Extremely large-scale array (XL-array) has emerged as a promising technology to improve the spectrum efficiency and spatial resolution of future wireless systems. However, the huge number of antennas renders the users more likely to locate in the near-field (instead of the far-field) region of the XL-array with spherical wavefront propagation. This inevitably incurs prohibitively high beam training overhead since it requires a two-dimensional (2D) beam search over both the angular and distance domains. To address this issue, we propose in this paper an efficient two-stage hierarchical beam training method for near-field communications. Specifically, in the first stage, we employ the central sub-array of the XL-array to search for a coarse user direction in the angular domain with conventional far-field hierarchical codebook. Then, in the second stage, given the coarse user direction, we progressively search for the fine-grained user direction-and-distance in the polar domain with a dedicatedly designed codebook. Numerical results show that our proposed two-stage hierarchical beam training method can achieve over 99% training overhead reduction as compared to the 2D exhaustive search, yet achieving comparable rate performance.

Extremely large-scale arrays (XL-arrays) have emerged as a promising technology to enable near-field communications for achieving enhanced spectrum efficiency and spatial resolution, by drastically increasing the number of antennas. However, this also inevitably incurs higher hardware and energy cost, which may not be affordable in future wireless systems. To address this issue, we propose in this paper two types of sparse arrays (SAs) for enabling near-field communications. Specifically, we first consider the linear sparse array (LSA) and characterize its near-field beam pattern. It is shown that LSAs can achieve the near-field beam-focusing gain with lower hardware cost and energy consumption, while it introduces several undesired grating-lobes, which are focused on specific regions exhibiting comparable beam power with the main-lobe. An efficient hybrid beamforming design is then proposed for the LSA to deal with the potential strong inter-user interference (IUI). Next, we further consider another form of SA, called extended coprime array (ECA), which is composed of two LSA subarrays with different (coprime) inter-antenna spacing. By characterizing the ECA near-field beam pattern, we show that compared with the LSA of the same array sparsity, ECAs can greatly suppress the beam power of near-field grating-lobes thanks to the offset effect of the two subarrays, albeit generating more low-power grating-lobes. This thus motivates us to propose a customized two-phase hybrid beamforming design for ECAs. Finally, numerical results are presented to demonstrate the energy-efficiency gain of the proposed two SAs over dense uniform linear arrays.

As one of the most critical technology in array signal processing, direction of arrival (DoA) estimation has received a great deal of attention in many areas. Traditional methods perform well when the signal-to-noise ratio (SNR) is high and the receiving array is perfect, which are quite different from the situation in some real applications (e.g., the marine communication scenario). To get satisfying performance of DoA estimation when SNR is low and the array is inaccurate (mutual coupling exist), this paper introduces a scheme consisting of denoising autoencoder (DAE) and deep neural networks (DNN), referred to as DAE-DNN scheme. DAE is used to reconstruct a clean “repaired” input from its corrupted version to increase the robustness, and then divide the input into multiple parts in different sub-areas. DNN is used to learn the mapping between the received signals and the refined grids of angle in each sub-areas, then the outputs of each sub-areas are concatenated to perform the final DoA estimation. By simulations in different SNR regimes, we study the performance of DAE-DNN in terms of the different snapshots, batch size, learning rate, and epoch. Our results demonstrate that the proposed DAE-DNN scheme outperforms traditional methods in accuracy and robustness.

This paper investigates the resource allocation and computation offloading problem for multi-access edge computing (MEC) systems, where multiple mobile users (MUs) equipped with multiple antennas access the base station in a non-orthogonal multiple access manner. We jointly optimize the offloading ratio, computational frequency and transmit precoding matrix of each MU to minimize the total energy consumption of all MUs while satisfying the latency constraints. The problem is formulated as a non-convex optimization problem and a two-layer iterative method is proposed to solve the problem efficiently with low complexity. Specifically, we first decompose the original problem into several subproblems, and then sequentially solve these subproblems in an alternative fashion. Furthermore, we also discuss the optimal decoding order of MUs under two different scenarios. Firstly, when the MUs’ channel conditions are similar, by deriving closed-form expressions for energy consumptions of all MUs, we prove that the optimal decoding order is only determined by the latency requirements. On the other hand, when the MUs’ channel conditions are different, we show that the optimal decoding order is determined by both the channel conditions and the latency requirements. As such, we propose a metric aiming to balance the effects of channel conditions and latency requirements on the MUs’ decoding order. Simulation results validate the convergence of the proposed method and demonstrate its superiority over benchmark algorithms.

AbstractAs a potential technology in weak signal detection (WSD), the chaos theory benefits from its characteristics of the sensitivity to the initial condition and the immunity to the Additive White Gaussian Noise (AWGN). Traditional methods based on chaos theory perform well on single non-variable frequency signal detection (SNVFSD), which is a common situation in texture flaw detection while it is a rare scenario in the communication field. In this paper, we give a comprehensive analysis of the chaos-based approach for weak signal frequency detection. To better understand the advantages and limitations of the chaos theory, we present the research results for several typical communication scenarios. Specifically, we proposed several chaos-based approaches to get a satisfying performance in the following situations: single variable-frequency signal detection (SVFSD), multiple non-variable-frequency signals detection (MNVFSD), multiple variable-frequency signals detection (MVFSD). To enhance the generalization performance to real application, the critical states characteristics, Lyapunov Exponents (LE) and Melnikov method are analyzed to employed in these proposed chaos-based approaches. By theoretical analysis and numerical simulations, we study the performance of chaos-based detection systems in terms of SVFSD, MNVFSD, MVFSD. Our results show that these proposed schemes can obtain a satisfying performance in terms of accuracy and robustness, and the extensive simulations demonstrate their effectiveness.

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.