Michele Marzollo, Jiawei Zhuang, Niklas Roemer, Niklas Zwingenberger, Lorenz K Muller, Lukas Cavigelli. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Large language models (LLMs) are typically served from clusters of GPUs/NPUs that consist of a large number of devices. Unfortunately, communication between these devices incurs significant overhead, increasing inference latency and cost while limiting the scalability. Prior work has addressed this issue by overlapping communication with computation; however, it has severe limitations due to the data dependencies between these operations. In this paper, we propose PRESERVE, a novel framework that prefetches model weights and KV-cache from off-chip HBM memory to the on-chip cache of AI accelerators in parallel with communication operations, offering up to 1.8× end-to-end speedup on commercial AI accelerators.

Integrated sensing and communication (ISAC) has emerged as a key enabler for future wireless networks by unifying communication and sensing functionalities within a shared framework. However, achieving the coordination gains of these two functionalities critically depends on accurate estimation of the sensing targets and communication channels, while their joint estimation remains challenging. To address this, this paper proposes a Bayesian tensor decomposition (BTD) approach for joint channel estimation and target sensing in multiple-input multiple-output (MIMO)-ISAC systems, where parts of sensing targets also act as communication scatterers. Specifically, we develop space-frequency domain received signal models for target sensing and channel estimation and formulate them as canonical polyadic decomposition (CPD) problems under the tensor decomposition framework. This formulation reveals the common multilinear structure and the partially shared physical parameters between sensing and communication, which underpins the ensuing joint estimation task. To solve these problems, we propose a dual-module Vandermonde structure-assisted BTD (V-BTD) algorithm that incorporates propagation-induced Vandermonde structure constraints within a Bayesian framework to enable effective sensing-communication collaboration while maintaining problem feasibility. In this algorithm, Module A estimates the factor matrices via unstructured BTD with Gaussian priors, whereas Module B exploits the Vandermonde structure to recover the underlying physical parameters using generalized von Mises priors. The dual-module design alternates between an unstructured tensor decomposition step and a structure-aware parameter recovery step, yielding a favorable trade-off between inference exactness and computational tractability. With the flexible prior models in the BTD framework, the proposed algorithm supports both uninformative and informative settings, thereby allowing sensing-derived information to be incorporated for communication channel estimation to further improve estimation accuracy. Simulation results demonstrate that the proposed method significantly outperforms the benchmarks, highlighting its superiority for advanced ISAC systems.

With the growing number of users in multi-user multiple-input multiple-output (MU-MIMO) systems, demodulation reference signals (DMRS) are efficiently multiplexed in the code domain via orthogonal cover codes (OCC) to ensure orthogonality and minimize pilot interference. In this paper, we investigate uplink DMRS-based channel estimation for MU-MIMO systems with Type II OCC pattern standardized in third generation partnership project (3GPP) Release 18, leveraging location-specific statistical channel state information (SCSI) to enhance performance. Specifically, we propose a SCSI-assisted Bayesian channel estimator (SA-BCE) based on the minimum mean square error criterion to suppress the pilot interference and noise, albeit at the cost of cubic computational complexity due to matrix inversions. To reduce this complexity while maintaining performance, we extend the scheme to a windowed version (SA-WBCE), which incorporates antenna-frequency domain windowing and beam-delay domain processing to exploit asymptotic sparsity and mitigate energy leakage in practical systems. To avoid the frequent real-time SCSI acquisition, we construct a grid-based location-specific SCSI database based on the principle of spatial consistency, and subsequently leverage the uplink received signals within each grid to extract the SCSI. Facilitated by the multilinear structure of wireless channels, we formulate the SCSI acquisition problem within each grid as a tensor decomposition problem, where the factor matrices are parameterized by the multi-path powers, delays, and angles. The computational complexity of SCSI acquisition can be significantly reduced by exploiting the Vandermonde structure of the factor matrices. Simulation results demonstrate that the proposed location-specific SCSI database construction method achieves high accuracy, while the SA-BCE and SA-WBCE significantly outperform state-of-the-art benchmarks in MU-MIMO systems.

In massive multiple-input multiple-output (MIMO) systems, the downlink transmission performance heavily relies on accurate channel state information (CSI). Constrained by the transmitted power, user equipment always transmits sounding reference signals (SRSs) to the base station through frequency hopping, which will be leveraged to estimate uplink CSI and subsequently predict downlink CSI. This paper aims to investigate joint channel estimation and prediction (JCEP) for massive MIMO with frequency hopping sounding (FHS). Specifically, we present a multiple-subband (MS) delay-angle-Doppler (DAD) domain channel model with off-grid basis to tackle the energy leakage problem. Furthermore, we formulate the JCEP problem with FHS as a multiple measurement vector (MMV) problem, facilitating the sharing of common CSI across different subbands. To solve this problem, we propose an efficient Off-Grid-MS hybrid message passing (HMP) algorithm under the constrained Bethe free energy (BFE) framework. Aiming to address the lack of prior CSI in practical scenarios, the proposed algorithm can adaptively learn the hyper-parameters of the channel by minimizing the corresponding terms in the BFE expression. To alleviate the complexity of channel hyper-parameter learning, we leverage the approximations of the off-grid matrices to simplify the off-grid hyper-parameter estimation. Numerical results illustrate that the proposed algorithm can effectively mitigate the energy leakage issue and exploit the common CSI across different subbands, acquiring more accurate CSI compared to state-of-the-art counterparts.