Wideband near-field communication (NFC) systems are subject to the spatial-wideband effect arising from frequency-dependent array responses, leading to array-gain loss and inter-user interference leakage. To address this challenge, we propose a rate-splitting multiple access (RSMA)-enabled NFC transmit scheme that integrates true-time-delay (TTD)–based hybrid beamfocusing. RSMA enables flexible inter-user interference management, while TTD-based architectures effectively mitigate spatial-wideband effect and significantly reduce radio frequency chain requirements. As a proxy for the performance degradation induced by the spatial-wideband effect, we adopt the minimum user rate as the optimization metric. Specifically, we aim to maximize the minimum rate by jointly optimizing frequency-dependent analog beamfocusing, digital beamfocusing, and common rate allocation. The resulting problem is highly nonconvex. To solve it efficiently, we develop a penalty-based iterative algorithm that partitions the design variables into three blocks and applies block coordinate descent (BCD) to optimize each block in an alternating manner. The proposed framework is further extended to accommodate sub-connected TTD-based hybrid architectures. Comprehensive simulation results demonstrate that the proposed scheme: (i) effectively compensates for the spatial-wideband effect, addressing a critical wideband NFC bottleneck; (ii) achieves performance close to that of full-digital beamfocusing with substantially lower hardware complexity; and (iii) delivers significant performance gains compared to existing benchmark schemes.

In this paper, we propose a super-resolution wideband beam training method for near-field communications, which is able to achieve ultra-low overhead. To this end, we first study the multi-beam characteristic of a sparse uniform linear array (S-ULA) in the wideband. Interestingly, we show that this leads to a new beam pattern property, called rainbow blocks, where the S-ULA generates multiple grating lobes and each grating lobe is further splitted into multiple versions in the wideband due to the well-known beam-split effect. As such, one directional beamformer based on S-ULA is capable of generating multiple rainbow blocks in the wideband, hence significantly extending the beam coverage. Then, by exploiting the beam-split effect in both the frequency and spatial domains, we propose a new three-stage wideband beam training method for extremely large-scale array (XL-array) systems. Specifically, we first sparsely activate a set of antennas at the central of the XL-array and judiciously design the time-delay (TD) parameters to estimate candidate user angles by comparing the received signal powers at the user over subcarriers. Next, to resolve the angular ambiguity introduced by the S-ULA, we activate all antennas in the central subarray and design an efficient subcarrier selection scheme to estimate the true user angle. In the third stage, we resolve the user range at the estimated user angle with high resolution by controlling the splitted beams over subcarriers to simultaneously cover the range domain. Finally, numerical results are provided to demonstrate the effectiveness of proposed wideband beam training scheme, which only needs three pilots in near-field beam training, while achieving near-optimal rate performance.

Integrated sensing and communication (ISAC) networks leverage extremely large-scale antenna arrays and high frequencies. This inevitably extends the Rayleigh distance, making near-field (NF) spherical wave propagation dominant. This unlocks numerous spatial degrees of freedom, raising the challenge of optimizing them for communication and sensing tradeoffs. To this end, we propose a rate-splitting multiple access (RSMA)-based NF-ISAC transmit scheme utilizing hybrid analog-digital antennas. RSMA enhances interference management, while a variable number of dedicated sensing beams adds beamforming flexibility. The objective is to maximize the minimum communication rate while ensuring multi-target sensing performance by jointly optimizing receive filters, analog and digital beamformers, common rate allocation, and the sensing beam count. To address uncertainty in sensing beam allocation, a rank-zero solution reconstruction method demonstrates that dedicated sensing beams are unnecessary for NF multi-target detection. A penalty dual decomposition (PDD)-based double-loop algorithm is introduced, employing weighted minimum mean-squared error (WMMSE) and quadratic transforms to reformulate communication and sensing rates. Simulations reveal that the proposed scheme: 1) achieves performance comparable to fully digital beamforming with fewer RF chains, 2) maintains NF multi-target detection without compromising communication rates, and 3) significantly outperforms conventional multiple access schemes and far-field ISAC systems.

Extremely large-scale arrays (XL-arrays) have emerged as one promising technology to improve the spectral efficiency and spatial resolution in future sixth generation (6G) wireless systems. The drastic increase in the number of antennas renders the communication users more likely to be located in the near-field region, which requires a more accurate spherical (instead of planar) wavefront propagation modeling. However, this also inevitably incurs unaffordable beam training overhead when performing a two-dimensional (2D) beam-search in both the angular and range domains. To address this issue, we first introduce in this paper a new sparse discrete Fourier transform (DFT) codebook, which exhibits the angular periodicity in the received beam pattern at the user. This thus motivates us to propose a three-phase beam training scheme. Specifically, in the first phase, we utilize the sparse DFT codebook for beam sweeping in an angular subspace and estimate candidate user angles according to the received beam pattern. Then, a central subarray is activated to scan specific candidate angles for resolving the issue of angular ambiguity for identifying the user angle. In the third phase, the polar-domain codebook is applied in the estimated angle to search the best effective user range. Finally, numerical results show that our proposed beam training scheme enabled by the sparse DFT codebook achieves 98.67% beam training overhead reduction as compared to the exhaustive-search scheme, yet without compromising rate performance in the high signal-to-ratio (SNR) regime.

The Internet of Medical Things (IoMT) and fog computing facilitate the shift from hospital-based medical examinations to real-time electronic healthcare. A novel transmit scheme for fog computing-enabled IoMT is proposed in this article to address real-time monitoring needs, utilizing uplink and downlink rate splitting (RS) techniques. Fog computing enables offloading partial computation tasks to the edge server while processing the remaining tasks locally to reduce computing time. Uplink and downlink RS techniques offer flexible co-channel interference management to minimize offloading and feedback durations. The primary objective is to minimize the overall time cost encompassing task offloading, data processing, and result feedback. For this purpose, decisions on task offloading, computing resource allocation, uplink beamforming, downlink beamforming, and common rate allocation are jointly designed. However, this approach leads to a nonconvex optimization problem. Several auxiliary variables are introduced to handle this, and accurate surrogates are constructed to smooth the logarithmic transmit rate. Additionally, closed-form expressions are derived for optimal computing resource allocation per user. Based on these formulations, computing resource allocation and energy consumption are transformed into a convex constraint set. Finally, an alternating optimization algorithm is developed to update auxiliary and intrinsic variables iteratively. Simulation results demonstrate the effectiveness of the proposed transmit scheme and algorithm, showing substantial improvements over several baseline methods.

We propose an uplink rate splitting (RS) scheme for real-time health monitoring in the Internet of Medical Things (IoMT). To minimize total time cost, we jointly optimize biosensor grouping (BG), decoding order, power allocation, receiver beamforming, and computation resources allocation under the constraints of the transmit power and computation resources. This process results in a discrete nonconvex problem, which we decouple into three independent subproblems: 1) reduce co-channel interference to ease the transmit time cost. We solve this with a low-complexity BG algorithm; 2) optimize decoding order, power allocation, and receiver beamforming to reduce the forwarding time cost. We thus develop an alternating optimization algorithm. Specifically, we propose a decoding order update algorithm to optimize ordering, which can converge to the global optimum. We construct accurate surrogates via a quadratic transform approach and use surrogate optimization to attack other variables; and 3) allocate computation resources to minimize the processing time cost. Here, we derive the optimal solution with closed-form expressions. Simulation results indicate that the proposed overall scheme and algorithms present significant performance gains over several existing benchmarks.