Joint Downlink and Uplink Optimization for RIS-Aided FDD MIMO Communication Systems

This paper investigates reconfigurable intelligent surface (RIS)-aided frequency division duplexing (FDD) communication systems. Since the downlink and uplink signals are simultaneously transmitted in FDD, the phase shifts at the RIS should be designed to support both transmissions. Considering a single-user multiple-input multiple-output system, we formulate a weighted sum-rate maximization problem to jointly maximize the downlink and uplink system performance. To tackle the non-convex optimization problem, we adopt an alternating optimization (AO) algorithm, in which two phase shift optimization techniques are developed to handle the unit-modulus constraints induced by the reflection coefficients at the RIS. The first technique exploits the manifold optimization-based algorithm, while the second uses a lower-complexity AO approach. Numerical results verify that the proposed techniques rapidly converge to local optima and significantly improve the overall system performance compared to existing benchmark schemes.

Learning to Team-Based Navigation: A Review of Deep Reinforcement Learning Techniques for Multi-Agent Pathfinding

Multi-agent pathfinding (MAPF) is a critical field in many large-scale robotic applications, often being the fundamental step in multi-agent systems. The increasing complexity of MAPF in complex and crowded environments, however, critically diminishes the effectiveness of existing solutions. In contrast to other studies that have either presented a general overview of the recent advancements in MAPF or extensively reviewed Deep Reinforcement Learning (DRL) within multi-agent system settings independently, our work presented in this review paper focuses on highlighting the integration of DRL-based approaches in MAPF. Moreover, we aim to bridge the current gap in evaluating MAPF solutions by addressing the lack of unified evaluation metrics and providing comprehensive clarification on these metrics. Finally, our paper discusses the potential of model-based DRL as a promising future direction and provides its required foundational understanding to address current challenges in MAPF. Our objective is to assist readers in gaining insight into the current research direction, providing unified metrics for comparing different MAPF algorithms and expanding their knowledge of model-based DRL to address the existing challenges in MAPF.

WiThRay: A Versatile Ray-Tracing Simulator for Smart Wireless Environments

This paper presents the development and evaluation of WiThRay, a new wireless three-dimensional ray-tracing (RT) simulator. RT-based simulators are widely used for generating realistic channel data by combining RT methodology to get signal trajectories and electromagnetic (EM) equations, resulting in generalized channel impulse responses (CIRs). This paper first provides a comprehensive comparison on methodologies of existing RT-based simulators. We then introduce WiThRay, which can evaluate the performance of various wireless communication techniques such as channel estimation/tracking, beamforming, and localization in realistic EM wave propagation. WiThRay implements its own RT methodology, the bypassing on edge (BE) algorithm, that follows the Fermat's principle and has low computational complexity. The scattering ray calibration in WiThRay also provides a precise solution in the analysis of EM propagation. Different from most of the previous RT-based simulators, WiThRay incorporates reconfigurable intelligent surfaces (RIS), which will be a key component of future wireless communications. We thoroughly show that the channel data from WiThRay match sufficiently well with the fundamental theory of wireless channels. The virtue of WiThRay lies in its feature of not making any assumption about the channel, like being slow/fast fading or frequency selective. A realistic wireless environment, which can be conveniently simulated via WiThRay, naturally defines the physical properties of the wireless channels. WiThRay is open to the public, and anyone can exploit this versatile simulator to develop and test their communications and signal processing techniques.

Channel Estimation for Reconfigurable Intelligent Surface with a few Active Elements

In this paper, a channel estimation technique for reconfigurable intelligent surface (RIS)-aided multi-user multiple-input single-output communication systems is proposed. By deploying a small number of active elements at the RIS, the RIS can receive and process the training signals. Through the partial channel state information (CSI) obtained from the active elements, the overall training overhead to estimate the entire channel can be dramatically reduced. To minimize the estimation complexity, the proposed technique is based on the linear combination of partial CSI, which only requires linear matrix operations. By exploiting the spatial correlation among the RIS elements, proper weights for the linear combination and normalization factors are developed. Numerical results show that the proposed technique outperforms other schemes using the active elements at the RIS in terms of the normalized mean squared error when the number of active elements is small, which is necessary to maintain the low cost and power consumption of RIS.

Bayesian Channel Estimation for Intelligent Reflecting Surface-Aided mmWave Massive MIMO Systems With Semi-Passive Elements

In this paper, we propose a Bayesian channel estimator for intelligent reflecting surface-aided (IRS-aided) millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems with semi-passive elements that can receive the signal in the active sensing mode. Ultimately, our goal is to minimize the channel estimation error using the received signal at the base station and additional information acquired from a small number of active sensors at the IRS. Unlike recent works on channel estimation with semi-passive elements that require both uplink and downlink training signals to estimate the UE-IRS and IRS-BS links, we only use uplink training signals to estimate all the links. To compute the minimum mean squared error (MMSE) estimates of all the links, we propose a novel variational inference-sparse Bayesian learning (VI-SBL) channel estimator that performs approximate posterior inference on the channel using VI with the mean-field approximation under the SBL framework. The simulation results show that VI-SBL outperforms the state-of-the-art baselines for IRS with passive reflecting elements in terms of the channel estimation accuracy, training overhead, and spectral efficiency. Furthermore, VI-SBL with semi-passive elements is shown to be more energy-efficient than the baselines with passive reflecting elements while employing a small number of low-cost active sensors.