The sixth generation (6G) wireless systems are envisioned to enable the paradigm shift from “connected things” to “connected intelligence”, featured by ultra high density, large-scale, dynamic heterogeneity, diversified functional requirements, and machine learning capabilities, which leads to a growing need for highly efficient intelligent algorithms. The classic optimization-based algorithms usually require highly precise mathematical model of data links and suffer from poor performance with high computational cost in realistic 6G applications. Based on domain knowledge (e.g., optimization models and theoretical tools), machine learning (ML) stands out as a promising and viable methodology for many complex large-scale optimization problems in 6G, due to its superior performance, computational efficiency, scalability, and generalizability. In this paper, we systematically review the most representative “learning to optimize" techniques in diverse domains of 6G wireless networks by identifying the inherent feature of the underlying optimization problem and investigating the specifically designed ML frameworks from the perspective of optimization. In particular, we will cover algorithm unrolling, learning to branch-and-bound, graph neural network for structured optimization, deep reinforcement learning for stochastic optimization, end-to-end learning for semantic optimization, as well as wireless federated learning for distributed optimization, which are capable of addressing challenging large-scale problems arising from a variety of crucial wireless applications. Through the in-depth discussion, we shed light on the excellent performance of ML-based optimization algorithms with respect to the classical methods, and provide insightful guidance to develop advanced ML techniques in 6G networks. Neural network design, theoretical tools of different ML methods, implementation issues, as well as challenges and future research directions are also discussed to support the practical use of the ML model in 6G wireless networks.

Unmanned aerial vehicle (UAV) with flexible mobility and low cost, has been a promising technology for wireless communication. Thus, it can be used for wireless data collection in Internet of things (IoT). In this paper, we consider millimeter-wave (mmWave) communication on a UAV platform, where the UAV base station (UAV-BS) serves multiple ground users which generate big sensor data. Both the deployment of the UAV-BS and the beamforming design have essential impact on the throughput of the system. Thus, we formulate a problem to maximize the achievable sum rate of all the users, subject to a minimum rate constraint for each user, a position constraint of the UAV-BS, and a constant-modulus constraint for the beamforming vector. We solve the non-convex problem with two steps. First, by introducing the approximate beam pattern, we solve the deployment and beam gain allocation sub-problem. Then, we utilize the artificial bee colony (ABC) algorithm to solve the beamforming sub-problem. For the global optimization problem, we find the near-optimal position of the UAV-BS and the beamforming vector to steer towards each user subject to an analog beamforming structure. Simulation results demonstrate that the proposed solution can achieve a more superior performance than the present random steering beamforming strategy in terms of achievable sum rate.

The ever-increasing growth in maritime activities with large amounts of maritime Internet of Things (M-IoT) devices and the exploration of ocean network leads to a great challenge for dealing with a massive amount of maritime data in a cost-effective and energy-efficient way. However, the resources-constrained maritime users cannot meet the high requirements of transmission delay and energy consumption, due to the excessive traffic and limited resources in maritime networks. To solve this problem, mobile edge computing is taken as a promising paradigm to help mobile devices from edge servers via computation offloading considering the different quality of services (QoS) with the complex ocean environments, resulting in energy saving and increased transmission latency. To investigate the tradeoff between latency and energy consumption in low-cost large-scale maritime communication, we formulate the offloading optimization problem and propose a two-stage joint optimal offloading algorithm, optimizing computation and communication resource allocation under limited energy and sensitive latency. At the first stage, the maritime users make the decision on whether to offload a computation considering their demands and environments. Then, the channel allocation and power allocation problem were proposed to optimize the offloading policy which coordinates with the center cloud servers at the second stage, considering the dynamic tradeoff of latency and energy consumption. Finally, numerical simulation results show the effectiveness of the proposed algorithm.

The existing channel estimation methods for millimeter-wave (mmWave) communications, e.g., hierarchical search and compressed sensing (CS), either acquire only one single multipath component (MPC) or require considerably high training overhead. To realize fast yet accurate channel estimation, we propose a multipath decomposition and recovery (MDR) approach in this paper. The proposed approach has two stages. In the first stage, instead of directly searching the real MPCs, we decompose each real MPC into several virtual MPCs, and acquire the virtual MPCs by using the hierarchical search based on a normal-resolution codebook. Then in the second stage, the real MPCs are recovered from the acquired virtual MPCs in the first stage, which turns out to be a sparse reconstruction problem, where the size of the dictionary matrix is greatly reduced by exploiting the results of the virtual multipath acquisition. Moreover, to make the proposed approach applicable for both analog and hybrid beamforming/combining devices with strict constant-modulus constraint, we particularly design a codebook for the hierarchical search by using an enhanced sub-array technique, and the codebook is also applicable in other hierarchical search methods. Performance comparisons show that the proposed approach achieves a superior tradeoff between estimation performance and training penalty over the state-of-the-art alternatives.

The unmanned aerial vehicles (UAVs) are envisioned as promising aerial facilities for providing advanced communication services as well as sensing functionalities in the next-generation wireless system. This paper considers a UAV-enabled integrated sensing and communications (ISAC) system, where a moving ground user (GU) is simultaneously tracked by multiple UAVs and receives the downlink communication information transmitted from the UAV. In particular, to jointly enhance the sensing and communication (S&C) performance, optimizing the UAV moving trajectory is demanded. To achieve this goal, we first harness the extended Kalman filtering (EKF) method for predicting and tracking the motion parameters of GU at each time slot, which relies on the range measurements extracted from the sensing echoes at the base station (BS). Afterward, we formulate a weighted optimization problem that addresses the design of UAV trajectories and GU-UAV association simultaneously, incorporating the consideration of real-time downlink communication rates and the Cramér-Rao bound (CRB) for GU tracking. The problem further is constrained by the maximum consumed power, maximum traveling distance, and minimum collision avoidance distance. As a step forward, to address the resultant non-convex problem, we develop an efficient iterative algorithm to obtain a near-optimal solution by utilizing the successive convex approximate (SCA) technique. Specifically, we alternately solve the GU-UAV association and the real-time trajectory design problem at each time slot. Finally, our numerical simulations illustrate that our proposed algorithm can track the GU accurately while meeting the sensing-centric and/or communication-centric requirements.

Since conventional multiple access schemes, such as time division multiple access (TDMA), frequency division multiple access (FDMA), et al., cannot meet the requirements on flexibility, throughput and access delay in large-scale flying ad hoc networks (FANETs), we propose a fair and efficient CSMA/CA-based MAC protocol to facilitate concurrent uplink transmissions from different unmanned aerial vehicles (UAVs) by leveraging multiple-user MIMO (MU-MIMO). In this paper, we first propose the MIMO-based integrated sensing and backscatter communication model to achieve address resolution and also realize channel estimation by leveraging maximum likelihood estimation. Next, we propose an analytical model to characterize the saturation throughput and mean access delay of this CSMA/CA-based MAC protocol operating in an MUMIMO FANET. Moreover, we derive the accurate expressions of saturation throughput and access delay under the proposed model. By means of the developed model, we evaluate the saturation throughput and access delay performance with respect to different network parameters, including the total payload, the number of UAVs and the number of UAV receiver's antennas. Numerical results indicate that our proposed protocol achieves superior throughput and decreased access delay