To leverage massive distributed data and computation resources, machine learning in the network edge is considered to be a promising technique especially for large-scale model training. Federated learning (FL), as a paradigm of collaborative learning techniques, has obtained increasing research attention with the benefits of communication efficiency and improved data privacy. Due to the lossy communication channels and limited communication resources (e.g., bandwidth and power), it is of interest to investigate fast responding and accurate FL schemes over wireless systems. Hence, we investigate the problem of jointly optimized communication efficiency and resources for FL over wireless Internet of things (IoT) networks. To reduce complexity, we divide the overall optimization problem into two sub-problems, i.e., the client scheduling problem and the resource allocation problem. To reduce the communication costs for FL in wireless IoT networks, a new client scheduling policy is proposed by reusing stale local model parameters. To maximize successful information exchange over networks, a Lagrange multiplier method is first leveraged by decoupling variables including power variables, bandwidth variables and transmission indicators. Then a linear-search based power and bandwidth allocation method is developed. Given appropriate hyper-parameters, we show that the proposed communication-efficient federated learning (CEFL) framework converges at a strong linear rate. Through extensive experiments, it is revealed that the proposed CEFL framework substantially boosts both the communication efficiency and learning performance of both training loss and test accuracy for FL over wireless IoT networks compared to a basic FL approach with uniform resource allocation.

Cell-free networks are considered to be a promising distributed network architecture to satisfy the increasing number of users and high rate expectations in beyond-5G systems. However, to further enhance network capacity, an increasing number of high-cost base stations (BSs) is required. To address this problem and inspired by the cost-effective intelligent reflecting surface (IRS) technique, we propose a fully decentralized design framework for cooperative beamforming in IRS-aided cell-free networks. We first transform the centralized weighted sum-rate maximization problem into a tractable consensus optimization problem, and then an incremental alternating direction method of multipliers (ADMM) algorithm is proposed to locally update the beamformer. The complexity and convergence of the proposed method are analyzed, and these results show that the performance of the new scheme can asymptotically approach that of the centralized one as the number of iterations increases. Results also show that IRSs can significantly increase the system sum-rate of cell-free networks and the proposed method outperforms existing decentralized methods.

To handle the data explosion in the era of Internet-of-things, it is of interest to investigate the decentralized network, with the aim at relaxing the burden at the central server along with preserving data privacy. In this work, we develop a fully decentralized federated learning (FL) framework with an inexact stochastic parallel random walk alternating direction method of multipliers (ISPW-ADMM). Performing more efficient communication and enhanced privacy preservation compared with the current state-of-the-art, the proposed ISPW-ADMM can be partially immune to the effect of time-varying dynamic network and stochastic data collection, while still in fast convergence. Benefiting from the stochastic gradients and biased first-order moment estimation, the proposed framework can be applied to any decentralized FL tasks over time-varying graphs. Thus, to demonstrate the practicability of such a framework in providing fast convergence, high communication efficiency, noise robustness for a specific on-board mission to some extent, we study the extreme learning machine-based FL model beamforming design in unmanned aerial vehicle communications, as verified by the numerical simulations.

Millimeter Wave (mmWave) communications with full-duplex (FD) have the potential of increasing the spectral efficiency, relative to those with half-duplex. However, the residual self-interference (SI) from FD and high pathloss inherent to mmWave signals may degrade the system performance. Meanwhile, hybrid beamforming (HBF) is an efficient technology to enhance the channel gain and mitigate interference with reasonable complexity. However, conventional HBF approaches for FD mmWave systems are based on optimization processes, which are either too complex or strongly rely on the quality of channel state information (CSI). We propose two learning schemes to design HBF for FD mmWave systems, i.e., extreme learning machine based HBF (ELM-HBF) and convolutional neural networks based HBF (CNN-HBF). Specifically, we first propose an alternating direction method of multipliers (ADMM) based algorithm to achieve SI cancellation beamforming, and then use a majorization-minimization (MM) based algorithm for joint transmitting and receiving HBF optimization. To train the learning networks, we simulate noisy channels as input, and select the hybrid beamformers calculated by proposed algorithms as targets. Results show that both learning based schemes can provide more robust HBF performance and achieve at least 22.1% higher spectral efficiency compared to orthogonal matching pursuit (OMP) algorithms. Besides, the online prediction time of proposed learning based schemes is almost 20 times faster than the OMP scheme. Furthermore, the training time of ELM-HBF is about 600 times faster than that of CNN-HBF with 64 transmitting and receiving antennas.

Wireless content caching in cellular networks is an efficient way to reduce the service delay and alleviate backhaul pressure. For the benefits of sharing spectral and storage resources, clustering in cached networks has recently attracted significant research interests. Meanwhile, since the multimedia content (e.g., video) of caching networks may require a huge transmission rates, millimeter wave (mmWave) communication is considered to be an efficient transmission scheme for cache-enabled networks. We investigate the ergodic rate and average service delay for typical user terminal (UT) in the clustered cache-enabled small cell networks (SCN) and ultra dense networks (UDN) with mmWave channels. In SCN, each cluster consists of cache-enabled UTs, and in the UDN a cluster is formed by cache-enabled UTs and small base stations (SBSs) with non-uniform caching capacity. The clusters are assumed to be discs and content sharing is only possible within clusters through mmWave device-to-device (D2D) tier and SBS tier communications. With stochastic geometry methods, the distributions of content sharing distance and signal-to-interference-noise-ratio (SINR) of typical UT in a cluster are derived for both SCN and UDN scenarios. To minimize the average service delay in high SINR region, we provide an algorithm to jointly optimize caching scheme for SBSs and UTs. By simulations, we validate our theoretical analysis and the performance of proposed caching scheme. The numerical results also show that there exists best radius in the design of cluster for UDNs.

Cell-free networks are considered as a promising distributed network architecture to satisfy the increasing number of users and high rate expectations in beyond-5G systems. However, to further enhance network capacity, an increasing number of high-cost base stations (BSs) is required. To address this problem and inspired by the cost-effective intelligent reflecting surface (IRS) technique, we propose a fully decentralized design framework for cooperative beamforming in IRS-aided cell-free networks.  We first transform the centralized weighted sum-rate maximization problem into a tractable consensus optimization problem, and then an incremental alternating direction method of multipliers (ADMM) algorithm is proposed to locally update the beamformer. The complexity and convergence of the proposed method are analyzed, and these results show that the performance of the new scheme can asymptotically approach that of the centralized one as the number of iterations increases. Results also show that IRSs can significantly increase the system sum-rate of cell-free networks and the proposed method outperforms existing decentralized methods.

Hybrid beamforming (HBF) design is a crucial stage in millimeter wave (mmWave) multi-user multi-input multi-output (MU-MIMO) systems. However, conventional HBF methods are still with high complexity and strongly rely on the quality of channel state information. We propose an extreme learning machine (ELM) framework to jointly optimize transmitting and receiving beamformers. Specifically, to provide accurate labels for training, we first propose a factional-programming and majorization-minimization based HBF method (FP-MM-HBF). Then, an ELM based HBF (ELM-HBF) framework is proposed to increase the robustness of beamformers. Both FP-MM-HBF and ELM-HBF can provide higher system sum-rate compared with conventional methods. Moreover, ELM-HBF cannot only provide robust HBF performance but also consume very short computation time.

For the capability of providing multi-Giga BPS (bits per second) rates, millimeter wave (mmWave) communication is one of the key enabling technologies for the new and future generations of mobile communications, i.e., the fifth generation (5G) and beyond. Meanwhile, non-orthogonal multiple access (NOMA) can significantly increase the spectral efficiency by simultaneously serving multiple users in the same channel. Thus, mmWave NOMA networks have recently attracted considerable research attention. Meanwhile, a large number of confidential messages exchanged within highly interconnected systems has posed tremendous challenges on secure wireless communications, and thus in this article, we investigate the physical layer security of mmWave NOMA networks. Considering the limited scattering characteristics of mmWave channels and imperfect successive interference cancellation at receivers, we develop an analytic framework for the secrecy outage probability (SOP) for mmWave NOMA networks, in which legitimate users and eavesdroppers are randomly distributed. Based on the directional transmission property of mmWave signals, we propose a minimal angle-difference user pairing scheme to reduce the SOP of users. Considering the spatial correlation between the selected user pair and eavesdroppers, we develop two maximum ratio transmission (MRT) beamforming schemes to further enhance the secrecy performance of mmWave NOMA networks. Closed-form SOPs for the paired users with different eavesdropper detection capacities are derived. Numerical results show the effectiveness of our analysis and that there exists an optimal radius of network coverage ranges and transmit power to minimize the SOP of the user pair.

Millimeter Wave (mmWave) communication has attracted massive attention, since the abundant available bandwidth can potentially provide reliable communication with orders of magnitude capacity improvements relative to microwave. However, the achievable rate of mmWave channels under latency and reliability constraints is still not quite clear. We investigate the achievable rates of mmWave channels by random coding error exponent (RCEE) with finite blocklength. With imperfect channel state information at the receiver, the exact and approximate analytical expressions of the training based maximum achievable rate are derived to capture the relationship among rate-latency-reliability. Additionally, the relationship between the training based maximum achievable rate and bandwidth is investigated. We show that there exists critical bandwidth to maximize the training based maximum achievable rate for the non-line-of-sight (NLoS) propagation. Numerical results show that the approximate expression of the training based maximum achievable rate are tight and can capture the tendency at low SNRs. In addition, results show that for a given rate, one can reduce both packet duration and decoding error probability by increasing bandwidth. Results also suggest that in some mmWave bands, e.g. 57-64 GHz band, the performance, i.e., Gallager function, is significantly affected by frequency selective power absorption.

Millimeter Wave (mmWave) communication has attracted massive attentions, since the abundant available bandwidth can potentially provide reliable communication with orders of magnitude capacity improvements relative to microwave. However, the achievable rate of mmWave channels under latency and reliability constraints is still not quite clear. We investigate the achievable rates of mmWave channels by random coding error exponent (RCEE) with finite blocklength. With imperfect channel state information at the receiver, the exact and approximate analytical expressions of the training based maximum achievable rate are derived to capture the relationship among rate-latency-reliability. Additionally, the relationship between the training based maximum achievable rate and bandwidth is investigated. We show that there exists critical bandwidth to maximize the training based maximum achievable rate for the non-line-of-sight (NLoS) propagation. Numerical results show that the approximate expression of the training based maximum achievable rate are tight and can capture the tendency at low SNRs. In addition, results show that for a given rate, one can reduce both packet duration and decoding error probability by increasing bandwidth. Results also suggest that in some mmWave bands, e.g. 57-64 GHz band, the performance, i.e., Gallager function, is significantly affected by frequency selective power absorption.