Based on our current understanding, extreme massive access over the same physical channel is only possible if an extra-large multiple-input multiple-output (XL-MIMO) antenna is used at the base station (BS) and instantaneous channel state information (CSI) is known at the BS side for precoding design. This casts doubt on scalability and challenges in device-to-device situations in which there is not a centralized, optimized BS for transmitting the user signals. To address this problem, we revisit the massive connectivity challenge by considering the case where no CSI is available at the BS and no precoding is used. In this situation, inter-user interference (IUI) mitigation can only be performed at the user terminal (UT) side. Leveraging the position flexibility of fluid antenna system (FAS), we adopt a fluid antenna multiple access (FAMA) approach that exploits the interference signal fluctuation in the spatial domain. Specifically, we assume that we haveNspatially correlated received signals per symbol duration from FAS. Our main approach uses a simple heuristic port shortlisting method that identifies promising ports to obtain favourable received signals that can be combined via maximum ratio combining (MRC) to form the received output signal for final detection. On top of this, a pre-trained deep joint source-channel coding (JSCC) scheme is employed, which together with a diffusion-based denoising model (MixDDPM) at the UT side, can improve the IUI immunity. We refer to the proposed scheme as turbo FAMA. Simulation results show that with a physical FAS size of 20 wavelengths at each UT transmitting quaternary phase shift keying (QPSK) symbols, fast FAMA can support 50 users whileturboFAMA can handle up to 200 users if the required symbol error rate (SER) is 10-2. If a higher error tolerance is acceptable, say SER at 0.1, turbo FAMA can even serve up to 1000 users but fast FAMA is only able to handle 160 users, all remarkably achieved without CSI at the BS.

The emergence of fluid antenna systems (FAS) offers a novel technique for obtaining spatial diversity and leveraging interference fades for spectrum sharing in multiuser scenarios—a paradigm referred to as fluid antenna multiple access (FAMA). Nevertheless, as the number of users increases, the interference mitigation capability diminishes. To overcome this, opportunistic scheduling that prioritizes robust users proves to be an effective method for enhancing FAMA. This paper introduces a resilient decentralized reinforcement learning (RL) approach for opportunistic FAMA (O-FAMA), to autonomously select robust users and the port of each chosen user’s FAS jointly to maximize the network sum-rate. In order to enhance learning efficiency in this multi-agent environment, we propose a novel team-theoretic RL framework that includes a derivative network guiding the multi-agent learning of each solution’s policy networks. Our simulation results confirm the effectiveness of the proposed methodology.

In this paper, we consider a non-orthogonal multiple access (NOMA)-enabled satellite-aerial-terrestrial network, where a batch of unmanned-aerial-vehicles (UAVS) act as decode-and-forward (DF) relays to simultaneously serve ground user equipments (UEs). The UAVs employ joint-transmission coordinated multi-point (JT-CoMP) to cooperatively provide coverage to a cluster of UEs utilizing the same resource block (RB), in a low signal-to-interference-plus-noise-ratio (SINR) setting. Our main objective is to increase the quality-of-service (QoS) of the UEs by maximizing the system sum-rate, which is accomplished by presenting a relay selection and a power allocation scheme, under limited available transmission power at the satellite and UAVs, QoS of UEs, and decoding order of UEs constraints. We first present an optimal UAV relays selection scheme which selects a group of UAVs satisfying the rate and decoding order constraints, and then sequentially solve the power allocation optimization problem for the two stages of transmission using Lagrange multipliers method and Karush-Kuhn-Tucker (KKT) conditions. Simulation results prove the effectiveness of our proposed system in successfully increasing the sum-rate of the network compared to baseline schemes, hence amplifying spectral efficiency.

As important services of the future sixth-generation (6G) wireless networks, vehicular communication and mobile edge computing (MEC) have received considerable interest in recent years for their significant potential applications in intelligent transportation systems. However, MEC-enabled vehicular networks depend heavily on network access and communication infrastructure, often unavailable in remote areas, making computation offloading susceptible to breaking down. To address this issue, we propose an MEC-enabled vehicular network assisted through aerial-terrestrial connectivity to provide network access and high data-rate entertainment services to a vehicular network. We present a time-varying, dynamic system model where high altitude platforms (HAPs) equipped with MEC servers, connected to a backhaul system of low-earth orbit (LEO) satellites, are used to provide computation offloading capability to the vehicles, as well as to provide network access for vehicle-to-vehicle (V2V) communications. Our main objective is to minimize the total computation and communication overhead of the joint computation offloading and resource allocation strategies for the system of vehicles. Since our formulated optimization problem is a mixed-integer non-linear programming (MINLP) problem, which is NP-hard, we propose a decentralized value-iteration-based reinforcement learning (RL) approach as a solution. In our Q-learning-assisted analysis, each vehicle acts as an intelligent agent to form optimal strategies for offloading and resource allocation. We further extend our solution to deep Q-learning (DQL) and double deep Q-learning to overcome the issues of dimensionality and the over-estimation of the value functions, as in Q-learning. Simulation results prove the effectiveness of our solution in successfully reducing system costs compared to baseline schemes.