Intelligent reflecting surface (IRS) has been recognized as a transformative technology for enabling smart and reconfigurable radio environment cost-effectively by leveraging its controllable passive reflection. In this paper, we study an IRS-assisted coverage enhancement problem for a given region, aiming to optimize the passive reflection of the IRS for improving the average communication performance in the region by accounting for both deterministic and random channels in the environment. To this end, we first derive the closed-form expression of the average received signal power in terms of the deterministic base station (BS)-IRS-user cascaded channels over all user locations, and propose an IRS-aided coverage enhancement framework to facilitate the estimation of such deterministic channels for IRS passive reflection design. Specifically, to avoid the exorbitant overhead of estimating the cascaded channels at all possible user locations, a location selection method is first proposed to select only a set of typical user locations for channel estimation by exploiting the channel spatial correlation in the region. To estimate the deterministic cascaded channels at the selected user locations, conventional IRS channel estimation methods require additional pilot signals, which not only results in high system training overhead but also may not be compatible with the existing communication protocols. To overcome this issue, we further propose a single-layer neural network (NN)-enabled IRS channel estimation method in this paper, based on only the average received signal power measurements at each selected location corresponding to different IRS random training reflections, which can be offline implemented in current wireless systems. Based on the estimated channels, the IRS passive reflection is then optimized to maximize the average received signal power over the selected locations. Numerical results demonstrate that our proposed scheme can significantly improve the coverage performance of the target region and outperform the existing power-measurement-based IRS reflection designs.

Channel state information (CSI) is essential to the performance optimization of intelligent reflecting surface (IRS)-aided wireless communication systems. However, the passive and frequency-flat reflection of IRS, as well as the high-dimensional IRS-reflected channels, have posed practical challenges for efficient IRS channel estimation, especially in wideband communication systems with significant multi-path channel delay spread. To tackle the above challenge, we propose a novel neural network (NN)-empowered IRS channel estimation and passive reflection design framework for the wideband orthogonal frequency division multiplexing (OFDM) communication system based only on the user’s reference signal received power (RSRP) measurements with time-varying random IRS training reflections. As RSRP is readily accessible in existing communication systems, our proposed channel estimation method does not require additional pilot transmission in IRS-aided wideband communication systems. In particular, we show that the average received signal power over all OFDM subcarriers at the user terminal can be represented as the prediction of a single-layer NN composed of multiple subnetworks with the same structure, such that the autocorrelation matrix of the wideband IRS channel can be recovered as their weights via supervised learning. To exploit the potential sparsity of the channel autocorrelation matrix, a progressive training method is proposed by gradually increasing the number of subnetworks until a desired accuracy is achieved, thus reducing the training complexity. Based on the estimates of IRS channel autocorrelation matrix, the IRS passive reflection is then optimized to maximize the average channel power gain over all subcarriers. Numerical results indicate the effectiveness of the proposed IRS channel autocorrelation matrix estimation and passive reflection design under wideband channels, which can achieve significant performance improvement compared to the existing IRS reflection designs based on user power measurements.

The rapid revolution of mobile communication technology provides a great avenue for efficient information transmission to facilitate digital multimedia services. In current 5G systems, broadcasting technology is used to improve the efficiency of information transmission, and polar codes are adopted to improve data transmission reliability. Reducing the decoding latency of polar codes is of great importance for ultra-low-latency and reliable data transmission for 5G broadcasting, which still remains a challenge in digital broadcasting services. In this paper, we propose an aggregation method to construct constituent codes for reducing the decoding latency of polar codes. The aggregation method jointly exploits the structure and reliability of constituent codes to increase the lengths of constituent codes that can be decoded in parallel, thus significantly reducing the decoding latency. Furthermore, an efficient parallel decoding algorithm is integrated with the proposed aggregation method to efficiently decode the reliable constituent codes without sacrificing error-correction performance. Simulation results show that the proposed method significantly reduces the decoding latency as compared to the existing state-of-the-art schemes.

Channel gain map (CGM) contains crucial large-scale fading information regarding wireless channels at specific frequency bands in wireless networks. Traditional CGM construction methods require either detailed propagation environment information or numerous channel measurements, rendering them practically cumbersome to implement. To overcome such difficulties, we propose in this paper a novel scatterer-based CGM construction framework to characterize the large-scale channel fading in a spatial region by estimating the scatterer responses from a few channel gain measurements at designated locations in the region. In particular, the region is divided into equally-spaced grids so as to smooth out the small-scale fading by averaging the channel power gain within each grid. As the average channel power gain within each grid can be expressed as a function of large-scale multi-path channel parameters such as the scatterer response coefficients and path-loss coefficients, an iterative algorithm is proposed to estimate these parameters based on only a sufficient number of channel gain measurements taken at random locations around each scatterer. Specifically, the proposed algorithm decomposes the parameter estimation problem into a set of univariate or linear estimation subproblems, which can be efficiently solved by one-dimensional (1D) line search and least-squares methods. Based on the estimated channel parameters, the CGM over the whole region can be estimated by calculating the average channel power gain for each grid. Simulation results under both the two-dimensional (2D) multipath channel model and the practical three-dimensional (3D) ray-tracing channel model verify that the proposed scatterer-based channel gain characterization can accurately capture the large-scale channel gain spatial distribution within a region, and the proposed CGM estimation framework outperforms other channel-measurement-based benchmarks in terms of estimation accuracy, while requiring a significantly reduced number of measurements.

In this letter, a simplified successive cancellation decoding algorithm of polar codes is proposed, where a hard decision rule is adopted by exploiting the prior information in frozen bits for better decoding performance. Considering the difference of the node reliability, a hypothesis-testing-based strategy is designed to select reliable unstructured nodes for hard decision. Then the reliable nodes are decoded by the proposed hard decision method without serial recursion, which reduces the decoding latency. Simulation results show the advantages of the proposed method in terms of the decoding latency and error-correction ability compared with the existing methods.

Movable antenna (MA) has been regarded as a promising technology to enhance wireless communication performance by enabling flexible antenna movement. However, the hardware cost of conventional MA systems scales with the number of movable elements due to the need for independently controllable driving components. To reduce hardware cost, we propose in this paper a novel architecture named cross-linked MA (CL-MA) array, which enables the collective movement of multiple antennas in both horizontal and vertical directions. To evaluate the performance benefits of the CL-MA array, we consider an uplink multiuser communication scenario. Specifically, we aim to minimize the total transmit power while satisfying a given minimum rate requirement for each user by jointly optimizing the horizontal and vertical antenna position vectors (APVs), the receive combining at the base station (BS), and the transmit power of users. A globally lower bound on the total transmit power is derived, with closed-form solutions for the APVs obtained under the condition of a single channel path for each user. For the more general case of multiple channel paths, we develop a low-complexity algorithm based on discrete antenna position optimization. Additionally, to further reduce antenna movement overhead, a statistical channel-based antenna position optimization approach is proposed, allowing for unchanged APVs over a long time period. Simulation results demonstrate that the proposed CL-MA schemes significantly outperform conventional fixed-position antenna (FPA) systems and closely approach the theoretical lower bound on the total transmit power. Compared to the instantaneous channel-based CL-MA optimization, the statistical channel-based approach incurs a slight performance loss but achieves significantly lower movement overhead, making it an appealing solution for practical wireless systems.