In this paper, a comprehensive survey is given on several major systematic approaches in dealing with delay-aware control problems, namely the equivalentrate constraint approach, the Lyapunov stability drift approach, and the approximate Markov decision process approach using stochastic learning. These approaches essentially embrace most of the existing literature regarding delay-aware resource control in wireless systems. They have their relative pros and cons in terms of performance, complexity, and implementation issues. For each of the approaches, the problem setup, the general solution, and the design methodology are discussed. Applications of these approaches to delay-aware resource allocation are illustrated with examples in single-hop wireless networks. Furthermore, recent results regarding delay-aware multihop routing designs in general multihop networks are elaborated. Finally, the delay performances of various approaches are compared through simulations using an example of the uplink OFDMA systems.

Frequency-division duplexing and time-division duplexing are two common duplexing methods used in various wireless systems. However, there are advantages and technical issues associated with them. In this article we discuss in detail the features, and the design and implementation challenges of FDD and TDD systems for 4G wireless systems. In particular, we present a number of advantages and flexibilities an TDD system can bring to 4G systems that an FDD system cannot offer, and identify the major challenges, including cross-slot interference, in applying TDD in practice. Due to the fact that cross-slot interference is one of the critical challenges to employing TDD in cellular networks, we also provide a quantitative analysis on its impact on co-channel and adjacent channel interfering cells

While machine-type communication (MTC) devices generate considerable amounts of data, they often cannot process the data due to limited energy and computational power. To empower MTC with intelligence, edge machine learning has been proposed. However, power allocation in this paradigm requires maximizing the learning performance instead of the communication throughput, for which the celebrated water-filling and max-min fairness algorithms become inefficient. To this end, this paper proposes learning centric power allocation (LCPA), which provides a new perspective on radio resource allocation in learning driven scenarios. By employing 1) an empirical classification error model that is supported by learning theory and 2) an uncertainty sampling method that accounts for different distributions at users, LCPA is formulated as a nonconvex nonsmooth optimization problem, and is solved using a majorization minimization (MM) framework. To get deeper insights into LCPA, asymptotic analysis shows that the transmit powers are inversely proportional to the channel gains, and scale exponentially with the learning parameters. This is in contrast to traditional power allocations where quality of wireless channels is the only consideration. Last but not least, a large-scale optimization algorithm termed mirror-prox LCPA is further proposed to enable LCPA in large-scale settings. Extensive numerical results demonstrate that the proposed LCPA algorithms outperform traditional power allocation algorithms, and the large-scale optimization algorithm reduces the computation time by orders of magnitude compared with MM-based LCPA but still achieves competing learning performance.

The ever-growing popularity and rapid development of artificial intelligence (AI) have raised rethinking on the evolution of wireless networks. Mobile edge computing (MEC) provides a natural platform for AI applications since has rich computation resources to train machine learning (ML) models, as well as low-latency access to the data generated by mobile and Internet of things (IoT) devices. In this article, we present an infrastructure to perform ML tasks at an MEC server with the assistance of a reconfigurable intelligent surface (RIS). In contrast to conventional communication systems where the principal criterions are to maximize the throughput, we aim at maximizing the learning performance. Specifically, we minimize the maximum learning error of all participating users by jointly optimizing transmit power of mobile users, beamforming vectors of the base station (BS), and the phase-shift matrix of the RIS. An alternating optimization (AO)-based framework is proposed to optimize the three terms iteratively, where a successive convex approximation (SCA)-based algorithm is developed to solve the power allocation problem, closed-form expressions are derived to solve the beamforming design problem, and an alternating direction method of multipliers (ADMM)-based algorithm is designed to efficiently solve the phase-shift matrix design problem. Simulation results demonstrate significant gains of deploying an RIS and validate the advantages of our proposed algorithms over various benchmarks. Lastly, a unified sensing-communication-learning platform is developed based on the CARLA simulator and the SECOND network, and a use case (3D object detection in autonomous driving) for the proposed scheme is demonstrated on the developed platform.

In most of the existing works on cognitive radio (CR) systems, the spectrum sensing and the cross-layer scheduling are designed separately. Specifically, the sensing module first determines whether or not a channel resource is available for the CR system based on the sensing information. The scheduling module then schedules the data transmission of different users on the available channels based on the hard-decision sensing information (HSI). In this paper, we shall propose a joint crosslayer and sensing design and study its performance advantages over the aforementioned traditional decoupled approaches. We shall consider the downlink transmission of an OFDMA-based secondary system sharing the spectrum with primary users using cognitive radio technology. We shall rely on the joint design framework to optimize a system utility, which adapts the power allocation and the subcarrier assignment across the secondary users (under a average interference constraint to the primary users) based on both the channel state information (CSI) and the raw sensing information (RSI). In addition, we shall also propose a distributed implementation for the cross-layer sensing and scheduling design using primal-dual decomposition approach. Simulation results reveals the substantial performance gain of the proposed joint design over the conventional CR systems.

Edge federated learning (FL) is an emerging paradigm that trains a global parametric model from distributed datasets based on wireless communications. This paper proposes a unit-modulus over-the-air computation (UMAirComp) framework to facilitate efficient edge federated learning, which simultaneously uploads local model parameters and updates global model parameters via analog beamforming. The proposed framework avoids sophisticated baseband signal processing, leading to low communication delays and implementation costs. Training loss bounds of UMAirComp FL systems are derived and two low-complexity large-scale optimization algorithms, termed penalty alternating minimization (PAM) and accelerated gradient projection (AGP), are proposed to minimize the nonconvex nonsmooth loss bound. Simulation results show that the proposed UMAirComp framework with PAM algorithm achieves a smaller mean square error of model parameters’ estimation, training loss, and test error compared with other benchmark schemes. Moreover, the proposed UMAirComp framework with AGP algorithm achieves satisfactory performance while reduces the computational complexity by orders of magnitude compared with existing optimization algorithms. Finally, we demonstrate the implementation of UMAirComp in a vehicle-to-everything autonomous driving simulation platform. It is found that autonomous driving tasks are more sensitive to model parameter errors than other tasks since the neural networks for autonomous driving contain sparser model parameters.