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Integrated sensing and communication (ISAC) has opened up numerous game-changing opportunities for realizing future wireless systems. In this paper, we propose an ISAC processing framework relying on millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. Specifically, we provide a compressed sampling (CS) perspective to facilitate ISAC processing, which can not only recover the highdimensional channel state information or/and radar imaging information, but also significantly reduce pilot overhead. First, an energy-efficient widely spaced array (WSA) architecture is tailored for the radar receiver, which enhances the angular resolution of radar sensing at the cost of angular ambiguity. Then, we propose an ISAC frame structure for time-varying ISAC systems considering different timescales. The pilot waveforms are judiciously designed by taking into account both CS theories and hardware constraints induced by hybrid beamforming (HBF) architecture. Next, we design the dedicated dictionary for WSA that serves as a building block for formulating the ISAC processing as sparse signal recovery problems. The orthogonal matching pursuit with support refinement (OMP-SR) algorithm is proposed to effectively solve the problems in the existence of the angular ambiguity. We also provide a framework for estimating the Doppler frequencies during payload data transmission to guarantee communication performances. Simulation results demonstrate the good performances of both communications and radar sensing under the proposed ISAC framework.

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Evolutionary computation, e.g., particle swarm optimization (PSO), has made impressive achievements in solving complex problems in science and industry. As an important open problem for more than 50 years, there is still no theoretical guarantee on the global optimum and the general reliability, due to lack of a unified representation of diverse problem structures and a generic mechanism to avoid local optimums. The long-standing pitfalls severely impair their trusted applications in a variety of problems. Here, we report a new evolutionary computation framework aided by machine learning, named EVOLER, which for the first time enables the theoretically guaranteed global optimization of complex nonconvex problems. This is achieved by: (1) learning a low-rank representation of problem with the limited samples, which helps to identify one attention subspace; and (2) exploring this small attention subspace via the evolutionary computation method, which allows to reliably avoid local optimums. As validated on 20 challenging benchmarks, it finds the global optimum with probability approaching 1; and moreover, it attains the best results in all cases, thus substantially extending the applicability in diverse problems. We use EVOLER to tackle 2 important problems, dispatch of power grid and inverse design of nano-photonics devices, whereby it consistently gains the optimal results that were rarely achieved by state-of-the-art methods. Our method takes a crucial step forward in globally guaranteed evolutionary computation, overcoming the uncertainty of data-driven black-box methods, offering broad prospects for tackling complex real-world problems.

Massive connectivity for extra large-scale multiinput multi-output (XL-MIMO) systems is a challenging issue due to the near-field access channels and the prohibitive cost. In this paper, we propose an uplink grant-free massive access scheme for XL-MIMO systems, in which a mixed-analog-todigital converters (ADC) architecture is adopted to strike the right balance between access performance and power consumption. By exploiting the spatial-domain structured sparsity and the piecewise angular-domain cluster sparsity of massive access channels, a compressive sensing (CS)-based two-stage orthogonal approximate message passing algorithm is proposed to efficiently solve the joint activity detection and channel estimation problem. Particularly, high-precision quantized measurements are leveraged to perform accurate hyper-parameter estimation, thereby facilitating the activity detection. Moreover, we adopt a subarraywise estimation strategy to overcome the severe angular-domain energy dispersion problem which is caused by the near-field effect in XL-MIMO channels. Simulation results verify the superiority of our proposed algorithm over state-of-the-art CS algorithms for massive access based on XL-MIMO with mixed-ADC architectures.