In this paper, we propose a novel approximate noise-whitening for multi-input multi-output (MIMO) detection with banded-inverse extension (BIE). If a noise covariance matrix origins from a Gauss-Markov process (GMP), its inverse is banded and the proposal is exact and yields no accuracy-losses. For general matrices to be inverted, the approximation-errors from inversion introduce a mismatched MIMO detection-model, which can cause performance-degradation. Hence, we develop an information-theoretic tool with generalized mutual information (GMI) to evaluate the impacts from approximation-errors on the final achievable rate. We show that the approximate noise-whitening method based on BIE not only minimizes the noise-distortion measured from the Kullback-Leibler (KL) divergence, but also asymptotically maximizes GMI of the MIMO system as signal-to-noise ratio (SNR) increases. Besides, the proposal also provides a unified framework for approximate matrix-inverse with an adjustable band-size that can tune the trade-off between complexity and accuracy.

It is well-known that probabilistic constellation shaping (PCS) provides a shaping-gain of 1.53dB asymptotically as signal-to-noie (SNR) increases. This is however, under ideal assumptions that the considered system operates in optimal sense and can achieve the Shannon capacity. While in practice the system can operate below the capacity, and in particular when block-error rate (BLER) and throughput are considered which are the most relevant merits, the benefits of PCS are unclear. In this paper, we propose a PCS-transceiver for the fifth-generation new-radio (5G-NR) that supports quadrature-amplitude-modulation (QAM) constellations shaped with PCS, namely, PCS-QAM. We put a special interest in the throughput achieved under a stringent BLER constraint, and validate the effectiveness of PCS with practical detection and decoding algorithms, in comparison to conventional uniform QAM constellations, namely, Uniform-QAM. We also analyze the properties of power-gain, entropy-loss, and peak-to-average power-ratio (PAPR), in connections to the PCS design. Further, we derive a necessary and sufficient condition for a PCS-QAM to outperform a Uniform-QAM in throughput, and prove that the normalized entropy-loss with the PCS-QAM must be less than the BLER obtained with the Uniform-QAM. Furthermore, we demonstrate that the PCS-transceiver is flexible in rate-adaption without affecting the encoder, and yields a better throughput-envelope by mitigating the SNR-gaps between two adjacent modulation and coding scheme (MCS) indexes in 5G-NR.

In this paper, we propose a novel three-dimensional (3D) near-field beamforming (BF) design for Large Intelligent Surface (LIS). We firstly investigate the definitions of near-field and far-field of LIS, and derive the Fresnel near-field region where amplitudes variations are negligible and only phase variations worsen the harvested array-gains. We show that the Fresnel region which covers the majority part of near-field, can be enlarged by a factor of four when considering possible imperfectness from a conventional two-dimensional (2D) far-field BF. Therefore, it is of interest to design analog 3D-BF than can recover array-gain losses in this region. Secondly, with the proved decomposition theorem we show that the optimal 3D-BF can be decomposed into a 2D far-field BF and a one-dimensional (1D) near-field BF. The 2D far-field BF compensates phase variations from mismatches in the azimuth and elevation angles, while the 1D near-field BF compensates remaining phases variations caused by distance differences from the UE to different antenna-elements on LIS. Such a proposed “2D+1D” BF design reduces codebook size significantly and is fully compatible with the existing far-field BF in the fifth-generation new-radio (5G-NR) system. Thirdly, we analyze an optimal codebook design for the 1D near-field BF, and show that with a small codebook it can perform close to optimal. Numerical results further verify that our proposal is effective and robust to recover array-gains in the near-field of LIS.