Integrated Sensing and Communications with Joint Beam Squint and Beam Split for Massive MIMO

Integrated sensing and communications (ISAC) has attracted tremendous attention for the future 6G wireless communication systems. To improve the transmission rates and sensing accuracy, massive multi-input multi-output (MIMO) technique is leveraged with large transmission bandwidth. However, the growing size of transmission bandwidth and antenna array results in the beam squint effect, which hampers the communications. Moreover, the time overhead of the traditional sensing algorithm is prohibitive for practical systems. In this paper, instead of alleviating the wideband beam squint effect, we take advantage of joint beam squint and beam split effect and propose a novel user directions sensing method integrated with massive MIMO orthogonal frequency division multiplexing (OFDM) systems. Specifically, with the beam squint effect, the BS utilizes the true-time-delay (TTD) lines to steer the beams of different OFDM subcarriers towards different directions simultaneously. The users feedback the subcarrier frequency with the maximum array gain to the BS. Then, the BS calculates the direction based on the subcarrier frequency feedback. Futhermore, the beam split effect introduced by enlarging the inter-antenna spacing is exploited to expand the sensing range. The proposed sensing method operates over frequency-domain, and the intended sensing range is covered by all the subcarriers simultaneously, which reduces the time overhead of the conventional sensing significantly. Simulation results have demonstrated the effectiveness as well as the superior performance of the proposed ISAC scheme.

Multi-Camera View Based Proactive BS Selection and Beam Switching for V2X

Due to the short wavelength and large attenuation of millimeter-wave (mmWave), mmWave BSs are densely distributed and require beamforming with high directivity. When the user moves out of the coverage of the current BS or is severely blocked, the mmWave BS must be switched to ensure the communication quality. In this paper, we proposed a multi-camera view based proactive BS selection and beam switching that can predict the optimal BS of the user in the future frame and switch the corresponding beam pair. Specifically, we extract the features of multi-camera view images and a small part of channel state information (CSI) in historical frames, and dynamically adjust the weight of each modality feature. Then we design a multi-task learning module to guide the network to better understand the main task, thereby enhancing the accuracy and the robustness of BS selection and beam switching. Using the outputs of all tasks, a prior knowledge based fine tuning network is designed to further increase the BS switching accuracy. After the optimal BS is obtained, a beam pair switching network is proposed to directly predict the optimal beam pair of the corresponding BS. Simulation results in an outdoor intersection environment show the superior performance of our proposed solution under several metrics such as predicting accuracy, achievable rate, harmonic mean of precision and recall.

Computer Vision-Aided Reconfigurable Intelligent Surface-Based Beam Tracking: Prototyping and Experimental Results

In this paper, we propose a novel computer vision-based approach to aid Reconfigurable Intelligent Surface (RIS) for dynamic beam tracking and then implement the corresponding prototype verification system. A camera is attached at the RIS to obtain the visual information about the surrounding environment, with which RIS identifies the desired reflected beam direction and then adjusts the reflection coefficients according to the pre-designed codebook. Compared to the conventional approaches that utilize channel estimation or beam sweeping to obtain the reflection coefficients, the proposed one not only saves beam training overhead but also eliminates the requirement for extra feedback links. We build a 20-by-20 RIS running at 5.4 GHz and develop a high-speed control board to ensure the real-time refresh of the reflection coefficients. Meanwhile we implement an independent peer-to-peer communication system to simulate the communication between the base station and the user equipment. The vision-aided RIS prototype system is tested in two mobile scenarios: RIS works in near-field conditions as a passive array antenna of the base station; RIS works in far-field conditions to assist the communication between the base station and the user equipment. The experimental results show that RIS can quickly adjust the reflection coefficients for dynamic beam tracking with the help of visual information.

Joint Precoding for Active Intelligent Transmitting Surface Empowered Outdoor-to-Indoor Communication in mmWave Cellular Networks

Outdoor-to-indoor communications in millimeter-wave (mmWave) cellular networks have been one challenging research problem due to the severe attenuation and the high penetration loss caused by the propagation characteristics of mmWave signals. We propose a viable solution to implement the outdoor-to-indoor mmWave communication system with the aid of an active intelligent transmitting surface (active-ITS), where the active-ITS allows the incoming signal from an outdoor base station (BS) to pass through the surface and be received by the indoor user-equipments (UEs) after shifting its phase and magnifying its amplitude. Then, the problem of joint precoding of the BS and active-ITS is investigated to maximize the weighted sum-rate (WSR) of the communication system. An efficient block coordinate descent (BCD) based algorithm is developed to solve it with the suboptimal solutions in nearly closed-forms. In addition, to reduce the size and hardware cost of an active-ITS, we provide a block-amplifying architecture to partially remove the circuit components for power-amplifying, where multiple transmissive-type elements (TEs) in each block share a same power amplifier. Simulations indicate that active-ITS has the potential of achieving a given performance with much fewer TEs compared to the passive-ITS under the same total system power consumption, which makes it suitable for application to the size-limited and aesthetic-needed scenario, and the inevitable performance degradation caused by the block-amplifying architecture is acceptable.

Beam Squint Assisted User Localization in Near-Field Communications Systems

The beam squint phenomenon in massive multi-input and multi-output wideband communications has been widely concerned recently, which generally deteriorates the beamforming performance. In this paper, we find that with the aid of the time-delay lines (TDs), the range and trajectory of the beam squint of a near-field communications system can be freely controlled, and hence it is possible to reversely utilize the beam squint for user localization. We derive the trajectory equation for near-field beam squint points and design a way to control the trajectory of these beam squint points. With the proposed design, beamforming from different subcarriers would purposely point to different angles and different distances such that users from different positions would receive the maximum power at different subcarriers. Hence, one can simply find the different users' position from the beam squint effect. Simulation results demonstrate the effectiveness of the proposed scheme.

Intelligent Reflecting Surface Networks with Multi-Order-Reflection Effect: System Modelling and Critical Bounds

In this paper, we model, analyze and optimize the multi-user and multi-order-reflection (MUMOR) intelligent reflecting surface (IRS) networks. We first derive a complete MUMOR IRS network model applicable for the arbitrary times of reflections, size and number of IRSs/reflectors. The optimal condition for achieving sum-rate upper bound with one IRS in a closed-form function and the analytical condition to achieve interference-free transmission are derived, respectively. Leveraging this optimal condition, we obtain the MUMOR sum-rate upper bound of the IRS network with different network topologies, where the linear graph (LG), complete graph (CG) and null graph (NG) topologies are considered. Simulation results verify our theories and derivations and demonstrate that the sum-rate upper bounds of different network topologies are under a K-fold improvement given K-piece IRS.

Adversarial Training-Aided Time-Varying Channel Prediction for TDD/FDD Systems

In this paper, a time-varying channel prediction method based on conditional generative adversarial network (CPcGAN) is proposed for time division duplexing/frequency division duplexing (TDD/FDD) systems. CPcGAN utilizes a discriminator to calculate the divergence between the predicted downlink channel state information (CSI) and the real sample distributions under a conditional constraint that is previous uplink CSI. The generator of CPcGAN learns the function relationship between the conditional constraint and the predicted downlink CSI and reduces the divergence between predicted CSI and real CSI. The capability of CPcGAN fitting data distribution can capture the time-varying and multipath characteristics of the channel well. Considering the propagation characteristics of real channel, we further develop a channel prediction error indicator to determine whether the generator reaches the best state. Simulations show that the CPcGAN can obtain higher prediction accuracy and lower system bit error rate than the existing methods under the same user speeds.

Reconfigurable Intelligent Surface for Near Field Communications: Beamforming and Sensing

Reconfigurable intelligent surface (RIS) can improve the communications between a source and a destination. The surface contains metamaterial that is configured to reflect the incident wave from the source towards the destination, especially when there is a blockage in between. Recently, continuous aperture RIS is proved to have better communication performance than discrete aperture RIS and has received much attention. However, the conventional continuous aperture RIS is designed to convert the incoming planar waves into the outgoing planar waves, which is not the optimal reflecting scheme when the receiver is not a planar array and is located in the near field of the RIS. In this paper, we consider two types of receivers in the radiating near field of the RIS: (1) when the receiver is equipped with a uniform linear array (ULA), we design RIS coefficient to convert planar waves into cylindrical waves; (2) when the receiver is equipped with a single antenna, we design RIS coefficient to convert planar waves into spherical waves. Simulation results demonstrate that the proposed scheme can reduce energy leakage at the receiver and thus enhance the channel capacity compared to the conventional scheme. More interestingly, with cylindrical or spherical wave radiation, the power received by the receiver is a function of its location and attitude, which could be utilized to sense the location and the attitude of the receiver with communication signaling.

Cluster Head Detection for Hierarchical UAV Swarm With Graph Self-supervised Learning

In this paper, we study the cluster head detection problem of a two-level unmanned aerial vehicle (UAV) swarm network (USNET) with multiple UAV clusters, where the inherent follow strategy (IFS) of low-level follower UAVs (FUAVs) with respect to high-level cluster head UAVs (HUAVs) is unknown. We first propose a graph attention self-supervised learning algorithm (GASSL) to detect the HUAVs of a single UAV cluster, where the GASSL can fit the IFS at the same time. Then, to detect the HUAVs in the USNET with multiple UAV clusters, we develop a multi-cluster graph attention self-supervised learning algorithm (MC-GASSL) based on the GASSL. The MC-GASSL clusters the USNET with a gated recurrent unit (GRU)-based metric learning scheme and finds the HUAVs in each cluster with GASSL. Numerical results show that the GASSL can detect the HUAVs in single UAV clusters obeying various kinds of IFSs with over 98% average accuracy. The simulation results also show that the clustering purity of the USNET with MC-GASSL exceeds that with traditional clustering algorithms by at least 10% average. Furthermore, the MC-GASSL can efficiently detect all the HUAVs in USNETs with various IFSs and cluster numbers with low detection redundancies.

Data-Driven Deep Learning Based Hybrid Beamforming for Aerial Massive MIMO-OFDM Systems with Implicit CSI

In an aerial hybrid massive multiple-input multiple-output (MIMO) and orthogonal frequency division multiplexing (OFDM) system, how to design a spectral-efficient broadband multi-user hybrid beamforming with a limited pilot and feedback overhead is challenging. To this end, by modeling the key transmission modules as an end-to-end (E2E) neural network, this paper proposes a data-driven deep learning (DL)-based unified hybrid beamforming framework for both the time division duplex (TDD) and frequency division duplex (FDD) systems with implicit channel state information (CSI). For TDD systems, the proposed DL-based approach jointly models the uplink pilot combining and downlink hybrid beamforming modules as an E2E neural network. While for FDD systems, we jointly model the downlink pilot transmission, uplink CSI feedback, and downlink hybrid beamforming modules as an E2E neural network. Different from conventional approaches separately processing different modules, the proposed solution simultaneously optimizes all modules with the sum rate as the optimization object. Therefore, by perceiving the inherent property of air-to-ground massive MIMO-OFDM channel samples, the DL-based E2E neural network can establish the mapping function from the channel to the beamformer, so that the explicit channel reconstruction can be avoided with reduced pilot and feedback overhead. Besides, practical low-resolution phase shifters (PSs) introduce the quantization constraint, leading to the intractable gradient backpropagation when training the neural network. To mitigate the performance loss caused by the phase quantization error, we adopt the transfer learning strategy to further fine-tune the E2E neural network based on a pre-trained network that assumes the ideal infinite-resolution PSs. Numerical results show that our DL-based schemes have considerable advantages over state-of-the-art schemes.