The demand for wireless connectivity has grown exponentially over the last few decades. Fifth-generation (5G) communications, with far more features than fourth-generation communications, will soon be deployed worldwide. A new paradigm of wireless communication, the sixth-generation (6G) system, with the full support of artificial intelligence, is expected to be implemented between 2027 and 2030. Beyond 5G, some fundamental issues that need to be addressed are higher system capacity, higher data rate, lower latency, higher security, and improved quality of service (QoS) compared to the 5G system. This paper presents the vision of future 6G wireless communication and its network architecture. This article describes emerging technologies such as artificial intelligence, terahertz communications, wireless optical technology, free-space optical network, blockchain, three-dimensional networking, quantum communications, unmanned aerial vehicles, cell-free communications, integration of wireless information and energy transfer, integrated sensing and communication, integrated access-backhaul networks, dynamic network slicing, holographic beamforming, backscatter communication, intelligent reflecting surface, proactive caching, and big data analytics that can assist the 6G architecture development in guaranteeing the QoS. Besides, expected applications with 6G communication requirements and possible technologies are presented. We also describe potential challenges and research directions for achieving this goal.

This letter studies the energy-efficient unmanned aerial vehicle (UAV) communications to support ground nodes (GNs). The system considers the UAV working as a relay while there is a base station (BS) on the ground. We analyze the UAV energy consumption model to design the energy-efficient UAV trajectory path. We formulate the energy-efficient UAV relaying communication, which considers both throughput and UAV propulsion energy consumption. We optimize joint transmit power of UAV and BS; UAV trajectory, acceleration, and flying speed to maximize the energy-efficient UAV relaying problem. We also introduce a constraint named as information causality constraint (ICC). The main idea of ICC is to guarantee that the UAV receives information from BS in any time slot and forward the only received information to GNs in remaining time slots. The formulated energy-efficiency maximization problem is not convex. Thus, we solve it sub-optimally using the iterative method. Finally, we present the simulation results to validate the efficacy of the proposed algorithm.

The unmanned aerial vehicle (UAV) communication is a potential technology to meet the excessive next-generation cellular users’ demand due to its reliable connectivity and cost-effective deployment. However, UAV communications have to be energy efficient so that it can save energy. Thus, the UAV flies sufficiently long enough time to serve the ground users with limited on-board energy. In this paper, we investigate an energy-efficient UAV communication via designing the UAV trajectory path. We consider throughput and the UAV propulsion energy consumption jointly. We assume that the UAV flies at a fixed altitude such that it can avoid tall obstacles. A binary decision variable is assigned to schedule UAV-to-user communication. First, we derive the UAV-to-user channel model based on the line of sight and non-line of sight communication links and jointly optimize the trajectory, transmit power, and the speed of UAV; and UAV-to-user scheduling to maximize throughput. Then, we apply the UAV propulsion energy consumption, which is a function of the UAV trajectory and speed. Finally, we formulate the UAV energy-efficiency maximization problem, which is defined as the total bits of information sent to the ground users by consuming the UAV energy for a given UAV flight duration. The formulated energy-efficiency maximization problem is non-convex, fractional, and mixed-integer non-linear programming in nature. We propose an efficient algorithm based on successive convex approximation and classical Dinkelbach method to achieve the optimal solution of energy-efficient UAV. We present simulation results to validate the efficacy of our proposed algorithms. The results show a significant performance improvement compared to the benchmark methods.

As the demand for high-speed data transmission grows with the expected emergence of 6G networks and the proliferation of wireless devices, more than traditional wireless infrastructure may be required. Small cell networks (ScNs) integrated with reconfigurable intelligent surfaces (RISs) and multiple-input-multiple-output (MIMO) have emerged as promising solutions to address this issue. However, ScNs have resource allocation limitations, and traditional RISs can only reflect signals in a limited propagation space of $180^{0}$ with fixed reflection properties. This paper proposes a novel approach to overcome these challenges by introducing actively simultaneously transmitting and reflecting (ASTAR)-RISs. Unlike conventional RIS, ASTAR-RISs actively amplify and transmit signals, effectively mitigating the limited propagation challenge and improving signal strength, especially in dense ScNs. This approach enhances the quality of service in complex channel environments by amplifying, on top of reflection, from the macro base station (mBS), improving the overall signal strength, and providing $360^{0}$ flexible propagation space. Furthermore, ASTAR-RIS enables dynamic beam management, significantly improving signal coverage and interference management, which are crucial in dense deployments. In this work, we propose a network architecture where distributed ASTAR-RIS units are deployed to assist small cell mBSs by optimizing signal coverage and enhancing communication performance. ASTAR-RISs dynamically control signal reflection and amplification, complementing the functionality of traditional small-cell BSs in dense network environments. Using the MIMO technique, we design phase shifts for ASTAR elements and develop optimal hybrid beamforming for users at the mBS. We dynamically control the ON/OFF status of the ASTAR-RIS based on active or idle status. We propose an efficient model that ensures fairness of signal-to-noise ratio (SNR) for all users and minimizes overall power consumption while meeting user SNR and phase shift constraints. To this end, we integrate robust beamforming and power allocation strategies, ensuring the system maintains reliable performance even under imperfect channel state information (CSI). We formulate a max-min optimization problem that optimizes the SNR and power consumption, subject to the ON/OFF status, phase shift, and power budget of the ASTAR-RIS. Our proposed method uses an alternating optimization algorithm to optimize the phase shift matrix at the ASTAR-RIS and the hybrid beamforming at the mBS. The approach includes two transmission schemes, and the phase optimization problem is solved using a successive convex approximation method that offers a closed-form solution at each step. Additionally, we use the dual method to determine the optimal ON/OFF status of the ASTAR-RIS. Comprehensive simulations validate the robustness and scalability of our proposed solution, particularly under varying network densities and CSI uncertainties. provides significant performance improvements over 170% compared to traditional RIS schemes.

Cognitive unmanned aerial vehicles (CUAVs) play a vital role in next-generation wireless networks as they assist in massive machine-type communication (mMTC) and ultra-reliable low-latency communication (URLLC) services. This study focuses on multiple CUAV-enabled networks wherein CUAVs are paired with each other. We analyze the data rate, energy efficiency, and latency of such networks by applying the finite information block length theory, wherein mMTC and URLLC information use a non-orthogonal multiple access technique. Furthermore, we formulate an optimization problem to maximize the energy efficiency of paired CUAV devices by jointly optimizing the transmission power of the mMTC and URLLC information to satisfy the latency requirement. The numerical results indicate that our proposed multiple-CUAV-enabled scheme enhances the network performance of CUAV devices in terms of energy efficiency and latency better than the existing scheme.

Enhancing Internet of Things (IoT) communications through Reconfigurable Intelligent Surfaces (RIS) necessitates novel approaches that go beyond the conventional deployment of passive elements. This paper introduces an efficient method to enhance IoT system performance by combining active and passive functionality in RISs: an active and passive integrated approach. One identified enhancement area is efficiently managing signal throughput and exchanges in IoT systems, which is essential for supporting numerous devices. For this challenge, we propose a thorough process model that describes channel gains, develops a nonlinear energy harvesting, and identifies device-to-device communication schemes without violating information causality. The innovative component of our work is the utilization and strategic application of RIS panels that benefit from the advantages of active and passive components synergism to solve thermal noise issues and optimize signal reflection and transmission. An advanced optimization mechanism is developed based on mixed-integer nonlinear programming: an enabling approach between performance efficiency and maximum service utilization. Our simulation analysis show that developed RIS panels optimize IoT system performance and surpass existing performance indicators in conventional RIS-less systems.