In this article, a framework of artificial intelligence (AI)-native cross-module optimized physical layer with cooperative control agents is proposed, which involves optimization across global AI/machine learning (ML) modules of the physical layer with innovative design of multiple enhancement mechanisms and control strategies. Specifically, it achieves simultaneous optimization across global modules of uplink AI/ML-based joint source-channel coding with modulation, and downlink AI/ML-based modulation with precoding and corresponding data detection, reducing traditional inter-module information barriers to facilitate end-to-end optimization toward global objectives. Moreover, multiple enhancement mechanisms are also proposed, including i) an AI/ML-based cross-layer modulation approach with theoretical analysis for downlink transmission that breaks the isolation of inter-layer features to expand the solution space for determining improved constellation, ii) a utility-oriented precoder construction method that shifts the role of the AI/ML-based CSI feedback decoder from recovering the original CSI to directly generating precoding matrices aiming to improve end-to-end performance, and iii) incorporating modulation into AI/ML-based CSI feedback to bypass bit-level bottlenecks that introduce quantization errors, non-differentiable gradients, and limitations in constellation solution spaces. Furthermore, AI/ML-based control agents for optimized transmission schemes are proposed that leverage AI/ML to perform model switching according to channel state, thereby enabling integrated control for global throughput optimization. Finally, simulation results demonstrate the superiority of the proposed solutions in terms of block error rate and throughput. These extensive simulations employ more practical assumptions that are aligned with the requirements of the 3rd Generation Partnership Project (3GPP), which hopefully provides valuable insights for future 3GPP standardization discussions.

Accurate and effective channel state information (CSI) feedback is a key technology for massive multiple-input and multiple-output systems. Recently, deep learning (DL) has been introduced for CSI feedback enhancement through massive collected training data and lengthy training time, which is quite costly and impractical for realistic deployment. In this article, a knowledge-driven meta-learning approach is proposed, where the DL model initialized by the meta model obtained from meta training phase is able to achieve rapid convergence when facing a new scenario during target retraining phase. Specifically, instead of training with massive data collected from various scenarios, the meta task environment is constructed based on the intrinsic knowledge of spatial-frequency characteristics of CSI for meta training. Moreover, the target task dataset is also augmented by exploiting the knowledge of statistical characteristics of wireless channel, so that the DL model can achieve higher performance with small actually collected dataset and short training time. In addition, we provide analyses of rationale for the improvement yielded by the knowledge in both phases. Simulation results demonstrate the superiority of the proposed approach from the perspective of feedback performance and convergence speed.

Accurate and effective channel state information (CSI) feedback is a key technology for massive multiple-input and multiple-output (MIMO) systems. Recently, deep learning (DL) has been introduced to enhance CSI feedback in massive MIMO application, where the massive collected training data and lengthy training time are costly and impractical for realistic deployment. In this paper, a knowledge-driven meta-learning solution for CSI feedback is proposed, where the DL model initialized by the meta model obtained from meta training phase is able to achieve rapid convergence when facing a new scenario during the target retraining phase. Specifically, instead of training with massive data collected from various scenarios, the meta task environment is constructed based on the intrinsic knowledge of spatial-frequency characteristics of CSI for meta training. Moreover, the target task dataset is also augmented by exploiting the knowledge of statistical characteristics of channel, so that the DL model initialized by meta training can rapidly fit into a new target scenario with higher performance using only a few actually collected data in the target retraining phase. The method greatly reduces the demand for the number of actual collected data, as well as the cost of training time for realistic deployment. Simulation results demonstrate the superiority of the proposed approach from the perspective of feedback performance and convergence speed.

Soil erosion poses a serious problem for sustainable agriculture and the environment. There is a need to develop a simple and practical approach for broad area mapping of soil erosion risk that uses the uncertain but available information as input data within the constraints of reasonable cost and time. In this work, a predictive approach for conducting analytical erosion risk assessment across broad areas is developed, which combines a fuzzy decision tree (FDT), remote sensing and Geographic Information System (GIS). This approach is applicable to situations with a limited amount of input data and can easily adjust assessment factors according to actual need. In this study, four dominating factors affecting soil erosion were considered: soil, topography, land cover and climate. GIS thematic layers of these factors were constructed followed by fuzzified analysis through trapezoidal shaped membership functions. Based on subdivided erosion response units (ERUs), an optimal FDT was determined to classify monthly soil erosion risk into five levels. High-risk and very high-risk soil erosion in the study area is mainly concentrated from June to August, with July and August showing the highest risk covering more than 80% of the study area. November to March is dominated by low risk over more than 90% of the study area, while medium risk is dominant in April, May, September and October. Compared with field survey data, the fuzzy decision erosion risk assessment approach was shown to be applicable and economical for rapidly identifying and locating soil erosion risk with limited input data by means of remote sensing and GIS.

We investigate the problem of downlink user scheduling in multiple-input multiple-output (MIMO) systems withMtransmit antennas andKhomogeneous multiantenna receiving users. We develop a simple algorithm to schedule a subset ofMactive users for data transmission. We first identify a set ofLcandidate users that should provide channel information feedback. We then selectMactive users among theLcandidates based on joint consideration of their effective channel gains and directions. We derive an asymptotic upper bound for the sum rate gap between the maximum sum rate of theMselected active users, achieved by dirty paper coding, and the full sum capacity of the original MIMO broadcast channel. Utilizing the upper bound, we show that the sum rate gap can be reduced to meet user requirement by suitably choosing a large but still finiteL. Furthermore, we also investigate the performance of the low complexity zero-forcing beamforming (ZFBF) for theMactive users.

We investigate new scheduling algorithms for MIMO broadcast systems to maximize the achievable sum rate of block diagonalization transceivers. In contrast to conventional schemes which select users directly, we investigate a more efficient type of scheduling algorithms, named as multiuser eigenmode transmission (MET). It is based on the selection of eigenmodes of all users. Utilizing an iterative procedure to calculate the Moore-Penrose generalized inverse of a matrix consisting of the already selected eigenmodes, we propose a low complexity multiuser eigenmode selection method based on the greedy algorithm. Then we show that the multiuser dominant eigenmode transmission can achieve the same sum rate scaling law as that of DPC. Therefore, to further reduce the complexity, we restrict our search space to the set of dominant eigenmodes and derive an alternative eigenmode selection algorithm with lower complexity at the cost of little performance loss, which we name as the low-complexity multiuser dominant eigenmode selection algorithm.

This work investigates the design of linear precoders for ARQ packet retransmissions in multi-input multi-output (MIMO) systems. We consider transmitter precoder design based on partial MIMO channel information in the form of their covariance feedback. Our objective is to maximize the ergodic mutual information provided by multiple (re)transmissions of a packet subject to transmission power constraint. We propose a set of near-optimal successive linear ARQ precoders for flat fading MIMO channels. This progressive linear ARQ precoder combines the appropriate power loading and the reverse-order pairing of singular values in the current retransmission with previous transmissions. This reverse-order pairing is a special feature unique to our sequential ARQ preceding approach with demonstrated performance gains.

We investigate the joint optimization of automatic repeat request (ARQ) and MIMO diversity for multi-user wireless communication systems. Exploiting MIMO for diversity gain and ARQ for error correction, our goal is to better integrate MIMO and ARQ in the transceivers to improve multi-user uplink performance. Specifically, we design optimal linear precoders for ARQ retransmissions by multiple wireless uplink terminals equipped with MIMO transmitters and we rely on joint detection of multiple retransmissions at the receivers. To better utilize ARQ diversity, we derive a set of progressive linear ARQ precoders for flat fading multiple-access channels based on the criterion of maximizing the mutual information of multiuser (re)-transmissions. We propose two efficient iterative numerical algorithms to determine the optimal precoders.

Orthogonal frequency division multiplexing (OFDM) is sensitive to carrier frequency offset (CFO), which destroys the orthogonality and causes inter-carrier interference (ICI). ICI self-cancellation schemes based on polynomial cancellation coding (PCC-OFDM) can evidently reduce the sensitivity of systems to CFO. In this paper, we analyze the performance of PCC-OFDM systems over additive white Gaussian noise (AWGN) channels in the presence of CFO. Two criteria are used to evaluate the effect of the CFO on performance degradation. Firstly, the closed-form expressions of the statistical average carrier-to-interference power ratio (CIR) and ICI power are presented. The closed-form expressions depend only on the normalized CFO, and are irrelevant to the number of subcarriers. Secondly, by exploiting the properties of the Beaulieu series, the effect of the CFO on symbol error rate (SER) for PCC-OFDM systems modulated with BPSK, QPSK and 16-QAM is exactly expressed as the sum of an infinite series in terms of the characteristic function (CHF) of the ICI.