Computer Science

This paper proposes a new algorithm, named Householder Dice (HD), for simulating dynamics on dense random matrix ensembles with translation-invariant properties. Examples include the Gaussian ensemble, the Haar-distributed random orthogonal ensemble, and their complex-valued counterparts. A "direct" approach to the simulation, where one first generates a dense n?n matrix from the ensemble, requires at least O( n 2 ) resource in space and time. The HD algorithm overcomes this O( n 2 ) bottleneck by using the principle of deferred decisions: rather than fixing the entire random matrix in advance, it lets the randomness unfold with the dynamics. At the heart of this matrix-free algorithm is an adaptive and recursive construction of (random) Householder reflectors. These orthogonal transformations exploit the group symmetry of the matrix ensembles, while simultaneously maintaining the statistical correlations induced by the dynamics. The memory and computation costs of the HD algorithm are O(nT) and O(n T 2 ) , respectively, with T being the number of iterations. When T?�n , which is nearly always the case in practice, the new algorithm leads to significant reductions in runtime and memory footprint. Numerical results demonstrate the promise of the HD algorithm as a new computational tool in the study of high-dimensional random systems.

Large MIMO transceivers are integral components of next-generation wireless networks. However, for such systems to be practical, their channel estimation process needs to be fast and reliable. Although several solutions for fast estimation of sparse channels do exist, there is still a gap in understanding the fundamental limits governing this problem. Specifically, we need to better understand the lower bound on the number of measurements under which accurate channel estimates can be obtained. This work bridges that knowledge gap by deriving a tight asymptotic lower bound on the number of measurements. This not only helps develop a better understanding for the sparse MIMO channel estimation problem, but it also provides a benchmark for evaluating current and future solutions.

We investigate how the type of Convexity of the Core function affects the Csiszár f -divergence functional. A general treatment for the type of convexity has been considered and the associated perspective functions have been studied. In particular, it has been shown that when the core function is \rm{MN}-convex, then the associated perspective function is jointly \rm{MN}-convex if the two scalar mean \rm{M} and \rm{N} are the same. In the case where M?�N , we study the type of convexity of the perspective function. As an application, we prove that the \textit{Hellinger distance} is jointly \rm{GG}-convex. As further applications, the matrix Jensen inequality has been developed for the perspective functions under different kinds of convexity.

Terahertz (THz) communications are regarded as a pillar technology for the sixth generation (6G) wireless systems, by offering multi-ten-GHz bandwidth. To overcome the short transmission distance and huge propagation loss, ultra-massive (UM) MIMO systems that employ sub-millimeter wavelength antennas array are proposed to enable an enticingly high array gain. In the UM-MIMO systems, hybrid beamforming stands out for its great potential in promisingly high data rate and reduced power consumption. In this paper, challenges and features of the THz hybrid beamforming design are investigated, in light of the distinctive THz peculiarities. Specifically, we demonstrate that the spatial degree-of-freedom (SDoF) is less than 5, which is caused by the extreme sparsity of the THz channel. The blockage problem caused by the huge reflection and scattering losses, as high as 15 dB or over, is studied. Moreover, we analyze the challenges led by the array containing 1024 or more antennas, including the requirement for intelligent subarray architecture, strict energy efficiency, and propagation characterization based on spherical-wave propagation mechanisms. Owning up to hundreds of GHz bandwidth, beam squint effect could cause over 5~dB array gain loss, when the fractional bandwidth exceeds 10%. Inspired by these facts, three novel THz-specific hybrid beamforming architectures are presented, including widely-spaced multi-subarray, dynamic array-of-subarrays, and true-time-delay-based architectures. We also demonstrate the potential data rate, power consumption, and array gain capabilities for THz communications. As a roadmap of THz hybrid beamforming design, multiple open problems and potential research directions are elaborated.

Intelligent reflecting surface (IRS) is a promising technology to improve the performance of wireless powered communication networks (WPCNs) due to its capability to reconfigure signal propagation environments via smart reflection. In particular, the high passive beamforming gain promised by IRS can significantly enhance the efficiency of both downlink wireless power transfer (DL WPT) and uplink wireless information transmission (UL WIT) in WPCNs. Although adopting different IRS phase shifts for DL WPT and UL WIT, i.e., dynamic IRS beamforming, is in principle possible but incurs additional signaling overhead and computational complexity, it is an open problem whether it is actually beneficial. To answer this question, we consider an IRS-assisted WPCN where multiple devices employ a hybrid access point (HAP) to first harvest energy and then transmit information using non-orthogonal multiple access (NOMA). Specifically, we aim to maximize the sum throughput of all devices by jointly optimizing the IRS phase shifts and the resource allocation. To this end, we first prove that dynamic IRS beamforming is not needed for the considered system, which helps reduce the number of IRS phase shifts to be optimized. Then, we propose both joint and alternating optimization based algorithms to solve the resulting problem. Simulation results demonstrate the effectiveness of our proposed designs over benchmark schemes and also provide useful insights into the importance of IRS for realizing spectrally and energy efficient WPCNs.

In this article, we overview intelligent reflecting surface (IRS)-empowered wireless communication systems. We first present the fundamentals of IRS-assisted wireless transmission. On this basis, we explore the integration of IRS with various advanced transmission technologies, such as millimeter wave, non-orthogonal multiple access, and physical layer security. Following this, we discuss the effects of hardware impairments and imperfect channel-state-information on the IRS system performance. Finally, we highlight several open issues to be addressed.

We analyze message identification via Gaussian channels with noiseless feedback, which is part of the Post Shannon theory. The consideration of communication systems beyond Shannon's approach is useful to increase the efficiency of information transmission for certain applications. We consider the Gaussian channel with feedback. If the noise variance is positive, we propose a coding scheme that generates infinite common randomness between the sender and the receiver and show that any rate for identification via the Gaussian channel with noiseless feedback can be achieved. The remarkable result is that this applies to both rate definitions 1 n logM (as Shannon defined it for the transmission) and 1 n loglogM (as defined by Ahlswede and Dueck for identification). We can even show that our result holds regardless of the selected scaling for the rate.

Intelligent entities such as self-driving vehicles, with their data being processed by machine learning units (MLU), are developing into an intertwined part of networks. These units handle distorted input but their sensitivity to noisy observations varies for different input attributes. Since blind transport of massive data burdens the system, identifying and delivering relevant information to MLUs leads in improved system performance and efficient resource utilization. Here, we study the integer bit allocation problem for quantizing multiple correlated sources providing input of a MLU with a bandwidth constraint. Unlike conventional distance measures between original and quantized input attributes, a new Kullback-Leibler divergence based distortion measure is defined to account for accuracy of MLU decisions. The proposed criterion is applicable to many practical cases with no prior knowledge on data statistics and independent of selected MLU instance. Here, we examine an inverted pendulum on a cart with a neural network controller assuming scalar quantization. Simulation results present a significant performance gain, particularly for regions with smaller available bandwidth. Furthermore, the pattern of successful rate allocations demonstrates higher relevancy of some features for the MLU and the need to quantize them with higher accuracy.

This paper investigates the impacts of Inter-Channel Interference (ICI) effects on a shallow underwater acoustic (UWA) orthogonal frequency-division multiplexing (OFDM) communication system. Considering both the turbulence of the water surface and the roughness of the bottom, a stochastic geometry-based channel model utilized for a wide-band transmission scenario has been exploited to derive a simulation model. Since the system bandwidth and the sub-carrier spacing is very limited in the range of a few kHz, the channel capacity of a UWA system is severely suffered by the ICI effect. For further investigation, we construct the signal-to-noise-plus-interference ratio (SINR) based on the simulation model, then evaluate the channel capacity. Numerical results show that the various factors of a UWA-OFDM system as subcarriers, bandwidth, and OFDM symbols affect the channel capacity under the different Doppler frequencies. Those observations give hints to select the good parameters for UWA-OFDM systems.

In this paper, we consider the distributed mean estimation problem where the server has access to some side information, e.g., its local computed mean estimation or the received information sent by the distributed clients at the previous iterations. We propose a practical and efficient estimator based on an r-bit Wynzer-Ziv estimator proposed by Mayekar et al., which requires no probabilistic assumption on the data. Unlike Mayekar's work which only utilizes side information at the server, our scheme jointly exploits the correlation between clients' data and server' s side information, and also between data of different clients. We derive an upper bound of the estimation error of the proposed estimator. Based on this upper bound, we provide two algorithms on how to choose input parameters for the estimator. Finally, parameter regions in which our estimator is better than the previous one are characterized.