Deployment Optimization for XL-IRS Assisted Multi-User Communications

In this paper, we study the deployment optimization for an extremely large-scale intelligent reflecting surface (XL-IRS) assisted multi-user communication system, within which the channels between the XL-IRS and the BS (or user) are modeled by the near-field spherical wavefronts. To draw some valuable insights, we first consider the single-user case, where an alternating optimization (AO) based algorithm is devised to maximize the received signal-to-noise ratio (SNR) at the user. To address the high computational complexity issue incurred by the AO based algorithm, three approximate received SNR expressions are obtained to yield useful insights, corresponding to the upper bound, approximate expression, and closed-form. It is demonstrated that the XL-IRS ought to be positioned near the user (rather than the BS) to obtain a higher beamforming gain. Then, for the multi-user scenario, an efficient algorithm is proposed to obtain a high-quality XL-IRS placement solution by using the AO and successive convex approximation (SCA) techniques. Furthermore, the effective degree of freedom (DoF) of the BS-IRS channel is provided, which indicates that the additional effective DoF can be leveraged to improve multi-user spatial multiplexing. Last, numerical results confirm the existence of a trade-off between near-field beam-focusing gain and multiplexing gain.

Physical-Layer Security in Mixed Near-Field and Far-Field Communication Systems

Extremely large-scale arrays (XL-arrays) have emerged as a promising technology to improve the spectrum efficiency and spatial resolution of future wireless systems. Different from existing works that mostly considered physical layer security (PLS) in either the far-field or near-field, we consider in this paper a new and practical scenario, where legitimate users (Bobs) are located in the far-field of a base station (BS) while eavesdroppers (Eves) are located in the near-field for intercepting confidential information at short distance, referred to as the mixed near-field and far-field PLS. Specifically, we formulate an optimization problem to maximize the sum-secrecy-rate of all Bobs by optimizing the power allocation of the BS, subject to the constraint on the total BS transmit power. To shed useful insights, we first consider a one-Bob-one-Eve system and characterize the insecure-transmission region of the Bob in closed form. Interestingly, we show that the insecure-transmission region is significantly \emph{expanded} as compared to that in conventional far-field PLS systems, due to the energy-spread effect in the mixed-field scenario. Then, we further extend the analysis to a two-Bob-one-Eve system. It is revealed that as compared to the one-Bob system, the interferences from the other Bob can be effectively used to weaken the capability of Eve for intercepting signals of target Bobs, thus leading to enhanced secrecy rates. Furthermore, we propose an efficient algorithm to obtain a high-quality solution to the formulated non-convex problem by leveraging the successive convex approximation (SCA) technique. Finally, numerical results demonstrate that our proposed algorithm achieves a higher sum-secrecy-rate than the benchmark scheme where the power allocation is designed based on the (simplified) far-field channel model.

A Tutorial on MIMO-OFDM ISAC: From Far-Field to Near-Field

Integrated sensing and communication (ISAC) is one of the key usage scenarios for future sixth-generation (6G) mobile communication networks, where communication and sensing (C&S) services are simultaneously provided through shared wireless spectrum, signal processing modules, hardware, and network infrastructure. Such an integration is strengthened by the technology trends in 6G, such as denser network nodes, larger antenna arrays, wider bandwidths, higher frequency bands, and more efficient utilization of spectrum and hardware resources, which incentivize and empower enhanced sensing capabilities. As the dominant waveform used in contemporary communication systems, orthogonal frequency division multiplexing (OFDM) is still expected to be a very competitive technology for 6G, rendering it necessary to thoroughly investigate the potential and challenges of OFDM ISAC. Thus, this paper aims to provide a comprehensive tutorial overview of ISAC systems enabled by large-scale multi-input multi-output (MIMO) and OFDM technologies and to discuss their fundamental principles, advantages, and enabling signal processing methods. To this end, a unified MIMO-OFDM ISAC system model is first introduced, followed by four frameworks for estimating parameters across the spatial, delay, and Doppler domains, including parallel one-domain, sequential one-domain, joint two-domain, and joint three-domain parameter estimation. Next, sensing algorithms and performance analyses are presented in detail for far-field scenarios where uniform plane wave (UPW) propagation is valid, followed by their extensions to near-field scenarios where uniform spherical wave (USW) characteristics need to be considered. Finally, this paper points out open challenges and outlines promising avenues for future research on MIMO-OFDM ISAC.

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.

Finite-Precision Conjugate Gradient Method for Massive MIMO Detection

The implementation of the conjugate gradient (CG) method for massive MIMO detection is computationally challenging, especially for a large number of users and correlated channels. In this paper, we propose a low computational complexity CG detection from a finite-precision perspective. First, we develop a finite-precision CG (FP-CG) detection to mitigate the computational bottleneck of each CG iteration and provide the attainable accuracy, convergence, and computational complexity analysis to reveal the impact of finite-precision arithmetic. A practical heuristic is presented to select suitable precisions. Then, to further reduce the number of iterations, we propose a joint finite-precision and block-Jacobi preconditioned CG (FP-BJ-CG) detection. The corresponding performance analysis is also provided. Finally, simulation results validate the theoretical insights and demonstrate the superiority of the proposed detection.

Fair Resource Allocation in UAV-based Semantic Communication System with Fluid Antenna

In this paper, the problem of maximization of the minimum equivalent rate in a unmanned-aerial-vehicle (UAV)-based multi-user semantic communication system is investigated. In the considered model, a multi-antenna UAV employs semantic extraction techniques to compress the data ready to be sent to the users, which are equipped with fluid antennas. Our aim is to jointly optimize the trajectory of the UAV, the transmit beamforming and the semantic compression rate at the UAV, as well as the selection of activated ports in fluid antenna system (FAS), to maximize the minimum equivalent transmission rate among all user. An alternating algorithm is designed to solve the problem. Simulation results validate the effectiveness of the proposed algorithm.

Rotatable Antennas for Integrated Sensing and Communications

In this letter, we propose to deploy rotatable antennas (RAs) at the base station (BS) to enhance both communication and sensing (C&S) performances, by exploiting a new spatial degree-of-freedom (DoF) offered by array rotation. Specifically, we formulate a multi-objective optimization problem to simultaneously maximize the sum-rate of multiple communication users and minimize the Cram\'er-Rao bound (CRB) for target angle estimation, by jointly optimizing the transmit beamforming vectors and the array rotation angle at the BS. To solve this problem, we first equivalently decompose it into two subproblems, corresponding to an inner problem for beamforming optimization and an outer problem for array rotation optimization. Although these two subproblems are non-convex, we obtain their high-quality solutions by applying the block coordinate descent (BCD) technique and one-dimensional exhaustive search, respectively. Moreover, we show that for the communication-only case, RAs provide an additional rotation gain to improve communication performance; while for the sensing-only case, the equivalent spatial aperture can be enlarged by RAs for achieving higher sensing accuracy. Finally, numerical results are presented to showcase the performance gains of RAs over fixed-rotation antennas in integrated sensing and communications (ISAC).

Mixed Near-field and Far-field Target Localization for Low-altitude Economy

In this paper, we study efficient mixed near-field and far-field target localization methods for low-altitude economy, by capitalizing on extremely large-scale multiple-input multiple-output (XL-MIMO) communication systems. Compared with existing works, we address three new challenges in localization, arising from 1) half-wavelength antenna spacing constraint, 2) hybrid uniform planar array (UPA) architecture, and 3) incorrect mixed-field target classification for near-field targets.To address these issues, we propose a new three-step mixed-field localization method.First, we reconstruct the signals received at UPA antennas by judiciously designing analog combining matrices over time with minimum recovery errors, thus tackling the reduced-dimensional signal-space issue in hybrid arrays.Second, based on recovered signals, we devise a modified MUSIC algorithm (catered to UPA architecture) to estimate 2D angular parameters of both far- and near-field targets. Due to half-wavelength inter-antenna spacing, there exist ambiguous angles when estimating true angles of targets.In the third step, we design an effective classification method to distinguish mixed-field targets, determine true angles of all targets, as well as estimate the ranges of near-field targets. In particular, angular ambiguity is resolved by showing an important fact that the three types of estimated angles (i.e., far-field, near-field, and ambiguous angles) exhibit significantly different patterns in the range-domain MUSIC spectrum. Furthermore, to characterize the estimation error lower-bound, we obtain a matrix closed-form Cram\'er-Rao bounds for mixed-field target localization. Finally, numerical results demonstrate the effectiveness of our proposed mixed-field localization method, which improves target-classification accuracy and achieves a lower root mean square error than various benchmark schemes.

Communication Efficient Cooperative Edge AI via Event-Triggered Computation Offloading

Rare events, despite their infrequency, often carry critical information and require immediate attentions in mission-critical applications such as autonomous driving, healthcare, and industrial automation. The data-intensive nature of these tasks and their need for prompt responses, combined with designing edge AI (or edge inference), pose significant challenges in systems and techniques. Existing edge inference approaches often suffer from communication bottlenecks due to high-dimensional data transmission and fail to provide timely responses to rare events, limiting their effectiveness for mission-critical applications in the sixth-generation (6G) mobile networks. To overcome these challenges, we propose a channel-adaptive, event-triggered edge-inference framework that prioritizes efficient rare-event processing. Central to this framework is a dual-threshold, multi-exit architecture, which enables early local inference for rare events detected locally while offloading more complex rare events to edge servers for detailed classification. To further enhance the system's performance, we developed a channel-adaptive offloading policy paired with an online algorithm to dynamically determine the optimal confidence thresholds for controlling offloading decisions. The associated optimization problem is solved by reformulating the original non-convex function into an equivalent strongly convex one. Using deep neural network classifiers and real medical datasets, our experiments demonstrate that the proposed framework not only achieves superior rare-event classification accuracy, but also effectively reduces communication overhead, as opposed to existing edge-inference approaches.

Federated Dropout: Convergence Analysis and Resource Allocation

Federated Dropout is an efficient technique to overcome both communication and computation bottlenecks for deploying federated learning at the network edge. In each training round, an edge device only needs to update and transmit a sub-model, which is generated by the typical method of dropout in deep learning, and thus effectively reduces the per-round latency. \textcolor{blue}{However, the theoretical convergence analysis for Federated Dropout is still lacking in the literature, particularly regarding the quantitative influence of dropout rate on convergence}. To address this issue, by using the Taylor expansion method, we mathematically show that the gradient variance increases with a scaling factor of $\gamma/(1-\gamma)$, with $\gamma \in [0, \theta)$ denoting the dropout rate and $\theta$ being the maximum dropout rate ensuring the loss function reduction. Based on the above approximation, we provide the convergence analysis for Federated Dropout. Specifically, it is shown that a larger dropout rate of each device leads to a slower convergence rate. This provides a theoretical foundation for reducing the convergence latency by making a tradeoff between the per-round latency and the overall rounds till convergence. Moreover, a low-complexity algorithm is proposed to jointly optimize the dropout rate and the bandwidth allocation for minimizing the loss function in all rounds under a given per-round latency and limited network resources. Finally, numerical results are provided to verify the effectiveness of the proposed algorithm.