Information Theory (cs.IT)

Abstract: This paper describes the design and C99 implementation of a free and open-source Low-Density Parity-Check (LDPC) codes encoder and decoder focused primarily on the Quasi-Cyclic LDPC (QCLDPC) codes utilized in the IEEE 802.11ax-2021 (Wi-Fi 6) and IEEE 802.16-2017 (WiMAX) standards. The encoder is designed in two variants: the first one universal, the other a minimal memory usage design. The decoder provides a single- and multi- threaded implementation of the layered singlescan min-sum LDPC decoding algorithm both for floating point and fixed-point arithmetic. Both encoder and decoder are directly callable from MATLAB using the provided MEX wrappers but are designed to be simply used in any C project. A comparison of throughput and error performance with the recent commercial closed-source MEX implementation of an LDPC encoder and decoder introduced in MATLAB R2021b Communications Toolbox is provided. Source code portability to alternative nonx86 architectures is facilitated by using only the standard C99 constructs, GNU tools, and POSIX libraries. The implementation maintains low-memory requirements, enabling its deployment in a constrained-architecture in the context of Internet of Things. All source codes are freely available on GitHub under a permissive BSD license.

Abstract: Numerous low-complexity iterative algorithms have been proposed to offer the performance of linear multiple-input multiple-output (MIMO) detectors bypassing the channel matrix inverse. These algorithms exhibit fast convergence in well-conditioned MIMO channels. However, in the emerging MIMO paradigm utilizing extremely large aperture arrays (ELAA), the wireless channel may become ill-conditioned because of spatial non-stationarity, which results in a considerably slower convergence rate for these algorithms. In this paper, we propose a novel ELAA-MIMO detection scheme that leverages user-wise singular value decomposition (UW-SVD) to accelerate the convergence of these iterative algorithms. By applying UW-SVD, the MIMO signal model can be converted into an equivalent form featuring a better-conditioned transfer function. Then, existing iterative algorithms can be utilized to recover the transmitted signal from the converted signal model with accelerated convergence towards zero-forcing performance. Our simulation results indicate that proposed UW-SVD scheme can significantly accelerate the convergence of the iterative algorithms in spatially non-stationary ELAA channels. Moreover, the computational complexity of the UW-SVD is comparatively minor in relation to the inherent complexity of the iterative algorithms.

Abstract: We consider an asynchronous hierarchical federated learning (AHFL) setting with a client-edge-cloud framework. The clients exchange the trained parameters with their corresponding edge servers, which update the locally aggregated model. This model is then transmitted to all the clients in the local cluster. The edge servers communicate to the central cloud server for global model aggregation. The goal of each client is to converge to the global model, while maintaining timeliness of the clients, i.e., having optimum training iteration time. We investigate the convergence criteria for such a system with dense clusters. Our analysis shows that for a system of $n$ clients with fixed average timeliness, the convergence in finite time is probabilistically guaranteed, if the nodes are divided into $O(1)$ number of clusters, that is, if the system is built as a sparse set of edge servers with dense client bases each.