Zero-Forget Preservation of Semantic Communication Alignment in Distributed AI Networks

Future communication networks are expected to connect massive distributed artificial intelligence (AI). Exploiting aligned priori knowledge of AI pairs, it is promising to convert high-dimensional data transmission into highly-compressed semantic communications (SC). However, to accommodate the local data distribution and user preferences, AIs generally adapt to different domains, which fundamentally distorts the SC alignment. In this paper, we propose a zero-forget domain adaptation (ZFDA) framework to preserve SC alignment. To prevent the DA from changing substantial neural parameters of AI, we design sparse additive modifications (SAM) to the parameters, which can be efficiently stored and switched-off to restore the SC alignment. To optimize the SAM, we decouple it into tractable continuous variables and a binary mask, and then handle the binary mask by a score-based optimization. Experimental evaluations on a SC system for image transmissions validate that the proposed framework perfectly preserves the SC alignment with almost no loss of DA performance, even improved in some cases, at a cost of less than 1% of additional memory.

Learning Rate-Compatible Linear Block Codes: An Auto-Encoder Based Approach

Artificial intelligence (AI) provides an alternative way to design channel coding with affordable complexity. However, most existing studies can only learn codes for a given size and rate, typically defined by a fixed network architecture and a set of parameters. The support of multiple code rates is essential for conserving bandwidth under varying channel conditions while it is costly to store multiple AI models or parameter sets. In this article, we propose an auto-encoder (AE) based rate-compatible linear block codes (RC-LBCs). The coding process associated with AI or non-AI decoders and multiple puncturing patterns is optimized in a data-driven manner. The superior performance of the proposed AI-based RC-LBC is demonstrated through our numerical experiments.

Beam Switching Based Beam Design for High-Speed Train mmWave Communications

For high-speed train (HST) millimeter wave (mmWave) communications, the use of narrow beams with small beam coverage needs frequent beam switching, while wider beams with small beam gain leads to weaker mmWave signal strength. In this paper, we consider beam switching based beam design, which is formulated as an optimization problem aiming to minimize the number of switched beams within a predetermined railway range subject to that the receiving signal-to-noise ratio (RSNR) at the HST is no lower than a predetermined threshold. To solve this problem, we propose two sequential beam design schemes, both including two alternately-performed stages. In the first stage, given an updated beam coverage according to the railway range, we transform the problem into a feasibility problem and further convert it into a min-max optimization problem by relaxing the RSNR constraints into a penalty of the objective function. In the second stage, we evaluate the feasibility of the beamformer obtained from solving the min-max problem and determine the beam coverage accordingly. Simulation results show that compared to the first scheme, the second scheme can achieve 96.20\% reduction in computational complexity at the cost of only 0.0657\% performance degradation.

Robust Beamforming with Application in High-Resolution Sensing

As a fundamental technique in array signal processing, beamforming plays a crucial role in amplifying signals of interest while mitigating interference and noise. When uncertainties exist in the signal model or the data size of snapshots is limited, the performance of beamformers significantly degrades. In this article, we comprehensively study the conceptual system, theoretical analysis, and algorithmic design for robust beamforming. Particularly, four technical approaches for robust beamforming are discussed, including locally robust beamforming, globally robust beamforming, regularized beamforming, and Bayesian-nonparametric beamforming. In addition, we investigate the equivalence among the methods and suggest a unified robust beamforming framework. As an application example, we show that the resolution of robust beamformers for direction-of-arrival (DoA) estimation can be greatly refined by incorporating the characteristics of subspace methods.

Near-Field Communications for Extremely Large-Scale MIMO: A Beamspace Perspective

Extremely large-scale multiple-input multiple-output (XL-MIMO) is regarded as one of the key techniques to enhance the performance of future wireless communications. Different from regular MIMO, the XL-MIMO shifts part of the communication region from the far field to the near field, where the spherical-wave channel model cannot be accurately approximated by the commonly-adopted planar-wave channel model. As a result, the well-explored far-field beamspace is unsuitable for near-field communications, thereby requiring the exploration of specialized near-field beamspace. In this article, we investigate the near-field communications for XL-MIMO from the perspective of beamspace. Given the spherical wavefront characteristics of the near-field channels, we first map the antenna space to the near-field beamspace with the fractional Fourier transform. Then, we divide the near-field beamspace into three parts, including high mainlobe, low mainlobe, and sidelobe, and provide a comprehensive analysis of these components. Based on the analysis, we demonstrate the advantages of the near-field beamspace over the existing methods. Finally, we point out several applications of the near-field beamspace and highlight some potential directions for future study in the near-field beamspace.

Downlink Beamforming for Cell-Free ISAC: A Fast Complex Oblique Manifold Approach

Cell-free integrated sensing and communication (CF-ISAC) systems are just emerging as an interesting technique for future communications. Such a system comprises several multiple-antenna access points (APs), serving multiple single-antenna communication users and sensing targets. However, efficient beamforming designs that achieve high precision and robust performance in densely populated networks are lacking. This paper proposes a new beamforming algorithm by exploiting the inherent Riemannian manifold structure. The aim is to maximize the communication sum rate while satisfying sensing beampattern gains and per AP transmit power constraints. To address this constrained optimization problem, a highly efficient augmented Lagrangian model-based iterative manifold optimization for CF-ISAC (ALMCI) algorithm is developed. This algorithm exploits the geometry of the proposed problem and uses a complex oblique manifold. Conventional convex-concave procedure (CCPA) and multidimensional complex quadratic transform (MCQT)-CSA algorithms are also developed as comparative benchmarks. The ALMCI algorithm significantly outperforms both of these. For example, with 16 APs having 12 antennas and 30 dBm transmit power each, our proposed ALMCI algorithm yields 22.7% and 6.7% sum rate gains over the CCPA and MCQT-CSA algorithms, respectively. In addition to improvement in communication capacity, the ALMCI algorithm achieves superior beamforming gains and reduced complexity.

Fast Adaptation for Deep Learning-based Wireless Communications

The integration with artificial intelligence (AI) is recognized as one of the six usage scenarios in next-generation wireless communications. However, several critical challenges hinder the widespread application of deep learning (DL) techniques in wireless communications. In particular, existing DL-based wireless communications struggle to adapt to the rapidly changing wireless environments. In this paper, we discuss fast adaptation for DL-based wireless communications by using few-shot learning (FSL) techniques. We first identify the differences between fast adaptation in wireless communications and traditional AI tasks by highlighting two distinct FSL design requirements for wireless communications. To establish a wide perspective, we present a comprehensive review of the existing FSL techniques in wireless communications that satisfy these two design requirements. In particular, we emphasize the importance of applying domain knowledge in achieving fast adaptation. We specifically focus on multiuser multiple-input multiple-output (MU-MIMO) precoding as an examples to demonstrate the advantages of the FSL to achieve fast adaptation in wireless communications. Finally, we highlight several open research issues for achieving broadscope future deployment of fast adaptive DL in wireless communication applications.

Meta-Learning Empowered Graph Neural Networks for Radio Resource Management

In this paper, we consider a radio resource management (RRM) problem in the dynamic wireless networks, comprising multiple communication links that share the same spectrum resource. To achieve high network throughput while ensuring fairness across all links, we formulate a resilient power optimization problem with per-user minimum-rate constraints. We obtain the corresponding Lagrangian dual problem and parameterize all variables with neural networks, which can be trained in an unsupervised manner due to the provably acceptable duality gap. We develop a meta-learning approach with graph neural networks (GNNs) as parameterization that exhibits fast adaptation and scalability to varying network configurations. We formulate the objective of meta-learning by amalgamating the Lagrangian functions of different network configurations and utilize a first-order meta-learning algorithm, called Reptile, to obtain the meta-parameters. Numerical results verify that our method can efficiently improve the overall throughput and ensure the minimum rate performance. We further demonstrate that using the meta-parameters as initialization, our method can achieve fast adaptation to new wireless network configurations and reduce the number of required training data samples.

Beam Prediction based on Large Language Models

Millimeter-wave (mmWave) communication is promising for next-generation wireless networks but suffers from significant path loss, requiring extensive antenna arrays and frequent beam training. Traditional deep learning models, such as long short-term memory (LSTM), enhance beam tracking accuracy however are limited by poor robustness and generalization. In this letter, we use large language models (LLMs) to improve the robustness of beam prediction. By converting time series data into text-based representations and employing the Prompt-as-Prefix (PaP) technique for contextual enrichment, our approach unleashes the strength of LLMs for time series forecasting. Simulation results demonstrate that our LLM-based method offers superior robustness and generalization compared to LSTM-based models, showcasing the potential of LLMs in wireless communications.

Machine Learning in Communications: A Road to Intelligent Transmission and Processing

Prior to the era of artificial intelligence and big data, wireless communications primarily followed a conventional research route involving problem analysis, model building and calibration, algorithm design and tuning, and holistic and empirical verification. However, this methodology often encountered limitations when dealing with large-scale and complex problems and managing dynamic and massive data, resulting in inefficiencies and limited performance of traditional communication systems and methods. As such, wireless communications have embraced the revolutionary impact of artificial intelligence and machine learning, giving birth to more adaptive, efficient, and intelligent systems and algorithms. This technological shift opens a road to intelligent information transmission and processing. This overview article discusses the typical roles of machine learning in intelligent wireless communications, as well as its features, challenges, and practical considerations.