Extremely large antenna arrays (ELAAs) operating in high-frequency bands have spurred the development of near-field communication, driving advancements in beam training and signal processing design. In this work, we present a low-complexity near-field beam training scheme that fully utilize the conventional discrete Fourier transform (DFT) codebook designed for far-field users. We begin by analyzing the received beam pattern in the near field and derive closed-form expressions for the beam width and central gain. These analytical results enable the definition of an angle-dependent, modified Rayleigh distance, which effectively distinguishes near-field and far-field user regimes. Building on the analysis, we develop a direct and computationally efficient method to estimate user distance, with a complexity ofO(1), and further improve its accuracy through a simple refinement. Simulation results demonstrate significant gains in both single- and multi-user settings, with up to 2.38 dB SNR improvement over exhaustive search. To further enhance estimation accuracy, we additionally propose a maximum likelihood estimation (MLE) based refinement method, leveraging the Rician distribution of signal amplitudes and achieving accuracy close to the Cramér–Rao bound (CRB). Simulation shows the single-user and multi-user achievable rates can both approach those obtained with ideal channel state information.

This paper proposes an adaptive near-field beam training method to enhance performance in multi-user and multipath environments. The approach identifies multiple strongest beams through beam sweeping and linearly combines their received signals—capturing both amplitude and phase—for improved channel estimation. Two codebooks are considered: the conventional DFT codebook and a near-field codebook that samples both angular and distance domains. As the near-field basis functions are generally non-orthogonal and often over-complete, we exploit sparsity in the solution using LASSO-based linear regression, which can also suppress noise. Simulation results show that the near-field codebook reduces feedback overhead by up to 95% compared to the DFT codebook. The proposed LASSO regression method also maintains robustness under varying noise levels, particularly in low SNR regions. Furthermore, an off-grid refinement scheme is introduced to enhance accuracy especially when the codebook sampling is coarse, improving reconstruction accuracy by 69.4%.

Determining which users to simultaneously schedule on the uplink and the downlink in a full-duplex (FD) system is crucial to control the inter-user interference between them and achieve a high spectral efficiency. The user-pair scheduling algorithm and the efficacy of FD is, thus, closely linked to the availability of channel state information and the feedback scheme that conveys it to the base station (BS). We consider a reduced feedback scheme in which a user that is scheduled feeds back only a limited number of quantized inter-user interferences that are below a pre-specified threshold. For it, we propose a novel user-pair scheduling and mode selection algorithm (UPSMA). We analyze the uplink and downlink spectral efficiencies of UPSMA for two channel models that highlight the different influences of small-scale fading and large-scale shadowing. We derive insightful asymptotic scaling laws that quantify the dependence of the threshold and the uplink and downlink spectral efficiencies on the number of users. UPSMA with limited feedback achieves a higher sum spectral efficiency than a half-duplex system and conventional FD resource allocation algorithms. Its performance is close to the exhaustive search algorithm in which the BS knows all the inter-user interferences.

In a full-duplex (FD) system, the base station (BS) needs to carefully schedule the uplink and downlink users that transmit simultaneously to control the inter-user interference. The efficacy of scheduling is closely tied to the availability of channel state information and the feedback scheme that conveys it to the BS. We propose a novel user-pair scheduling and mode selection algorithm (UPSMA) and a reduced feedback scheme, in which a user feeds back only a limited number of inter-user interferences that are below a pre-specified threshold. We derive expressions for the uplink and downlink rates of UPSMA in the presence of small-scale fading, large-scale shadowing, and pathloss. These lead to novel scaling laws for the threshold and the uplink and downlink rates. Even with limited feedback, UPSMA achieves a higher sum rate compared to a half-duplex system and conventional FD resource allocation algorithms.

The asymmetric network model, in which the access point (AP) is full-duplex (FD) capable while the other nodes are only half-duplex (HD) capable, is motivated by early-stage deployments of FD-capable networks. We propose a medium access control (MAC) protocol called asymmetric FD MAC (AFD-MAC) for this model. It leverages features such as random back-off and carrier sensing of the widely-used 802.11 HD MAC protocol, and it introduces two signals to exploit the FD capability of the AP. We also develop a general, network topology-specific renewal-theoretic analysis that characterizes the saturation throughput of AFD-MAC. It captures the differences in the statistical properties of the nodes and the AP due to differences in their duplexing capabilities or the number of hidden nodes. AFD-MAC can increase the throughput by a factor as large as two and can reduce head-of-line delay by a factor more than half compared to the conventional 802.11 HD MAC protocol. The gains depend on the ratio of the uplink to downlink packet lengths, network topology, and the extent of self-interference cancellation. A contrarian insight that emerges is that hidden nodes can enable the network to exploit its asymmetric FD capability.