Double-Side Delay Alignment Modulation for Multi-User Millimeter Wave and TeraHertz Communications

Delay alignment modulation (DAM) is an innovative broadband modulation technique well suited for millimeter wave (mmWave) and terahertz (THz) massive multiple-input multiple-output (MIMO) communication systems. Leveraging the high spatial resolution and sparsity of multi-path channels, DAM mitigates inter-symbol interference (ISI) effectively, by aligning all multi-path components through a combination of delay pre/post-compensation and path-based beamforming. As such, ISI is eliminated while preserving multi-path power gains. In this paper, we explore multi-user double-side DAM with both delay pre-compensation at the transmitter and post-compensation at the receiver, contrasting with prior one-side DAM that primarily focuses on delay pre-compensation only. Firstly, we reveal the constraint for the introduced delays and the delay pre/post-compensation vectors tailored for multi-user double-side DAM, given a specific number of delay pre/post-compensations. Furthermore, we show that as long as the number of base station (BS)/user equipment (UE) antennas is sufficiently large, single-side DAM, where delay compensation is only performed at the BS/UE, is preferred than double-side DAM since the former results in less ISI to be spatially eliminated. Next, we propose two low-complexity path-based beamforming strategies based on the eigen-beamforming transmission and ISI-zero forcing (ZF) principles, respectively, based on which the achievable sum rates are studied. Simulation results verify that with sufficiently large BS/UE antennas, single-side DAM is sufficient. Furthermore, compared to the benchmark scheme of orthogonal frequency division multiplexing (OFDM), multi-user BS-side DAM achieves higher spectral efficiency and/or lower peak-to-average power ratio (PAPR).

AlphaPruning: Using Heavy-Tailed Self Regularization Theory for Improved Layer-wise Pruning of Large Language Models

Recent work on pruning large language models (LLMs) has shown that one can eliminate a large number of parameters without compromising performance, making pruning a promising strategy to reduce LLM model size. Existing LLM pruning strategies typically assign uniform pruning ratios across layers, limiting overall pruning ability; and recent work on layerwise pruning of LLMs is often based on heuristics that can easily lead to suboptimal performance. In this paper, we leverage Heavy-Tailed Self-Regularization (HT-SR) Theory, in particular the shape of empirical spectral densities (ESDs) of weight matrices, to design improved layerwise pruning ratios for LLMs. Our analysis reveals a wide variability in how well-trained, and thus relatedly how prunable, different layers of an LLM are. Based on this, we propose AlphaPruning, which uses shape metrics to allocate layerwise sparsity ratios in a more theoretically principled manner. AlphaPruning can be used in conjunction with multiple existing LLM pruning methods. Our empirical results show that AlphaPruning prunes LLaMA-7B to 80% sparsity while maintaining reasonable perplexity, marking a first in the literature on LLMs. We have open-sourced our code at https://github.com/haiquanlu/AlphaPruning.

Early Preparation Pays Off: New Classifier Pre-tuning for Class Incremental Semantic Segmentation

Class incremental semantic segmentation aims to preserve old knowledge while learning new tasks, however, it is impeded by catastrophic forgetting and background shift issues. Prior works indicate the pivotal importance of initializing new classifiers and mainly focus on transferring knowledge from the background classifier or preparing classifiers for future classes, neglecting the flexibility and variance of new classifiers. In this paper, we propose a new classifier pre-tuning~(NeST) method applied before the formal training process, learning a transformation from old classifiers to generate new classifiers for initialization rather than directly tuning the parameters of new classifiers. Our method can make new classifiers align with the backbone and adapt to the new data, preventing drastic changes in the feature extractor when learning new classes. Besides, we design a strategy considering the cross-task class similarity to initialize matrices used in the transformation, helping achieve the stability-plasticity trade-off. Experiments on Pascal VOC 2012 and ADE20K datasets show that the proposed strategy can significantly improve the performance of previous methods. The code is available at \url{https://github.com/zhengyuan-xie/ECCV24_NeST}.

Group Movable Antenna With Flexible Sparsity: Joint Array Position and Sparsity Optimization

Movable antenna (MA) is a promising technology to exploit the spatial variation of wireless channel for performance enhancement, by dynamically varying the antenna position within a certain region. However, for multi-antenna communication systems, moving each antenna independently not only requires prohibitive complexity to find the optimal antenna positions, but also incurs sophisticated movement control in practice. To address this issue, this letter proposes a new MA architecture termed group MA (GMA), enabling the group movement of all elements collectively in a continuous manner, and simultaneously achieving flexible array architecture by antenna selection (AS). In this letter, we focus on the uniform sparse array based GMA, where equally spaced antenna elements are selected to achieve desired array sparsity. The array position and sparsity level are jointly optimized to maximize the sum rate of the multi-user communication system. Numerical results verify the necessity to optimize the position and sparsity of GMA, and considerable performance gain is achieved as compared to the conventional fixed-position antenna (FPA).

Sharpness-diversity tradeoff: improving flat ensembles with SharpBalance

Recent studies on deep ensembles have identified the sharpness of the local minima of individual learners and the diversity of the ensemble members as key factors in improving test-time performance. Building on this, our study investigates the interplay between sharpness and diversity within deep ensembles, illustrating their crucial role in robust generalization to both in-distribution (ID) and out-of-distribution (OOD) data. We discover a trade-off between sharpness and diversity: minimizing the sharpness in the loss landscape tends to diminish the diversity of individual members within the ensemble, adversely affecting the ensemble's improvement. The trade-off is justified through our theoretical analysis and verified empirically through extensive experiments. To address the issue of reduced diversity, we introduce SharpBalance, a novel training approach that balances sharpness and diversity within ensembles. Theoretically, we show that our training strategy achieves a better sharpness-diversity trade-off. Empirically, we conducted comprehensive evaluations in various data sets (CIFAR-10, CIFAR-100, TinyImageNet) and showed that SharpBalance not only effectively improves the sharpness-diversity trade-off, but also significantly improves ensemble performance in ID and OOD scenarios.

A Tutorial on Near-Field XL-MIMO Communications Towards 6G

Extremely large-scale multiple-input multiple-output (XL-MIMO) is a promising technology for the sixth-generation (6G) mobile communication networks. By significantly boosting the antenna number or size to at least an order of magnitude beyond current massive MIMO systems, XL-MIMO is expected to unprecedentedly enhance the spectral efficiency and spatial resolution for wireless communication. The evolution from massive MIMO to XL-MIMO is not simply an increase in the array size, but faces new design challenges, in terms of near-field channel modelling, performance analysis, channel estimation, and practical implementation. In this article, we give a comprehensive tutorial overview on near-field XL-MIMO communications, aiming to provide useful guidance for tackling the above challenges. First, the basic near-field modelling for XL-MIMO is established, by considering the new characteristics of non-uniform spherical wave (NUSW) and spatial non-stationarity. Next, based on the near-field modelling, the performance analysis of XL-MIMO is presented, including the near-field signal-to-noise ratio (SNR) scaling laws, beam focusing pattern, achievable rate, and degrees-of-freedom (DoF). Furthermore, various XL-MIMO design issues such as near-field beam codebook, beam training, channel estimation, and delay alignment modulation (DAM) transmission are elaborated. Finally, we point out promising directions to inspire future research on near-field XL-MIMO communications.

Delay-Doppler Alignment Modulation for Spatially Sparse Massive MIMO Communication

Delay alignment modulation (DAM) is an emerging technique for achieving inter-symbol interference (ISI)-free wideband communications using spatial-delay processing, without relying on channel equalization or multi-carrier transmission. However, existing works on DAM only consider multiple-input single-output (MISO) communication systems and assume time-invariant channels. In this paper, by extending DAM to time-variant frequency-selective multiple-input multiple-output (MIMO) channels, we propose a novel technique termed \emph{delay-Doppler alignment modulation} (DDAM). Specifically, by leveraging \emph{delay-Doppler compensation} and \emph{path-based beamforming}, the Doppler effect of each multi-path can be eliminated and all multi-path signal components may reach the receiver concurrently and constructively. We first show that by applying path-based zero-forcing (ZF) precoding and receive combining, DDAM can transform the original time-variant frequency-selective channels into time-invariant ISI-free channels. The necessary and/or sufficient conditions to achieve such a transformation are derived. Then an asymptotic analysis is provided by showing that when the number of base station (BS) antennas is much larger than that of channel paths, DDAM enables time-invariant ISI-free channels with the simple delay-Doppler compensation and path-based maximal-ratio transmission (MRT) beamforming. Furthermore, for the general DDAM design with some tolerable ISI, the path-based transmit precoding and receive combining matrices are optimized to maximize the spectral efficiency. Numerical results are provided to compare the proposed DDAM technique with various benchmarking schemes, including MIMO-orthogonal time frequency space (OTFS), MIMO-orthogonal frequency-division multiplexing (OFDM) without or with carrier frequency offset (CFO) compensation, and beam alignment along the dominant path.

Achievable Rate Region and Path-Based Beamforming for Multi-User Single-Carrier Delay Alignment Modulation

Delay alignment modulation (DAM) is a novel wideband transmission technique for mmWave massive MIMO systems, which exploits the high spatial resolution and multi-path sparsity to mitigate ISI, without relying on channel equalization or multi-carrier transmission. In particular, DAM leverages the delay pre-compensation and path-based beamforming to effectively align the multi-path components, thus achieving the constructive multi-path combination for eliminating the ISI while preserving the multi-path power gain. Different from the existing works only considering single-user DAM, this paper investigates the DAM technique for multi-user mmWave massive MIMO communication. First, we consider the asymptotic regime when the number of antennas Mt at BS is sufficiently large. It is shown that by employing the simple delay pre-compensation and per-path-based MRT beamforming, the single-carrier DAM is able to perfectly eliminate both ISI and IUI. Next, we consider the general scenario with Mt being finite. In this scenario, we characterize the achievable rate region of the multi-user DAM system by finding its Pareto boundary. Specifically, we formulate a rate-profile-constrained sum rate maximization problem by optimizing the per-path-based beamforming. Furthermore, we present three low-complexity per-path-based beamforming strategies based on the MRT, zero-forcing, and regularized zero-forcing principles, respectively, based on which the achievable sum rates are studied. Finally, we provide simulation results to demonstrate the performance of our proposed strategies as compared to two benchmark schemes based on the strongest-path-based beamforming and the prevalent OFDM, respectively. It is shown that DAM achieves higher spectral efficiency and/or lower peak-to-average-ratio, for systems with high spatial resolution and multi-path diversity.

Multi-User Delay Alignment Modulation for Millimeter Wave Massive MIMO

Delay alignment modulation (DAM) is a novel wideband communication technique, which exploits the high spatial resolution and multi-path sparsity of millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems to mitigate inter-symbol interference (ISI), without relying on conventional techniques like channel equalization or multi-carrier transmission. In this paper, we extend the DAM technique to multi-user mmWave massive MIMO communication systems. We first provide asymptotic analysis by showing that when the number of base station (BS) antennas is much larger than the total number of channel paths, DAM is able to eliminate both ISI and inter-user interference (IUI) with the simple delay pre-compensation and per-path-based maximal ratio transmission (MRT) beamforming. We then study the general multi-user DAM design by considering the three classical transmit beamforming strategies in a per-path basis, namely MRT, zero-forcing (ZF) and regularized zero-forcing (RZF). Simulation results demonstrate that multi-user DAM can significantly outperform the benchmarking single-carrier ISI mitigation technique that only uses the strongest channel path of each user.

Delay Alignment Modulation: Manipulating Channel Delay Spread for Efficient Single- and Multi-Carrier Communication

The evolution of mobile communication networks has always been accompanied by the advancement of inter-symbol interference (ISI) mitigation techniques, from equalization in 2G, spread spectrum and RAKE receiver in 3G, to OFDM in 4G and 5G. Looking forward towards 6G, by exploiting the extremely large spatial dimension brought by large antenna arrays and multi-path sparsity of millimeter wave (mmWave)/Terahertz channels, we propose a novel ISI mitigation technique, termed delay alignment modulation (DAM). The key ideas of DAM are path delay pre-compensation and path-based beamforming, i.e., by deliberately introducing symbol delays to compensate respective multi-path delays of the channel, so that with appropriate per-path-based beamforming, the multi-path signal components will arrive at the receiver simultaneously and constructively. To gain some insights, we first show that perfect delay alignment can be achieved to transform the time-dispersive channel to time non-dispersive channel, without sophisticated channel equalization or multi-carrier processing. This thus enables efficient equalization-free single-carrier transmission or CP-free OFDM transmission. When perfect DAM is infeasible or undesirable, we propose the generic DAM technique to significantly reduce the channel delay spread. This thus provides a new DoF to combat channel time dispersion for more efficient single- or multi-carrier signal transmissions. As an illustration, we propose the novel DAM-OFDM technique, which may save the CP overhead or mitigate the PAPR and CFO issues suffered by conventional OFDM. We show that DAM-OFDM involves joint frequency-domain and time-domain beamforming optimization, for which a closed-form solution is derived. Simulation results show that the proposed DAM-OFDM achieves significant performance gains over the conventional OFDM, in terms of spectral efficiency, BER and PAPR.