RISnet: A Domain-Knowledge Driven Neural Network Architecture for RIS Optimization with Mutual Coupling and Partial CSI

Multiple access techniques are cornerstones of wireless communications. Their performance depends on the channel properties, which can be improved by reconfigurable intelligent surfaces (RISs). In this work, we jointly optimize MA precoding at the base station (BS) and RIS configuration. We tackle difficulties of mutual coupling between RIS elements, scalability to more than 1000 RIS elements, and channel estimation. We first derive an RIS-assisted channel model considering mutual coupling, then propose an unsupervised machine learning (ML) approach to optimize the RIS. In particular, we design a dedicated neural network (NN) architecture RISnet with good scalability and desired symmetry. Moreover, we combine ML-enabled RIS configuration and analytical precoding at BS since there exist analytical precoding schemes. Furthermore, we propose another variant of RISnet, which requires the channel state information (CSI) of a small portion of RIS elements (in this work, 16 out of 1296 elements) if the channel comprises a few specular propagation paths. More generally, this work is an early contribution to combine ML technique and domain knowledge in communication for NN architecture design. Compared to generic ML, the problem-specific ML can achieve higher performance, lower complexity and symmetry.

Energy-Efficient Power Allocation in Cell-Free Massive MIMO via Graph Neural Networks

CF-mMIMO systems are a promising solution to enhance the performance in 6G wireless networks. Its distributed nature of the architecture makes it highly reliable, provides sufficient coverage and allows higher performance than cellular networks. EE is an important metric that reduces the operating costs and also better for the environment. In this work, we optimize the downlink EE performance with MRT precoding and power allocation. Our aim is to achieve a less complex, distributed and scalable solution. To achieve this, we apply unsupervised ML with permutation equivariant architecture and use a non-convex objective function with multiple local optima. We compare the performance with the centralized and computationally expensive SCA. The results indicate that the proposed approach can outperform the baseline with significantly less computation time.

A Survey of Advances in Optimization Methods for Wireless Communication System Design

Mathematical optimization is now widely regarded as an indispensable modeling and solution tool for the design of wireless communications systems. While optimization has played a significant role in the revolutionary progress in wireless communication and networking technologies from 1G to 5G and onto the future 6G, the innovations in wireless technologies have also substantially transformed the nature of the underlying mathematical optimization problems upon which the system designs are based and have sparked significant innovations in the development of methodologies to understand, to analyze, and to solve those problems. In this paper, we provide a comprehensive survey of recent advances in mathematical optimization theory and algorithms for wireless communication system design. We begin by illustrating common features of mathematical optimization problems arising in wireless communication system design. We discuss various scenarios and use cases and their associated mathematical structures from an optimization perspective. We then provide an overview of recent advances in mathematical optimization theory and algorithms, from nonconvex optimization, global optimization, and integer programming, to distributed optimization and learning-based optimization. The key to successful solution of mathematical optimization problems is in carefully choosing and/or developing suitable optimization algorithms (or neural network architectures) that can exploit the underlying problem structure. We conclude the paper by identifying several open research challenges and outlining future research directions.

AI-based Radio and Computing Resource Allocation and Path Planning in NOMA NTNs: AoI Minimization under CSI Uncertainty

In this paper, we develop a hierarchical aerial computing framework composed of high altitude platform (HAP) and unmanned aerial vehicles (UAVs) to compute the fully offloaded tasks of terrestrial mobile users which are connected through an uplink non-orthogonal multiple access (UL-NOMA). In particular, the problem is formulated to minimize the AoI of all users with elastic tasks, by adjusting UAVs trajectory and resource allocation on both UAVs and HAP, which is restricted by the channel state information (CSI) uncertainty and multiple resource constraints of UAVs and HAP. In order to solve this non-convex optimization problem, two methods of multi-agent deep deterministic policy gradient (MADDPG) and federated reinforcement learning (FRL) are proposed to design the UAVs trajectory and obtain channel, power, and CPU allocations. It is shown that task scheduling significantly reduces the average AoI. This improvement is more pronounced for larger task sizes. On the one hand, it is shown that power allocation has a marginal effect on the average AoI compared to using full transmission power for all users. On the other hand, compared with traditional transmissions (fixed method) simulation result shows that our scheduling scheme has a lower average AoI.

Non-Orthogonal Multiple Access Assisted by Reconfigurable Intelligent Surface Using Unsupervised Machine Learning

Nonorthogonal multiple access (NOMA) with multi-antenna base station (BS) is a promising technology for next-generation wireless communication, which has high potential in performance and user fairness. Since the performance of NOMA depends on the channel conditions, we can combine NOMA and reconfigurable intelligent surface (RIS), which is a large and passive antenna array and can optimize the wireless channel. However, the high dimensionality makes the RIS optimization a complicated problem. In this work, we propose a machine learning approach to solve the problem of joint optimization of precoding and RIS configuration. We apply the RIS to realize the quasi-degradation of the channel, which allows for optimal precoding in closed form. The neural network architecture RISnet is used, which is designed dedicatedly for RIS optimization. The proposed solution is superior than the works in the literature in terms of performance and computation time.

RISnet: A Scalable Approach for Reconfigurable Intelligent Surface Optimization with Partial CSI

The reconfigurable intelligent surface (RIS) is a promising technology that enables wireless communication systems to achieve improved performance by intelligently manipulating wireless channels. In this paper, we consider the sum-rate maximization problem in a downlink multi-user multi-input-single-output (MISO) channel via space-division multiple access (SDMA). Two major challenges of this problem are the high dimensionality due to the large number of RIS elements and the difficulty to obtain the full channel state information (CSI), which is assumed known in many algorithms proposed in the literature. Instead, we propose a hybrid machine learning approach using the weighted minimum mean squared error (WMMSE) precoder at the base station (BS) and a dedicated neural network (NN) architecture, RISnet, for RIS configuration. The RISnet has a good scalability to optimize 1296 RIS elements and requires partial CSI of only 16 RIS elements as input. We show it achieves a high performance with low requirement for channel estimation for geometric channel models obtained with ray-tracing simulation. The unsupervised learning lets the RISnet find an optimized RIS configuration by itself. Numerical results show that a trained model configures the RIS with low computational effort, considerably outperforms the baselines, and can work with discrete phase shifts.

Energy Efficient Operation of Adaptive Massive MIMO 5G HetNets

For energy efficient operation of the massive multiple-input multiple-output (MIMO) networks, various aspects of energy efficiency maximization have been addressed, where a careful selection of number of active antennas has shown significant gains. Moreover, switching-off physical resource blocks (PRBs) and carrier shutdown saves energy in low load scenarios. However, the joint optimization of spectral PRB allocation and spatial layering in a heterogeneous network has not been completely solved yet. Therefore, we study a power consumption model for multi-cell multi-user massive MIMO 5G network, capturing the joint effects of both dimensions. We characterize the optimal resource allocation under practical constraints, i.e., limited number of available antennas, PRBs, base stations (BSs), and frequency bands. We observe a single spatial layer achieving lowest energy consumption in very low load scenarios, whereas, spatial layering is required in high load scenarios. Finally, we derive novel algorithms for energy efficient user to BS assignment and propose an adaptive algorithm for PRB assignment and power control. All results are illustrated by numerical system-level simulations, describing a realistic metropolis scenario. The results show that a higher frequency band should be used to support users with large rate requirements via spatial multiplexing and assigning each user maximum available PRBs.

Smart Resource Allocation Model via Artificial Intelligence in Software Defined 6G Networks

In this paper, we design a new flexible smart software-defined radio access network (Soft-RAN) architecture with traffic awareness for sixth generation (6G) wireless networks. In particular, we consider a hierarchical resource allocation model for the proposed smart soft-RAN model where the software-defined network (SDN) controller is the first and foremost layer of the framework. This unit dynamically monitors the network to select a network operation type on the basis of distributed or centralized resource allocation procedures to intelligently perform decision-making. In this paper, our aim is to make the network more scalable and more flexible in terms of conflicting performance indicators such as achievable data rate, overhead, and complexity indicators. To this end, we introduce a new metric, i.e., throughput-overhead-complexity (TOC), for the proposed machine learning-based algorithm, which supports a trade-off between these performance indicators. In particular, the decision making based on TOC is solved via deep reinforcement learning (DRL) which determines an appropriate resource allocation policy. Furthermore, for the selected algorithm, we employ the soft actor-critic (SAC) method which is more accurate, scalable, and robust than other learning methods. Simulation results demonstrate that the proposed smart network achieves better performance in terms of TOC compared to fixed centralized or distributed resource management schemes that lack dynamism. Moreover, our proposed algorithm outperforms conventional learning methods employed in recent state-of-the-art network designs.

RISNet: a Dedicated Scalable Neural Network Architecture for Optimization of Reconfigurable Intelligent Surfaces

The reconfigurable intelligent surface (RIS) is a promising technology for next-generation wireless communication. It comprises many passive antennas, which reflect signals from the transmitter to the receiver with adjusted phases without changing the amplitude. The large number of the antennas enables a huge potential of signal processing despite the simple functionality of a single antenna. However, it also makes the RIS configuration a high dimensional problem, which might not have a closed-form solution and has a high complexity and, as a result, severe difficulty in online real-time application if we apply iterative numerical solutions. In this paper, we introduce a machine learning approach to maximize the weighted sum-rate (WSR). We propose a dedicated neural network architecture called RISNet. The RIS optimization is designed according to the RIS property of product and direct channel and homogeneous RIS antennas. The architecture is scalable due to the fact that the number of trainable parameters is independent from the number of RIS antennas (because all antennas share the same parameters). The weighted minimum mean squared error (WMMSE) precoding is applied and an alternating optimization (AO) training procedure is designed. Testing results show that the proposed approach outperforms the state-of-the-art block coordinate descent (BCD) algorithm. Moreover, although the training takes several hours, online testing with trained model (application) is almost instant, which makes it feasible for real-time application. Compared to it, the BCD algorithm requires much more convergence time. Therefore, the proposed method outperforms the state-of-the-art algorithm in both performance and complexity.