In distributed random access (RA), each device should consider its own information as well as the influence from others, which is difficult to obtain with diverse capabilities of devices. In this paper, we study the RA problem for massive devices with heterogeneous device capabilities in large-scale energy harvesting IoT networks. To deal with the overload issue for massive devices with different capabilities, we propose an optimal RA policy by improving the conventional mean field games (MFG) via exchanging the mean field terms (MFT) among devices. Specifically, we formulate the delay optimal problem as a two-dimensional Markov Decision Process (MDP) problem involving both energy and data states. For distributed deployment of massive RA, we divide the MDP into multiple per-device subproblems, and propose the distributed RA scheme by solving the Hamilton-Jacobi-Bellman (HJB) equation using stochastic learning. Considering the deviation of MFT estimation induced by heterogeneous device capabilities, we design an MFT consensus scheme based on stochastic approximation by information exchange among neighbor devices. For reducing the state space and exchanging the MFT efficiently, we adopt the number of simultaneous access devices instead of the conventional MFT. Furthermore, we prove the convergence of the proposed scheme with coupling MFT and Q-factor. Finally, simulation results demonstrate that the proposed RA scheme outperforms baseline schemes on delay performance, especially in the heavy traffic load regime.

Delay Optimal Random Access With Heterogeneous Device Capabilities in Energy Harvesting Networks Using Mean Field Game

In reconfigurable intelligent surface (RIS) aided communications, how to optimize a large number of RIS reflecting elements in a scalable and efficient manner remains an open challenge. This paper addresses the scalability issue by partitioning RIS into sub-surfaces, so as to optimize the phase shifts in sub-surface levels to reduce complexity. In the proposed design, each sub-surface employs a linear phase variation structure to anomalously reflect the incident signal to a desired direction. We formulate the achievable rate maximization problem by jointly optimizing the transmit covariance matrix and the structured RIS phase shifts. Then, we characterize the asymptotic behavior of the system with an infinitely large number of transceiver antennas and reflecting elements. We develop an efficient algorithm to find the optimal solution to the asymptotic problem via one-dimensional (1D) search. By applying the asymptotic result to a finite-size system with necessary modifications, we show by numerical results that the proposed design achieves a favorable tradeoff between system performance and computational complexity.

pdf/ris-partitioning-based-scalable-beamforming-design-for-large-2azjempm.pdf

RIS Partitioning Based Scalable Beamforming Design for Large-Scale MIMO

Carrier Sense Multiple Access with Collision Avoid (CSMA/CA) restricts the channel utilization efficiency although it always is regarded as a promising distributed channel access scheme, especially in dynamic dense wireless network scenarios. This paper investigates a Reinforcement Learning (RL) based multi-parameter joint optimization mechanism to build suitable access configuration for dense wireless multi-hop networks. Specifically, we propose a light-weight CSMA/CA mechanism based on Q-Learning (QL), namely QL-CSMA/CA, which jointly optimizes multiple channel access parameters, including Contention Window (CW), Maximum Backoff Exponent (MaxBE), and Maximum CSMA Backoffs (MaxCSMABackoffs), by setting mixed rewards and thus leading to higher network performance. Due to the limitation of the QL-CSMA/CA in large-scale state-space scenarios, we further present an enhanced CSMA/CA mechanism based on Deep Q-Network (DQN), namely DQN-CSMA/CA. Despite introducing a little complexity, the DQN-CSMA/CA can significantly improve transmission performance by utilizing more comprehensive network state information in complex dynamic network scenarios. Simulation results demonstrate the effectiveness of the proposed algorithms in terms of throughput, packet loss rate, delay, and collision rate, especially in high-density scenarios. Specifically, compared to SCAMA and traditional CSMA/CA algorithms, the QL-CSMA/CA algorithm achieves 9% and 5% performance improvements in terms of throughput, while DQN-CSMA/CA algorithm achieves 14% and 10% improvements in terms of throughput, respectively. 

Reinforcement learning based multi-parameter joint optimization in dense multi-hop wireless networks

To reduce the overheads of channel estimation and beamforming caused by a large number of intelligent reflecting surface (IRS) elements, a strategy of grouping the IRS elements, in which the same reflection coefficient is shared by all IRS elements within a group, has been used. However, this approach results in very low beam gains at specific directional angles of a channel. To address this issue, we propose a novel scheme that employs a variation pattern of the reflection coefficients within a group. The patterns are designed to differ among the groups, while being fixed regardless of a dynamic channel. We present several sets of patterns that increase the minimized beam gain over the directional angles of a channel and enhance the worst-case performance metrics such as the outage capacity and coverage probability. Some of the pattern sets are generated via heuristic methods, while others are crafted using a systematic iterative sub-optimization technique. Numerical examples demonstrate that the proposed scheme outperforms the conventional method in terms of the outage capacity.

IRS Element Grouping with Element-Wise Reflection Coefficients

Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) have been receiving great attention nowadays due to the capability of achieving full-space coverage. In this paper, the queue-aware STAR-RIS assisted non-orthogonal multiple access (NOMA) communication system is investigated to ensure the system stability. To tackle the challenge of infinite time periods required for stability, the long-term stability-oriented problem is reformulated as a queue-weighted sum rate (QWSR) maximization problem in each single time slot based on the Lyapunov drift theory. In particular, the rate weight allocated to each user is determined by the length of a data queue, which is maintained at the base station (BS) and pending delivery to each user. Then, the QWSR is maximized by jointly optimizing the NOMA decoding order, the active beamforming coefficients (ABCs) at the BS, and the passive transmission and reflection coefficients (PTRCs) at the STAR-RIS, where three STAR-RIS operating protocols are considered, namely energy splitting (ES), mode switching (MS), and time switching (TS). For ES, to handle the highly-coupled and non-convex problem, the blocked coordinate descent and the successive convex approximation methods are invoked to iteratively and alternatively optimize the problem. Moreover, the proposed iterative algorithm is further extended to a penalty-based two-loop algorithm to solve the binary amplitude constrained problem for MS. For TS, the formulated problem is decomposed into two subproblems, each of which can be solved in a similar manner as introduced for ES. Simulation results show that: i) our proposed STAR-RIS assisted NOMA communication achieves better performance compared with the conventional schemes; ii) the reformulated QWSR maximization problem is proven to ensure the system stability; and iii) the TS protocol achieves superior performance with respect to both the QWSR and the average queue length. N. Zhang and W. Wang are with College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China (e-mail: zhangnannan@zju.edu.cn; wangw@zju.edu.cn). Y. Liu is with the School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, U.K. (e-mail: yuanwei.liu@qmul.ac.uk). X. Mu is with the School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China (e-mail: muxidong@bupt.edu.cn).

pdf/queue-aware-star-ris-assisted-noma-communication-systems-yfwz7c1n.pdf

Queue-Aware STAR-RIS Assisted NOMA Communication Systems

Intelligent reflecting surface (IRS) is a promising solution for enabling the control of wireless environments. Most of existing works consider that an IRS consists of a large number of reconfigurable passive elements and each of them can reflect the signal independently. However, it is difficult and costly in practice to manufacture this kind of large-scale IRS. In this paper, we propose a practical electromagnetic-compliant reflection coefficient model for element-grouping IRS, which configures each sub-surface (including multiple homogeneous elements) but not each single element. Under this model, the reflection amplitude and the phase shift are coupled, which brings new challenges for optimizing the IRS configuration. We study a phase shift optimization problem to maximize the received power in an IRS-aided wireless system. As this problem is difficult to solve due to the complicated coupling in the practical model, we transform it into a vector composition problem equivalently. By constructing a geometry-based phase analysis model, we analyze the relationship of the vectors in a two-dimensional space to maximize the length of the sum vector. By exploiting the geometrical properties, we propose a low-complexity phase control algorithm to find the optimal phase shifts of IRS. Simulation results reveal that substantial performance gains are achieved by the proposed algorithm compared to the conventional schemes.

Element-Grouping Intelligent Reflecting Surface: Electromagnetic-Compliant Model and Geometry-Based Optimization

Queue based carrier-sense multiple access (Q-CSMA) is designed to ensure throughput optimality in wireless networks, where the access probability depends on the local queue length information. However, some links may be long-term blocked if their neighbors continuously occupy the channel, which is called starvation and leads to poor delay performance. In this letter, to deal with the starvation problem, we construct a neighbor-aware virtual queue system, which considers not only the local queue lengths but also the neighbors’ queue lengths. In the virtual queue system, we design a new Lyapunov function and derive a virtual queue based CSMA (VQ-CSMA) algorithm. With the presence of negative virtual queues, we discuss four cases of virtual queues and prove that VQ-CSMA achieves throughput optimality by analyzing the corresponding maximum-weight scheduling. The simulation results demonstrate that VQ-CSMA alleviates the starvation problem and consequently leads to a better delay performance compared to existing Q-CSMA.

VQ-CSMA: Throughput-Optimal Low-Delay Random Access

When only a part of measurements are collected sequentially in large-scale Internet of Things (IoT) networks, it is motivated to extract significant information from the collected data for utilizing the communication resources over incoming slots more efficiently. In this article, we study the successive measurement selection problem for sparse parameter estimation and its distributed implementation in large-scale networks. Specifically, we transform the intractable multi-slot measurement selection problem into per-slot subproblems, and in each slot we select the uncollected measurements that are more “useful” for parameter estimation based on sparse Bayesian learning. In order to deal with the combinatorial problem with distributed implementation, we relax the subproblem and propose an enhanced subgradient method to solve the Boolean constraint. The proposed distributed scheme needs only a common information broadcast by the centralized controller, where the communication overheads only depend on the number of sparse active sources rather than the number of sensors which is always huge. Simulation results demonstrate the performance gain of our proposed measurement selection method compared to the conventional schemes. When the total communication overheads are limited with the network scale, our scheme presents extreme practicability and applicability for the large-scale networks.

Multi-Slot Distributed Measurement Selection: A Sparsity Learning Approach

Considering the limitations on communication capability in the big data era, measurement selection plays an important role in obtaining the desired information by collecting only a part of data from the sensors. In this paper, we study the large-scale measurement selection problem, and propose a distributed algorithm exploiting the sparsity property extracted from the on-line data processing. Different to the existing works, we propose a mission-oriented framework to analyze the performance improvements for the specific mission of collecting new data. Specifically, a Bayesian hierarchical prior is adopted in order to quantify the importance of uncollected data by the on-line inference from the collected data. Based on the sparsity property obtained by on-line data processing, the sensors with important uncollected data will have high priority to access. Due to the massive number of sensors, the measurement selection algorithm is executed distributively at each device according to the common information broadcast by the fusion center. Simulation results demonstrate the performance gain of our proposed measurement selection method compared to the conventional schemes.

Distributed Successive Measurement Selection Based on Online Sparsity Inference

Intelligent reflecting surface (IRS) usually consists of a large number of passive elements, for which the element-grouping strategies can be adopted to group adjacent elements into a sub-surface for lower computational complexity. For the grouped elements of a sub-surface, the linear gradient phase shift configuration can achieve directional IRS reflect beam towards the intended receiver. In this paper, we propose a practical scalable optimization framework for element-grouping IRS by adopting the amplitude-dependent phase-gradient directional beamforming, which induces a new amplitude-phase coupling to the reflected signal. Specifically, by deriving the phase-gradient condition from Fermat’s principle, we propose a practical phase-gradient IRS reflection model. Under this practical model, the amplitude-phase coupling becomes complicated, which brings technical challenges to the IRS beamforming optimization. We study a joint transmit and reflect beamforming optimization problem to minimize the transmit power. By designing a trigonometric transformation to deal with the complicated amplitude-phase coupling, we propose a penalty-based phase control strategy under given element grouping. Subsequently, to solve the element-grouping combinatorial problem with performance guarantee, we propose a low-complexity IRS reflect beamforming algorithm based on Markov approximation. Simulation results demonstrate that the proposed algorithm achieves substantial performance gains compared to conventional schemes.

Amplitude-Dependent Phase-Gradient Directional Beamforming for IRS: A Scalable Optimization Framework

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