arXiv daily

Information Theory (cs.IT)

Wed, 19 Jul 2023

Other arXiv digests in this category:Thu, 14 Sep 2023; Wed, 13 Sep 2023; Tue, 12 Sep 2023; Mon, 11 Sep 2023; Fri, 08 Sep 2023; Tue, 05 Sep 2023; Fri, 01 Sep 2023; Thu, 31 Aug 2023; Wed, 30 Aug 2023; Tue, 29 Aug 2023; Mon, 28 Aug 2023; Fri, 25 Aug 2023; Thu, 24 Aug 2023; Wed, 23 Aug 2023; Tue, 22 Aug 2023; Mon, 21 Aug 2023; Fri, 18 Aug 2023; Thu, 17 Aug 2023; Wed, 16 Aug 2023; Tue, 15 Aug 2023; Mon, 14 Aug 2023; Fri, 11 Aug 2023; Thu, 10 Aug 2023; Wed, 09 Aug 2023; Tue, 08 Aug 2023; Mon, 07 Aug 2023; Fri, 04 Aug 2023; Thu, 03 Aug 2023; Wed, 02 Aug 2023; Tue, 01 Aug 2023; Mon, 31 Jul 2023; Fri, 28 Jul 2023; Thu, 27 Jul 2023; Wed, 26 Jul 2023; Tue, 25 Jul 2023; Mon, 24 Jul 2023; Fri, 21 Jul 2023; Thu, 20 Jul 2023; Tue, 18 Jul 2023; Mon, 17 Jul 2023; Fri, 14 Jul 2023; Thu, 13 Jul 2023; Wed, 12 Jul 2023; Tue, 11 Jul 2023; Mon, 10 Jul 2023; Fri, 07 Jul 2023; Thu, 06 Jul 2023; Wed, 05 Jul 2023; Tue, 04 Jul 2023; Mon, 03 Jul 2023; Fri, 30 Jun 2023; Thu, 29 Jun 2023; Wed, 28 Jun 2023; Tue, 27 Jun 2023; Mon, 26 Jun 2023; Fri, 23 Jun 2023; Thu, 22 Jun 2023; Wed, 21 Jun 2023; Tue, 20 Jun 2023; Fri, 16 Jun 2023; Thu, 15 Jun 2023; Tue, 13 Jun 2023; Mon, 12 Jun 2023; Fri, 09 Jun 2023; Thu, 08 Jun 2023; Wed, 07 Jun 2023; Tue, 06 Jun 2023; Mon, 05 Jun 2023; Fri, 02 Jun 2023; Thu, 01 Jun 2023; Wed, 31 May 2023; Tue, 30 May 2023; Mon, 29 May 2023; Fri, 26 May 2023; Thu, 25 May 2023; Wed, 24 May 2023; Tue, 23 May 2023; Mon, 22 May 2023; Fri, 19 May 2023; Thu, 18 May 2023; Wed, 17 May 2023; Tue, 16 May 2023; Mon, 15 May 2023; Fri, 12 May 2023; Thu, 11 May 2023; Wed, 10 May 2023; Tue, 09 May 2023; Mon, 08 May 2023; Fri, 05 May 2023; Thu, 04 May 2023; Wed, 03 May 2023; Tue, 02 May 2023; Mon, 01 May 2023; Fri, 28 Apr 2023; Thu, 27 Apr 2023; Wed, 26 Apr 2023; Tue, 25 Apr 2023; Mon, 24 Apr 2023; Fri, 21 Apr 2023; Thu, 20 Apr 2023; Wed, 19 Apr 2023; Tue, 18 Apr 2023; Mon, 17 Apr 2023; Fri, 14 Apr 2023; Thu, 13 Apr 2023; Wed, 12 Apr 2023; Mon, 10 Apr 2023
1.A Low-Complexity Beamforming Design for Beyond-Diagonal RIS aided Multi-User Networks

Authors:Tianyu Fang, Yijie Mao

Abstract: Beyond-diagonal reconfigurable intelligent surface (BD-RIS) has been proposed recently as a novel and generalized RIS architecture that offers enhanced wave manipulation flexibility and large coverage expansion. However, the beyond-diagonal mathematical model in BD-RIS inevitably introduces additional optimization challenges in beamforming design. In this letter, we derive a closed-form solution for the BD-RIS passive beamforming matrix that maximizes the sum of the effective channel gains among users. We further propose a computationally efficient two-stage beamforming framework to jointly design the active beamforming at the base station and passive beamforming at the BD-RIS to enhance the sum-rate for a BD-RIS aided multi-user multi-antenna network.Numerical results show that our proposed algorithm achieves a higher sum-rate while requiring less computation time compared to state-of-the-art algorithms. The proposed algorithm paves the way for practical beamforming design in BD-RIS aided wireless networks.

2.Transmitter Side Beyond-Diagonal Reconfigurable Intelligent Surface for Massive MIMO Networks

Authors:Anup Mishra, Yijie Mao, Carmen D'Andrea, Stefano Buzzi, Bruno Clerckx

Abstract: This letter focuses on a transmitter or base station (BS) side beyond-diagonal reflecting intelligent surface (BD-RIS) deployment strategy to enhance the spectral efficiency (SE) of a time-division-duplex massive multiple-input multiple-output (MaMIMO) network. In this strategy, the active antenna array utilizes a BD-RIS at the BS to serve multiple users in the downlink. Based on the knowledge of statistical channel state information (CSI), the BD-RIS coefficients matrix is optimized by employing a novel manifold algorithm, and the power control coefficients are then optimized with the objective of maximizing the minimum SE. Through numerical results we illustrate the SE performance of the proposed transmission framework and compare it with that of a conventional MaMIMO transmission for different network settings.

3.Repeated Observations for Classification

Authors:Hüseyin Afşer, László Györfi, Harro Walk

Abstract: We study the problem nonparametric classification with repeated observations. Let $\bX$ be the $d$ dimensional feature vector and let $Y$ denote the label taking values in $\{1,\dots ,M\}$. In contrast to usual setup with large sample size $n$ and relatively low dimension $d$, this paper deals with the situation, when instead of observing a single feature vector $\bX$ we are given $t$ repeated feature vectors $\bV_1,\dots ,\bV_t $. Some simple classification rules are presented such that the conditional error probabilities have exponential convergence rate of convergence as $t\to\infty$. In the analysis, we investigate particular models like robust detection by nominal densities, prototype classification, linear transformation, linear classification, scaling.

4.Fundamental Limits of Reference-Based Sequence Reordering

Authors:Nir Weinberger, Ilan Shomorony

Abstract: The problem of reconstructing a sequence of independent and identically distributed symbols from a set of equal size, consecutive, fragments, as well as a dependent reference sequence, is considered. First, in the regime in which the fragments are relatively long, and typically no fragment appears more than once, the scaling of the failure probability of maximum likelihood reconstruction algorithm is exactly determined for perfect reconstruction and bounded for partial reconstruction. Second, the regime in which the fragments are relatively short and repeating fragments abound is characterized. A trade-off is stated between the fraction of fragments that cannot be adequately reconstructed vs. the distortion level allowed for the reconstruction of each fragment, while still allowing vanishing failure probability

5.Optimized Design of Joint Mirror Array and Liquid Crystal-Based RIS-Aided VLC systems

Authors:Omar Maraqa, Telex M. N. Ngatched

Abstract: Most studies of reflecting intelligent surfaces (RISs)-assisted visible light communication (VLC) systems have focused on the integration of RISs in the channel to combat the line-of-sight (LoS) blockage and to enhance the corresponding achievable data rate. Some recent efforts have investigated the integration of liquid crystal (LC)-RIS in the VLC receiver to also improve the corresponding achievable data rate. To jointly benefit from the previously mentioned appealing capabilities of the RIS technology in both the channel and the receiver, in this work, we propose a novel indoor VLC system that is jointly assisted by a mirror array-based RIS in the channel and an LC-based RIS aided-VLC receiver. To illustrate the performance of the proposed system, a rate maximization problem is formulated, solved, and evaluated. This maximization problem jointly optimizes the roll and yaw angles of the mirror array-based RIS as well as the refractive index of the LC-based RIS VLC receiver. Moreover, this maximization problem considers practical assumptions, such as the presence of non-users blockers in the LoS path between the transmitter-receiver pair and the user's random device orientation (i.e., the user's self-blockage). Due to the non-convexity of the formulated optimization problem, a low-complexity algorithm is utilized to get the global optimal solution. A multi-user scenario of the proposed scheme is also presented. Furthermore, the energy efficiency of the proposed system is also investigated. Simulation results are provided, confirming that the proposed system yields a noteworthy improvement in data rate and energy efficiency performances compared to several baseline schemes.