Ross D. Murch

The fluid antenna system (FAS) concept represents shape-flexible and position-flexible antenna technologies designed to enhance wireless communication applications. In this paper, we apply this concept to reconfigurable intelligent surfaces (RISs), introducing fluid RIS (FRIS), where each tunably reflecting element becomes a fluid element with additional position reconfigurability. This new paradigm is referred to as fluid RIS (FRIS). We investigate an FRIS-programmable wireless channel, in which the fluid metasurface is divided into non-overlapping subareas, each acting as a fluid element that can dynamically adjust both its position and phase shift of the reflected signal. We first analyze the single-user, single-input single-output (SU-SISO) channel, in which a single-antenna transmitter communicates with a single-antenna receiver via an FRIS. The achievable rate is then maximized by optimizing the fluid elements using a particle swarm optimization (PSO)-based approach. Next, we extend our analysis to the multi-user, multiple-input single-output (MU-MISO) case, where a multi-antenna base station (BS) transmits individual data streams to multiple single-antenna users via an FRIS. In this case, the joint optimization of the positions and phase shifts of the FRIS element, as well as the BS precoding to maximize the sum-rate is studied. To solve the problem, a combination of techniques including PSO, semi-definite relaxation (SDR), and minimum mean square error (MMSE) is proposed. Numerical results demonstrate that the proposed FRIS approach significantly outperforms conventional RIS configurations in terms of achievable rate performance.

Future wireless networks must deliver unprecedented spectral efficiency to accommodate massive numbers of interconnected devices; yet today’s multiple-access schemes remain fundamentally constrained by interference. Fluid antenna multiple access (FAMA) has emerged as a promising paradigm that tackles this bottleneck by introducing a new degree of freedom (DoF): receiver-side spatial fluidity enabled by fluid antenna systems (FAS). By dynamically repositioning the FAS aperture to exploit fine-grained channel variations, FAMA can mitigate inter-user interference and enable massive multiple access on the same time-frequency resource without precoding and any other interference suppression techniques. This paper presents a comprehensive survey of recent advances in FAMA. We first summarize the operating principles of FAS and then formalize FAMA, including representative network and channel models. We systematically categorize and review FAMA architectures—fast FAMA, slow FAMA, compact ultra-massive antenna arrays (CUMA), coded FAMA, and turbo FAMA—highlighting their design rationales, performance benefits, and applicability regimes. We further discuss practical considerations such as channel acquisition and tracking, hardware constraints, switching/positioning overhead, and integration with existing air interfaces. Finally, we outline open challenges and future research directions, including scalable resource allocation, robustness under mobility and correlation, protocol/standardization aspects, and cross-layer co-design. Overall, this survey provides a unified framework to understand FAMA’s potential and to guide both academic research and industrial innovation in this rapidly evolving area.

Fluid antenna multiple access (FAMA) has recently emerged as a user-centric approach to massive connectivity. By exposing many reconfigurable “ports” on an antenna aperture, they exploit local spatial fading fluctuations at each user terminal (UT) to mitigate multiuser interference (MUI) without the need for precoding. While both turbo and fast FAMA promise ultra-massive access, its practicality is limited by the need to observe, at symbol rate, the complete set of per-port channel gains and received signals. This letter addresses this bottleneck with a novel plug-and-play attentional-copula extrapolator that reconstructs the joint per-symbol field of channel gains and received signals from a small subset of ports. The module couples monotone normalizing-flow marginals with a Transformer-based copula to capture both intra-port (channel-signal) and inter-port (spatial) dependencies, yielding drop-in estimates for existing turbo FAMA pipelines without retraining. Simulation results reveal that even with an extremely small port subset, the normalized mean-square error (NMSE) rapidly converges and the symbol error rate (SER) after turbo FAMA remains within a few percent of the full-channel state information (CSI) oracle across aperture sizes. By replacing exhaustive sampling with accurate learned extrapolation, the proposed scheme preserves turbo FAMA’s scalability while unlocking practical, low-overhead deployment—a viable pathway to extreme massive access.

An antenna coding approach for exploitingthe spatial multiplexing capability of pixel antennas is proposed. This approach can leverage additional degrees of freedom in the beamspace domain to transmit more information streams. Pixel antennas are a general reconfigurable antenna design where a radiating structure with arbitrary shape and size can be discretized into sub-wavelength elements called pixels. The adjacent pixels are connected by radio frequency switches. By controlling the switch states, the pixel antenna topology can be flexibly adjusted so that the resulting radiation pattern can be reconfigured for beamspace spatial multiplexing. In this work, we introduce the antenna coder and pattern coder for pixel antennas, provide a multiple-input multiple-output (MIMO) communication system model with antenna coding in the beamspace domain, and derive the spectral efficiency. Utilizing the antenna coder, the radiation pattern of the pixel antenna is analyzed and efficient optimization algorithms are provided for antenna coding design. Numerical simulation results show that the proposed technique using pixel antennas can enhance spectral efficiency of 4-by-4 MIMO by up to 12 bits/s/Hz, or equivalently reduce the required transmit power by up to 90%, when compared to conventional MIMO, demonstrating the effectiveness of the antenna coding technique in spectral efficiency enhancement and its promise for future sixth generation (6 G) wireless communication.

For a single-antenna fluid antenna system (FAS) with N fixed antenna locations or ports, analytical expressions for the error performance of four types of signaling schemes for digitally modulated data transmission and reception are found. The signaling schemes are: (1) M-ary phase-shift keying with coherent reception, (2) two-sided M-ary amplitude-shift keying (M-ASK) with coherent reception, (3) one-sided M-ASK with noncoherent reception, and (4) one-sided M-ASK with partially noncoherent reception. The N ports are evenly distributed on a line. The FAS error performance is analyzed when the ports are subject to identically distributed Rayleigh fading with uniform correlation between the channels at the ports. A series expression for the characteristic function (c.f.) of the random complex channel coefficient magnitude squared of the chosen FAS port, in terms of a series of elementary c.f.s, where each elementary c.f. is the c.f. of a sum of N independent gamma random variables, is obtained. Series expressions for the symbol error probabilities as weighted sums of functions in closed form are presented. Closed form asymptotic high signal-to-noise ratio results for error performance are also derived. Numerical are presented to show the variation of the error performance with respect to the spacing between the first and Nth ports and N.

Pixel-based reconfigurable intelligent surfaces (RISs) employ a novel design to achieve high reflection gain at a lower hardware cost by eliminating the phase shifters used in traditional RIS. However, this design presents challenges for channel estimation and passive beamforming due to its non-separable state response, rendering existing solutions ineffective. To address this, we first approximate the non-separable RIS response functions using a kernel-based method and a deep neural network, achieving high accuracy while reducing computational and memory complexity. Next, we propose a simplified cascaded channel model that focuses on dominated scattering paths with limited unknown parameters, along with customized algorithms to estimate short-term and long-term parameters separately. Finally, we introduce a low-complexity passive beamforming algorithm to configure the discrete RIS state vector, maximizing the achievable rate. Our simulation results demonstrate that the proposed solution significantly outperforms various baselines across a wide SNR range.