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Aerial base stations (ABSs) are expected to be important supplementary components for the 5G-and-beyond communication systems to achieve global Internet of Everything. To fully exploit the advantages of the ABSs seamless connection, in this paper, we propose a high altitude platform (HAP) and low altitude platforms (LAPs) cooperated network architecture. Specifically, the HAP can extend the network coverage and LAPs can act as relays to improve the transmission performance for hot spots. In the considered network, we investigate the sum rate maximization problem by optimizing the downlink and the uplink transmissions, respectively. In the downlink, we adopt the orthogonal frequency division multiple access technique and take the basic rate requirement into account. In addition, we optimize the height of LAPs and the spectrum allocation between the HAP and LAPs, and between the LAP and users. Then, we decouple the downlink sum rate maximation problem as three subproblems. An alternating optimization framework is designed to deal with these non-convex optimization problems, where the height and spectrum allocation are tackled in turn. In the uplink, we adopt the non-orthogonal multiple access technique, and consider the decoding threshold of the successive interference cancellation technique. Afterward, we optimize the power allocation of each user, and the spectrum allocation between users and the LAP, and between LAPs and the HAP. Finally, simulation results show the effects of spectrum allocation, height optimization and power allocation on network performance, which verify that the proposed scheme can achieve higher sum rate on both downlink and uplink in comparison with the current works. [ABSTRACT FROM AUTHOR]

Intelligent reflecting surface (IRS)-enhanced wireless communication is one of the critical technologies of the sixth generation mobile communication, which has recently emerged as an innovative solution to the realization of intelligent transportation. It can meet the increasing demand for high-speed railway (HSR) communications services. In this paper, we consider a train-ground TDD wireless mobile communication paradigm to deploy two IRSs on the roadside and the train roof to assist the wireless transmission for the HSR communication system. To achieve the more significant performance gain, in the uplink transmission phase, we propose the practical channel estimation framework of the MIMO system aided by double IRSs to acquire reliable channel state information (CSI) of each channel. To facilitate the processing of the three-dimension tensor, we leverage the trilinearity of the parallel factor decomposition to decompose the received signal into a two-dimensional matrix. The Doppler frequency offset compensating scheme and channel tracking scheme based on Kalman-Filter are proposed to further decrease the pilot overheads. Then, in the downlink phase, a block coordinate descent (BCD) algorithm is proposed to handle the data rate maximization problem of double IRSs communication system with the estimated CSI. We reformulate the original problem and decouple the double IRSs phase optimization problem into two single IRS phase optimization non-convex problems under the unit-modulus constraint. The Majorization-Minimization (MM) algorithm is proposed to approximate the optimal solution of the two optimization subproblems. Simulation results corroborate the effectiveness of the proposed channel estimation and phase shifts optimization scheme. [ABSTRACT FROM AUTHOR]

From the fourth generation (4G) of cellular systems onward, wireless personal devices (WPDs) support multi-input multi-output (MIMO) communication. However, the impact of MIMO communication on the electromagnetic field (EMF) of WPD users has yet to be fully understood and analyzed at the system level. In this paper, we first provide a generic model for assessing the individual exposure dose of multi-antenna WPD users in a multi-user multi-carrier communication system. An optimization framework for minimizing this exposure dose is then developed based on our exposure model. This framework helps us to identify a new criterion, i.e., the ratio between the normalized exposure dose and the channel to noise ratio (CNR), as the main system level criterion for minimizing the individual exposure dose of multi-antenna WPD users. This criterion is further integrated in the design of two novel centralized resource allocation schemes that take advantage of the multiple antennas at the WPD to minimize the per-user exposure dose, when full or limited knowledge of each user channel is available. Our new schemes can significantly reduce the individual exposure dose of WPD users (by approximately 80%) in comparison with the most relevant existing resource allocation schemes. Our results also provide insights into the logarithmic relationship between the per-user exposure dose and the number of receive antennas (or the number of time slots), and how such a parameter can be exploited to further reduce the exposure and/or provide a higher SE while maintaining a low exposure dose. [ABSTRACT FROM AUTHOR]

Inspired by the promising potentialof reconfigurable intelligent surface (RIS)-based transmission, this paper investigates the RIS-aided vehicle-to-everything (V2X) communication system with the social trust, where multiple vehicle-to-infrastructure (V2I) links share the spectrum with vehicle-to-vehicle (V2V) links. Subject to the reliability constraint of V2V links, the sum V2I link capacity is maximized, through jointly optimizing the power allocation, RIS reflection coefficients and spectrum allocation. The formulated mix-integer and non-convex problem is effectively converted into three subproblems, which can be iteratively solved by our proposed three-stage joint resource allocation and RIS optimization (JRARO) algorithm. Simulation results demonstrate the effectiveness of the proposed algorithm, and that the RIS can significantly improve the quality of vehicular communications in terms of the sum V2I capacity. [ABSTRACT FROM AUTHOR]

In this paper, we study an unmanned aerial vehicle (UAV)-enabled wireless powered communication network, where a UAV serves multiple energy constrained wireless sensor nodes (WSNs). In such a network, a UAV transmits wireless power to a group of WSNs which exploits the harvested power to transfer their own information towards the UAV in the uplink. For practical scenarios, a nonlinear energy harvesting saturation model is considered at WSNs. We aim to design resource allocation which maximizes the achievable sum rate in the uplink and minimizes the downlink transmit power simultaneously. The design is formulated as a multi-objective optimization problem (MOOP) which optimizes the UAV's 3D position, its transmit power, the time splitting ratio, the uplink transmission time and the beamwidth angle of the UAV. The formulated problem is a non-convex problem which is generally intractable. To address the MOOP, a weighted Tchebycheff method is proposed. The numerical results show a trade-off between the minimum power in the downlink and the maximum rate as well as energy efficiency in the uplink. [ABSTRACT FROM AUTHOR]

To support ultra-reliable and low-latency communication (URLLC) in vehicular networks, the virtual cells, where multiple access points (APs) cooperatively serve one mobile node, have been proposed to reduce the end-to-end latency in the downlink. The latency can be further reduced by eliminating the need for retransmission and feedback control, i.e., open-loop communications. However, it is difficult to achieve a high reliability of the uplink via virtual cells, because of multiple access interference and collisions from other virtual cells nearby. In this paper, we formulate the proactive radio resource allocation in the open-loop uplink of vehicular networks as a stochastic optimization problem with the objective to maximize the uplink reliability while ensuring the network stability. The optimal resource allocation policies are obtained solving the optimization problem using the Lyapunov optimization technique in a distributed manner. To reduce the computational and to improve the challenges of the Lyapunov optimization, we propose a virtual resource slicing algorithm that maps radio resource units to virtual resource blocks. Simulation results exhibit that both ultra-low latency and ultra high reliability are guaranteed in the open-loop uplink of vehicular networks. Based on the theoretical performance analysis and simulations, the uplink radio access procedure is summarized for the URLLC in vehicular networks. [ABSTRACT FROM AUTHOR]

This paper studies multi-agent deep reinforcement learning (MADRL) based resource allocation methods for multi-cell wireless powered communication networks (WPCNs) where multiple hybrid access points (H-APs) wirelessly charge energy-limited users to collect data from them. We design a distributed reinforcement learning strategy where H-APs individually determine time and power allocation variables. Unlike traditional centralized optimization algorithms which require global information collected at a central unit, the proposed MADRL technique models an H-AP as an agent producing its action based only on its own locally observable states. Numerical results verify that the proposed approach can achieve comparable performance of the centralized algorithms. [ABSTRACT FROM AUTHOR]

This paper considers a heavy loaded full-duplex (FD) small cell consisting of one FD base station and multiple half-duplex users where the number of subchannels is less than the number of uplink and downlink users. With the objective of maximizing the total sum-rate, a joint power allocation, and user pairing problem is formulated. To solve this problem, a novel decomposition method is proposed, which decouples the power allocation and user pairing problem into two subproblems. It is proven that the optimal solution of subproblems is the optimal solution of the original problem. Then, optimal algorithms are developed for the two subproblems. Numerical results show that the proposed algorithm achieves better performance compared with the baselines of the greedy algorithm and random pairing algorithm. [ABSTRACT FROM AUTHOR]

This paper considers a multiuser simultaneous wireless information and power transfer system with a nonlinear energy harvesting model, in which a multiantenna base station estimates the downlink channel state information (CSI) via uplink pilots. Each single-antenna user is equipped with a power splitter. Three crucial issues on resource management for this system include: 1) power-efficient improvement, 2) user-fairness guarantee, and 3) nonideal channel reciprocity effect mitigation. Potentially, a resource allocation scheme to address jointly such issues can be devised by using the framework of multiobjective optimization. However, the resulting problem might be complex to solve since the three issues hold different characteristics. Therefore, we propose a novel method to design the resource allocation scheme. In particular, the principle of our method relies on structuralizing mathematically the issues into a cross-layer multilevel optimization problem. On this basis, we then devise solving algorithms and closed-form solutions. Moreover, to instantly adapt the CSI changes in practice while reducing computational burdens, we propose a closed-form suboptimal solution to tackle the problem. Finally, we provide numerical results to show the achievable performance gains using the optimal and suboptimal solutions, and then validate the proposed resource allocation scheme. [ABSTRACT FROM AUTHOR]

Proximate cloud computing enables computationally intensive applications on mobile devices, providing a rich user experience. However, remote resource bottlenecks limit the scalability of offloading, requiring optimization of the offloading decision and resource utilization. To this end, in this paper, we leverage the variability in capabilities of mobile devices and user preferences. Our system utility metric is a measure of quality of experience (QoE) based on task completion time and energy consumption of a mobile device. We propose a heuristic offloading decision algorithm (HODA), which is semidistributed and jointly optimizes the offloading decision, and communication and computation resources to maximize system utility. Our main contribution is to reduce the problem to a submodular maximization problem and prove its NP-hardness by decomposing it into two subproblems: 1) optimization of communication and computation resources solved by quasiconvex and convex optimization and 2) offloading decision solved by submodular set function optimization. HODA reduces the complexity of finding the local optimum to O(K^3), where $K$ is the number of mobile users. Simulation results show that HODA performs within 5% of the optimal on average. Compared with other solutions, HODA's performance is significantly superior as the number of users increases. [ABSTRACT FROM PUBLISHER]