Millimeter‐wave (mmWave) and massive multiple‐input multiple‐output (MIMO) technologies are critical in current and future communication research. They play an essential role in meeting the demands for high‐capacity, high‐speed, and low‐latency communication brought about by technological advancements. However, existing mmWave channel estimation schemes rely on idealized common sparse channel support assumptions, and their performance significantly degrades when encountering beam squint scenarios. To address this issue, this paper introduces a dynamic support detection window (DSDW) algorithm. This algorithm dynamically adjusts the position and size of the window based on the received signal strength, thereby better capturing signal strength variations and obtaining a more complete set of signal supports. The DSDW algorithm can better capture and utilize the sparsity of the channel, improving the efficiency and accuracy of the channel state information acquisition. By combining the beam‐split pattern (BSP) algorithm with the DSDW algorithm, this paper designs an effective method to address the inherent beam‐spreading problem in mmWave scenarios. Simulation results are proposed to demonstrate the effectiveness of the BSP‐DSDW algorithm.

The Internet of Things (IoT) allows physical devices to be connected over the wireless networks. Although device-to-device (D2D) communication has emerged as a promising technology for IoT, the conventional solutions for D2D resource allocation are usually computationally complex and time consuming. The high complexity poses a significant challenge to the practical implementation of wireless IoT networks. A graph neural network (GNN)-based framework is proposed to address this challenge in a supervised manner. Specifically, the wireless network is modeled as a directed graph, where the desirable communication links are modeled as nodes and the harmful interference links are modeled as edges. The effectiveness of the proposed framework is verified via two case studies, namely the link scheduling in D2D networks and the joint channel and power allocation in D2D underlaid cellular networks. Simulation results demonstrate that the proposed framework outperforms the benchmark schemes in terms of the average sum rate and the sample efficiency. In addition, the proposed GNN approach shows potential generalizability to different system settings and robustness to the corrupted input features. It also accelerates the D2D resource optimization by reducing the execution time to only a few milliseconds.

Simultaneous wireless information and power transfer (SWIPT) has long been proposed as a key solution for charging and communicating with low-cost and low-power devices. However, the employment of radio frequency (RF) signals for information/power transfer needs to comply with international health and safety regulations. In this article, we provide a complete framework for the design and analysis of far-field SWIPT under safety constraints. In particular, we deal with two RF exposure regulations, namely, the specific absorption rate (SAR) and the maximum permissible exposure (MPE). The state of the art regarding SAR and MPE is outlined together with a description as to how these can be modeled in the context of communication networks. We propose a deep learning approach for the design of robust beamforming subject to specific information, energy harvesting, and SAR constraints. Furthermore, we present a thorough analytical study for the performance of large-scale SWIPT systems, in terms of information and energy coverage under MPE constraints. This work provides insights with regards to the optimal SWIPT design and the potentials from the proper development of SWIPT systems under health and safety restrictions.

Accurate downlink channel information is crucial to the beamforming design, but it is difficult to obtain in practice. This paper investigates a deep learning-based optimization approach of the downlink beamforming to maximize the system sum rate, when only the uplink channel information is available. Our main contribution is to propose a model-driven learning technique that exploits the structure of the optimal downlink beamforming to design an effective hybrid learning strategy with the aim to maximize the sum rate performance. This is achieved by jointly considering the learning performance of the downlink channel, the power and the sum rate in the training stage. The proposed approach applies to generic cases in which the uplink channel information is available, but its relation to the downlink channel is unknown and does not require an explicit downlink channel estimation. We further extend the developed technique to massive multiple-input multiple-output scenarios and achieve a distributed learning strategy for multicell systems without an inter-cell signalling overhead. Simulation results verify that our proposed method provides the performance close to the state of the art numerical algorithms with perfect downlink channel information and significantly outperforms existing data-driven methods in terms of the sum rate.

This paper studies the long-standing problem of outage-constrained robust downlink beamforming in multi-user multi-antenna wireless communications systems. State of the art solutions have very high computational complexity which poses a major challenge to meet the latency requirement in the future communications systems, e.g., the targeted 1 ms end-to-end latency in 5G. By transforming the robust beamforming problem into a deep learning problem, we propose a new unsupervised data augmentation based deep neural network (DNN) method to address the outage-constrained robust beamforming problem with uncertain channel state information at the transmitter. Simulation results demonstrate that our proposed data augmentation based DNN method for the robust beamforming problem is capable to satisfy the required outage probability, and more importantly, compared to the benchmark Bernstein-Type Inequality (BTI) method, it is less conservative, more power efficient and several orders of magnitude faster.

In the Fog Radio Access Networks (F-RANs), the local storage and computing capability of Fog Access Points (FAPs) provide new communication resources to address the latency and computing constraints for delay-sensitive applications. To achieve the ultra-low latency, a novel joint user association and channel allocation scheme is proposed in this paper, where the FAPs are clustered from a user-centric perspective. The delay performance is improved regarding both the control signaling procedure and the data transmission procedure. Specifically, the multiple access interference (MAI) between users is analyzed, where the closed-form expression for the effective rate of a typical user with multiple FAP connections and arbitrary interfering users is obtained. With the consideration of MAI, the proposed distributed joint user association and channel allocation algorithm provides a guaranteed delay violation probability. Moreover, the distributed algorithm can be conducted on individual FAPs, whose calculation is simplified by look-up tables. Simulation results show that the proposed algorithm is capable of providing statistical delay performance guarantee including both average delay and delay bound violation probability, which demonstrates its superiority in supporting delay-sensitive applications in F-RANs.

This paper studies fast downlink beamforming algorithms using deep learning in multiuser multiple-input-single-output systems where each transmit antenna at the base station has its own power constraint. We focus on the signal-to-interference-plus-noise ratio (SINR) balancing problem which is quasi-convex but there is no efficient solution available. We first design a fast subgradient algorithm that can achieve near-optimal solution with reduced complexity. We then propose a deep neural network structure to learn the optimal beamforming based on convolutional networks and exploitation of the duality of the original problem. Two strategies of learning various dual variables are investigated with different accuracies, and the corresponding recovery of the original solution is facilitated by the subgradient algorithm. We also develop a generalization method of the proposed algorithms so that they can adapt to the varying number of users and antennas without re-training. We carry out intensive numerical simulations and testbed experiments to evaluate the performance of the proposed algorithms. Results show that the proposed algorithms achieve close to optimal solution in simulations with perfect channel information and outperform the alleged theoretically optimal solution in experiments, illustrating a better performance-complexity tradeoff than existing schemes.

Voltage stability is a long standing issue in power systems. Due to the requirements of on-line monitoring and high computation efficiency, L-index is used as voltage stability metric in this paper. We propose a novel L-index sensitivity based control algorithm for voltage stability enhancement. The proposed method uses both outputs of wind generators and additional reactive power compensators as control variables. The sensitivities between L-index and control variables are introduced. Based on these sensitivities, the control algorithm can minimise all the control efforts, while satisfying the predetermined L-index value. This paper then verifies the proposed voltage stability enhancement method using real load and wind generation data in the IEEE 14 bus system. The simulation results prove the effectiveness of proposed methodology in enhancement of voltage stability.

This paper proposes a unified framework for the effective rate analysis over arbitrary correlated and not necessarily identical multiple-input single-output (MISO) fading channels, which uses the moment generating function (MGF) based approach and H transform representation. The proposed framework has the potential to simplify the cumbersome analysis procedure compared with the probability density function-based approach. Moreover, the effective rates over two specific fading scenarios are investigated, namely, independent but not necessarily identical distributed (i.n.i.d.) MISO hyper Fox's H fading channels and arbitrary correlated generalized K fading channels. The exact analytical representations for these two scenarios are also presented. By substituting corresponding parameters, the effective rates in various practical fading scenarios, such as Rayleigh, Nakagami-m, Weibull/Gamma, and generalized K fading channels, are readily available. In addition, asymptotic approximations are provided for the proposed H transform and MGF-based approach as well as for the effective rate over i.n.i.d. MISO hyper Fox's H fading channels. Simulations under various fading scenarios are also presented, which support the validity of the proposed method.

In this study, a novel method for target identification in a foliage environment is presented. This method is based on the ultra wideband (UWB) wireless sensor networks (WSNs) model, and the foliage environment is specially considered. The data used to identify the targets are derived from the received signal waveform, so most existing transceivers can be exploited as detecting sensors, which leads to a potential low‐cost way to identify targets during the normal communications within the WSNs under foliage environment. The selected bispectra algorithm is applied to extract the feature vector, and chaos particle swarm optimisation‐based support vector machine is used as the target classifier. Experiments with real‐world data samples indicate that this method has an excellent classification performance in a foliage environment. Moreover, this method shows potential for online training.