Super-resolution Wideband Beam Training for Near-field Communications with Ultra-low Overhead

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.

Hybrid Beamforming Design for RSMA-enabled Near-Field Integrated Sensing and Communications

To enable high data rates and sensing resolutions, integrated sensing and communication (ISAC) networks leverage extremely large antenna arrays and high frequencies, extending the Rayleigh distance and 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 digital-analog 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 space division multiple access (SDMA) and far-field ISAC systems.

Flexible Rate-Splitting Multiple Access for Near-Field Integrated Sensing and Communications

This letter presents a flexible rate-splitting multiple access (RSMA) framework for near-field (NF) integrated sensing and communications (ISAC). The spatial beams configured to meet the communication rate requirements of NF users are simultaneously leveraged to sense an additional NF target. A key innovation lies in its flexibility to select a subset of users for decoding the common stream, enhancing interference management and system performance. The system is designed by minimizing the Cram\'{e}r-Rao bound (CRB) for joint distance and angle estimation through optimized power allocation, common rate allocation, and user selection. This leads to a discrete, non-convex optimization problem. Remarkably, we demonstrate that the preconfigured beams are sufficient for target sensing, eliminating the need for additional probing signals. To solve the optimization problem, an iterative algorithm is proposed combining the quadratic transform and simulated annealing. Simulation results indicate that the proposed scheme significantly outperforms conventional RSMA and space division multiple access (SDMA), reducing distance and angle estimation errors by approximately 100\% and 20\%, respectively.

Joint Beam Scheduling and Resource Allocation for Flexible RSMA-aided Near-Field Communications

Supporting immense throughput and ubiquitous connectivity holds paramount importance for future wireless networks. To this end, this letter focuses on how the spatial beams configured for legacy near-field (NF) users can be leveraged to serve extra NF or far-field users while ensuring the rate requirements of legacy NF users. In particular, a flexible rate splitting multiple access (RSMA) scheme is proposed to efficiently manage interference, which carefully selects a subset of legacy users to decode the common stream. Beam scheduling, power allocation, common rate allocation, and user selection are jointly optimized to maximize the sum rate of additional users. To solve the formulated discrete non-convex problem, it is split into three subproblems. The accelerated bisection searching, quadratic transform, and simulated annealing approaches are developed to attack them. Simulation results reveal that the proposed transmit scheme and algorithm achieve significant gains over three competing benchmarks.