Performance Analysis and EE Optimization in AAV-Assisted Dual-Layer Heterogeneous Communication Network Based on RSMA

On user fairness of uplink rate splitting multiple access with randomly deployed users

Rate-splitting multiple access (RSMA) and the intelligent reflecting surface (IRS) are novel technologies to improve spectrum and energy efficiencies in multi-antenna networks. In this paper, we investigate a multi-user multiple-input single-output (MISO) simultaneous wireless information and power transfer (SWIPT) system assisted with an IRS and using RSMA. In particular, the IRS assists with data and energy transmission from the base station (BS) to power-splitting (PS) users under a non-linear energy harvesting (EH) model. Our objective is to minimize the transmission power subject to rate and EH requirements at the users. To solve the non-convex problem, we propose a bilevel programming problem in which a genetic algorithm is used to solve the outer optimization problem, and the inner optimization problem is addressed with the semidefinite relaxation (SDR) method. In addition, to study a practical scenario of imperfect channel state information (CSI), we consider a robust beamforming design based on imperfect cascade channels at the BS. Then, we propose a solution based on the S-procedure with the SDR technique. Simulation results show that our proposed RSMA-based scheme provides a significant reduction in transmission power compared to the space-division multiple access (SDMA) and non-orthogonal multiple access (NOMA) methods under perfect and imperfect CSI.

A multi-tier architecture consisting of a macrocell overlaid with small cells, e.g., pico base station (BS), with provision of relays and device-to-device (D2D) communication is needed to satisfy the quality-of-service (QoS) requirements in a joint spectrum and energy efficient manner for the future Fifth generation (5G) networks. D2D communication enables the users located in close proximity to each other to communicate directly without going through the macro-cell, and hence, can be utilised to offload the traffic from the cellular infrastructure. This paper investigates the trade-off between Area Energy Efficiency (AEE) and Area Spectral Efficiency (ASE) in D2D-enabled uplink heterogeneous networks. The tradeoff is modelled as an optimization problem, in which each user wants to maximize its own ASE subject to its required AEE levels. Taking into consideration of the AEE requirement and maximum transmission power constraint, a distributed resource allocation approach is proposed to jointly optimize the mode selection, subcarrier and optimal power allocation by exploiting the properties of fractional programming. The relationship between the achievable AEE and ASE trade-off is investigated with different network parameters.

Rate-splitting multiple access (RSMA) has recently received considerable attention due to its high efficiency in both spectral utilization and energy consumption. Inspired by this emerging technique, this letter presents a secure beamforming (BF) scheme for RSMA-based cognitive satellite terrestrial networks in the presence of multiple eavesdroppers. Assuming that the system operates in the millimeter wave band and only imperfect wiretap channel state information is available at the satellite and terrestrial base station, our objective is to maximize the secrecy-energy efficiency of the earth station (ES) while meeting the constraints on the ES secrecy rate, the cellular users' rate requirements, and transmit power budgets of the satellite and base station. As the formulated optimization problem is mathematically intractable, by applying successive convex approximation and Taylor expansion methods, we propose a robust BF scheme to convert the nonconvex objective and constraints into convex ones, which can be iteratively solved. The effectiveness and superiority of the proposed scheme are confirmed through simulation results.

Current research on Rate-Splitting Multiple Access (RSMA) focuses mostly on the case where the base station is fixed, and the use of UAVs as base stations for wireless communication is growing. The article introduces RSMA, a new type of non-orthogonal multiple access technique, to study the uplink transmission optimization problem of UAVs as base stations, and designs an algorithm based on the Augmented Lagrangian Method (ALM) and the Artificial Fish Swarm Algorithm (AFSA), which adds to the dynamic step size and field of view strategy, as well as the memory behavior of artificial fish. To solve the optimization problem further under the constraints of linear equations, a correction factor is introduced to the algorithm to constrain the behavior of the artificial fish. Finally, we simulate UAV-assisted uplink communication using RSMA, and the results demonstrate that this scheme has higher throughput than Orthogonal Multiple Access (OMA) and Non-Orthogonal Multiple Access (NOMA), which is 11.60% higher than NOMA.

This paper investigates reconfigurable intelligent surface (RIS)-assisted simultaneous wireless information and power transfer (SWIPT) networks with rate splitting multiple access (RSMA). An energy efficiency (EE) maximization problem is formulated subject to the power budget at the transmitter and the quality of service (QoS) requirements of both information communication and energy harvesting, where the beamforming vectors, the power splitting (PS) ratios, the common message rates, and the discrete phase shifts are jointly optimized. To tackle the non-convex problem with both discrete and continuous variables, a deep reinforcement learning-based approach is proposed with the proximal policy optimization (PPO) framework. Different from traditional optimization approaches which optimizes the beamforming vectors and phase shifts separately and alternatively, our proposed PPO-based approach optimizes all the variables in unison. Besides, to perform beamforming design in action space, the beamforming vectors for the common stream and the private stream are respectively designed based on the maximum-ratio transmission and the zero forcing to enhance both energy and information transmission. To evaluate the performance of the PPO-based approach, a successive convex approximation (SCA) and Dinkelbach’s method based solution scheme (named SCA-D scheme) is also presented. Simulation results show that the system EE obtained by the proposed PPO-based approach is close to that obtained by the SCA-D scheme while outperforming various benchmarks. The RSMA contributes to the EE of the system greatly compared with traditional scheme. As for the case of time-varying channels, the proposed PPO-based approach is with much smaller running time by only sacrificing a slight EE performance compared with the SCA-D scheme.

Rate-splitting multiple access (RSMA) is acknowledged as a promising solution for improving the capacity of dense downlink networks designed to meet the severe criteria of networks beyond 5G, in which a large number of users may be simultaneously supplied by a single bandwidth spectrum. Numerous studies have focused on establishing acceptable RSMA solutions for energy optimization measures like energy efficiency (EE) and weighted power consumption of all users under the acceptance of perfect or imperfect channel state information at transmitter (CSIT). This evaluation focuses on the technical features of newly published papers and their relevance in a variety of situations. We also cover the difficulties and unresolved concerns of RSMA applications in future heterogeneous downlink networks.

This paper presents a machine learning-based framework for the predictive deployment of unmanned aerial vehicles (UAVs) as flying base stations (BSs) to offload heavy traffic from ground BSs. To account for time-varying traffic distribution, a long short-term memory (LSTM)-based prediction algorithm is introduced to predict future cellular traffic. A joint K-means and expectation maximization (EM) algorithm based on Gaussian mixture models (GMM) is proposed to determine the service area of each UAV based on the predicted user service distribution. Based on the predicted traffic, the optimal positions of UAVs are derived, and four multiple access techniques, namely, rate splitting multiple access (RSMA), frequency domain multiple access (FDMA), time domain multiple access (TDMA), and non-orthogonal multiple access (NOMA), are compared to minimize the total transmit power. Simulation results show that the proposed method can reduce up to 24% of the total power consumption compared to the conventional method without traffic prediction. Furthermore, RSMA is found to require the lowest transmit power among the four multiple access techniques. Therefore, this paper focuses on the comparison of multiple access techniques for UAV deployment, which is essential for the efficient and effective use of UAVs as flying BSs.

Unmanned aerial vehicles (UAVs) and Satellites are promising as power frameworks for 5G and 6G networks to provide solutions for the limitation in deployment, batteries, and transference reliability in areas. It leads to efficiency in terrestrial systems, broken Base stations (BSs) substitution, reduced cost infrastructures, and mobility management. It is also an indispensable role to improve the quality of Internet Of Things (IoTs) systems. Besides, Rate-Splitting Multiple Access (RSMA) has emerged as a promising technique to manage interference in multiple access (MA) systems, and optimize the non-orthogonal transmission (non-OT) for 5G and 6G networks. In the current, one of the most impressive schemes for the Unmanned Aerial Vehicle (UAV) and Satellite-based networks is RSMA. This paper details a comprehensive review on UAV and Satellite networks with RSMA of wireless network 6G. After analyzing the overview of UAV and Satellite-based networks with RSMA, the main applications of RSMA scheme in networks based on UAV and Satellite-based networks will be exploited. Finally, the main challenges and future directions are suggested and defined.

With the ubiquitous data-rate demand in cellular networks, total power consumption is growing rapidly. Thus, it is essential to design the future networks with energy and spectral efficient strategies. Using heterogeneous networks (HetNets) is one possible way of improving spectral and energy efficiencies in cellular networks. In this paper, under coverage ratio and area spectral efficiency constraints, we consider deploying strategies with low power small cells in order to improve the spectral and energy efficiencies of the conventional macrocell only cellular network. To compare the efficiencies of different scenarios, we employ area spectral efficiency and power consumption per bit criterion. As a result, we observe that deploying microcells increases both energy and spectral efficiencies.

The growth of energy consumption driven by a rise in traffic rate demand and services, requires cellular operators to extend their infrastructure to meet these demands. To address this issue, deployment of low power base stations (BSs) in traditional macrocells have recently being considered to offer high data rates and simultaneously reduce the load of the macrocellular networks. However, the massive and unplanned deployment of small cells and their uncoordinated operations raise important questions about the energy efficiency in the heterogeneous cellular networks. In this paper, the impact of deploying a number of pico BSs on the system performance of macrocellular network is investigated. We analyze the energy efficiency of the downlink macro-picocells heterogeneous network, measured by the traffic capacity divided by the power consumption of the BSs. An adaptive power control based on the iterative-waterfilling scheme has been implemented to control the power among the macro and the pico BSs. The numerical results show that the total macrocellular capacity improves as the number of pico stations increases, but the energy efficiency is improved only for a reasonable number of pico base stations. Moreover, the iterative-waterfilling power allocation scheme improves the energy efficiency even with introducing more pico base stations as compared with other power allocation schemes. This work provides essential understanding for successful deployment of power efficient heterogeneous networks.

The substantial increase in the number of base stations (BSs) compels researchers to focus on spectral efficiency (SE) and energy efficiency (EE) in wireless networks. To this end, we propose an intracell frequency band exiling (ICE) technique as a promising solution for green wireless networks. In the proposed technique, operating frequency bands of mobile users are assigned from upper frequency bands (UFBs) to lower frequency bands (LFBs) by suitably adjusting their coverage area to provide energy-efficient communications. To do this, we derive ICE probabilities on a log-normally distributed traffic model and calculate the EE and area SE (ASE) considering the power consumption model. The simulation results demonstrate that the ASE can be improved by increasing the traffic density. However, increasing the traffic density does not improve the EE beyond a certain threshold. Therefore, we present the tradeoff between EE and ASE and provide an optimum operating point. In addition, we show the ICE performance to be better than that of the existing cell-zooming (CZ) technique, unveil the ICE relation with beyond-5G (B5G) networks, and, finally, provide a cell-exiling manager system to illustrate the applicability of the proposed technique for various implementations.

This paper studies base station (BS) cooperation in nonuniform heterogeneous networks. Considering the limited capacity at BS and existence of untrusted small cell BSs (SBSs) in practical scenarios, a novel capacity- and trust-aware BS cooperation strategy is proposed. The BS cooperation is performed in a user centric manner, based on the average received signal strength at users, the capacity of BSs, and the trustworthiness of SBSs. Furthermore, with the proposed BS cooperation, the statistics of aggregate information-signal strength and interference strength are theoretically analyzed, based on stochastic geometry. Then, expressions for spectral efficiency (SE) and area SE (ASE) are analytically derived. In addition, to study the impact of the SBS density on the SE and ASE, the optimal densities of normal SBSs to maximize the SE and ASE are proved to exist and obtained. Finally, simulations and numerical evaluations validate the theoretical analysis and reveal that with the awareness of BS capacity and trustworthiness, optimal cooperative thresholds achieving the maximum SE and ASE exist, which decrease with high path-loss exponent; and in high path-loss fading environment with high existence probability of untrusted SBSs, more normal SBSs are required to be deployed to achieve the maximum SE and ASE performance.

Integrated sensing and communication (ISAC) has been a promising technology in beyond 5G and 6G network to simultaneously support high-speed information transfer and high-quality environmental perception. Cloud radio access networks (C-RAN) enables the cooperation among multiple base stations (BSs) to provide additional cooperation gains for both communication and sensing functionalities. In this paper, we investigate the cooperative ISAC (CoISAC) system with rate-splitting multiple access (RSMA) transmission scheme for advanced interference management and direct localization sensing scheme to provide high-accuracy positioning services. Specifically, we adopt Pareto optimization framework to characterize the achievable performance region of the CoISAC system built on the sum rate of multiple communication users and the positioning error bound of radar target. Then, we formulate communication-centric and radar-centric optimization problems in the CoISAC system to systematically search for the optimal Pareto boundary of the achievable performance region. Two iterative algorithms based on successive convex approximation (SCA) are proposed to effectively solve these two optimization problems, respectively, therefore near-optimal Pareto boundary can be found. Numerical results show that the RSMA-assisted CoISAC system achieves the best trade-off performance among various baselines. The cooperative scheme can fully unveil the potential of RSMA in the ISAC system by providing more freedom for rate-splitting policy design under limited resources. Therefore, incorporating RSMA in the CoISAC system can significantly boost the overall performance.

In order to solve the problem that the increase of antenna channels brings about the increase of power consumption, which makes the cost of base station electricity increase continuously, the research of adaptive regulation method for optimizing the comprehensive energy efficiency of 5G base station is proposed. This paper is based on the research of ant colony algorithm iterative optimization algorithm to solve the problem of iterative optimal solution of massive MIMO parameter combination. At the same time, a power optimization model based on digital twin technology is constructed to realize the optimization of 5G network energy efficiency. The experimental results show that in the case of base station coverage, the power is reduced from 210W before optimization to 170W after optimization, with an average reduction of 40W, and the coverage RSRP is improved from -96.1dBm before optimization to -94.3dBm after optimization, with an increase of 2dBm, which achieves the goals of energy consumption reduction and efficiency improvement. Conclusion: The algorithm achieves the expected results, and provides a reference for the evolution of energy efficiency optimization and MIMO intelligent optimization in subsequent base stations.