Digital communication systems inherently operate through physical media governed by partial differential equations (PDEs). In this paper, we introduce a physics-aware decoding framework that integrates gradient descent-based error correcting algorithms with PDE-based channel modeling using differentiable PDE solvers. At the core of our approach is gradient flow decoding, which harnesses gradient information directly from the PDE solver to guide the decoding process. We validate our method through numerical experiments on both the heat equation and the nonlinear Schrödinger equation (NLSE), demonstrating significant improvements in decoding performance. The implications of this work extend beyond decoding applications, establishing a new paradigm for physicsaware signal processing that shows promise for various signal detection and signal recovery tasks.

In this paper, we propose a novel low-complexity Bayesian receiver design to jointly perform channel, CFO, and data estimation from observations subject to different CFO among users in MU-MIMO-OFDM systems. ICI due to CFO significantly reduces channel estimation accuracy under frequency-selective fading environments, making reliable communications difficult. To tackle this difficulty, a JCCE algorithm is designed based on BP. Our method uses a BG distribution as the prior distribution of the channel coefficient to capture its delay-domain sparsity, and a GM distribution as the prior distribution of the phase shift due to CFO to perform parallel search for the allowable range of CFO defined in the 3GPP standard by the number of mixture components. The proposed algorithm can further improve the accuracy of channel, CFO, and data estimation by treating the tentatively detected data symbols as extra pilots. The efficacy of the proposed method is confirmed by numerical studies, which show that the proposed method not only significantly outperforms the SotA methods with much lower computational cost but also approaches the performance of an idealized Genie-aided scheme.

This paper proposes a novel angle-of-arrival (AoA) estimation-aided Bayesian joint channel and data estimation (JCDE) algorithm for uplink signal detection in millimeter-wave (mmWave) massive multi-user MIMO (MU-MIMO) systems with short non-orthogonal pilots. In the proposed method, the prior distribution of mmWave channels in the angular domain after digital beamforming is approximated by a Bernoulli-Gaussian (BG) distribution, and the mismatch with the actual distribution is corrected based on AoA estimation by low-complexity angle rotation (AR) method. The resultant beam-domain JCDE algorithm has an inherent mechanism to update path gains of the channels estimated based on AoA for every iteration. This receiver design allows us to take the advantage of both stochastic (i.e., Bayesian) and deterministic (i.e., AR) approaches. Efficacy of the proposed method over the state-of-the-art is confirmed via computer simulations in terms of bit error rate (BER) performance compared to the state-of-the-art alternatives.

Mode-division multiplexing (MDM) using few-mode fibers (FMFs) appears as a promising technology to increase fiber capacity by a few orders of magnitude and sustain the traffic demand for the decades to come. The potential of MDM lies in its ability to exploit multiple modes within a single optical fiber strand. Since 2011 and the first promising demonstrations, impressive progress has been made. In this article, we will show how the most recent advances in design and fabrication have improved the performance of FMFs and MDM systems, and allowed to turn research demonstrations into practical deployments.

We propose a novel variable stiffness joint (VSJ) mechanism that achieves the desired stiffness by using elastic deformation of silicone rubber cushions (SRCs). This mechanism can absorb the forces from multiple direction by the flexibility of SRCs and guarantee high safety. To react adequately against unpredictable collision or to manipulate an unstable object skillfully, a joint should be designed to have a stiffness adjustment capability in a short time. The proposed mechanism adjusts stiffness by controlling the position of an elliptical cam that pushes or pulls the support wall of the SRCs to surround them. The elastic deformation of the SRCs can be restricted according to the degree of the surrounded region of the SRCs. As a result, we can control the joint stiffness easily. Furthermore, the rotation of the cam from 0 deg. to 90 deg. accomodates the entire range of stiffness;, as a result, we can expect a fast adjustment of the stiffness. In this paper, we describe in detail the mechanism and the theoretical model for the stiffness adjustment. Moreover fundamental performances on the stiffness range, adjustment speed, and movability of the joint are shown through several experiments, and the problems in actual use are discussed.