Millimeter-wave (mmWave) beamspace massive multiple-input multiple-output (mMIMO) system with lens antenna array can minimize transceiver hardware complexity without compromising performance. However, the number of supported portable user terminals (PUTs) cannot exceed the number of radio frequency (RF) blocks accessible at the same time, frequency, and coding resources. As a result, we propose the integration of rate-splitting multiple access (RSMA) into a beamspace mMIMO system to support a larger number of PUTs than the number of available RF blocks while minimizing intra-beam interferences. Moreover, orthogonal random precoding (ORP) is utilized in the downlink of the beamspace mMIMO system to mitigate the inter-beam interferences and extend the cell coverage area. Then, we develop an optimization problem to optimize the system’s overall throughput while keeping the minimum needed throughput and power budget in consideration. The nonconvex optimization issue is then turned into a convex optimization problem using the successive convex approximation approach. Following that, we offer an alternating method to solve the approximate optimization issue and select an optimal solution. Furthermore, the suggested method’s effectiveness is evaluated in terms of total throughput, energy efficiency, and cell coverage area. Finally, numerical results confirm the superior performance of the proposed method over benchmark techniques in terms of sum throughput, energy efficiency, and cell coverage area.

The development of our neoteric scientific fields has reached such a magnificent level that the benefits from innovation have extended to all regions, including those most distant. Modern information and communication network systems have greatly improved the overall effectiveness and performance of the wireless networks. With the emergence of cellular networks, the wireless communication network system has evolved from the first generation to the sixth generation (6G), which will provide an integrated framework for a variety of utilities and applications. The goal of 6G network systems is to improve channel capacity, low bit rate error, efficient signal transmission, and low latency while providing reliable connectivity and seamless communication. Various technologies are integrated with 6G network systems to provide controlled, efficient, and reliable communication. One emerging technology for 6G communication network systems is reconfigurable intelligent surfaces (RIS), which is ideal for smooth and controllable wireless signal transmission. RIS uses smart beamforming methods with the assistance of an electronic circuit controller to provide superior signal quality. This study presents a complete overview of RIS, including its hardware architecture, key characteristics, control mechanism, operating frequency, communication duplex mode, and operating mode. The study also elaborates on the RIS operational environment, advantages of implementing RIS, deployment, and different types of communication by utilizing RIS. Finally, we conclude with a discussion of challenges and future research directions to overcome limitations to achieve the goal of RIS implementation and RIS-aided communication for 6G communication systems.

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.