simulations.

Most prior studies on wireless spatial-reuse TDMA (STDMA) link scheduling for throughput optimization deal with the situation where instantaneous channel state information (CSI) is available. Under fast fading, however, the channel may change too quickly for the scheduler to track the instantaneous CSI. In this paper, instead of the instantaneous CSI, the scheduler performs its task according to the stochastic behavior of the channel state. A basic schedule consists of a set of simultaneously transmitting links. The essence of the scheduling problem is to determine a mixed schedule consisting of a weighted sum (TDMA mixture) of a number of basic schedules to optimize a certain utility objective. A key to reducing scheduling complexity is to identify the Pareto-efflcient basic schedules, referred to as the extreme-point schedules, so that the construction of the optimal mixed schedule can be based on the extreme-point schedules rather than all the basic schedules. The precise identification of the extreme-point schedules, however, is intractable computationally. We show in this paper that identifying a slightly larger superset of the extreme-point schedules can be highly efficient using a Perron-Frobenius condition: simulation experiments indicate that oftentimes there are only very few extraneous non-extreme-point schedules in the superset. Building on the effective identification method, we propose a fast scheduling algorithm. This algorithm beats the algorithm without using the identification method by a complexity-reduction factor of 166 in a 15-link network. In addition, numerical results suggest that our algorithm is robust to variations of system parameters.

This paper concerns the problem of finding the minimum-length TDMA frame of a power-controlled wireless network subject to traffic demands and SINR (signal- to-interference-plus-noise ratio) constraints. We formulate the general joint link scheduling and power control problem as an integer linear programming (ILP) problem. The linear relaxation of the ILP problem has been claimed to be NP-hard in the literature. We present a computationally efficient heuristic algorithm, called the increasing demand greedy scheduling (IDGS) algorithm, to solve the general ILP problem. In addition, we propose using a column generation (CG) method as an augmentation to IDGS to further improve its performance. Simulation results show that integration of IDGS and CG can achieve superior performance in terms of both algorithm run time and solution optimality.