Prototyping and Experimental Results for Environment-Aware Millimeter Wave Beam Alignment via Channel Knowledge Map

Channel knowledge map (CKM), which aims to directly reflect the intrinsic channel properties of the local wireless environment, is a novel technique for achieving environmentaware communication. In this paper, to alleviate the large training overhead in millimeter wave (mmWave) beam alignment, an environment-aware and training-free beam alignment prototype is established based on a typical CKM, termed beam index map (BIM). To this end, a general CKM construction method is first presented, and an indoor BIM is constructed offline to learn the candidate transmit and receive beam index pairs for each grid in the experimental area. Furthermore, based on the location information of the receiver (or the dynamic obstacles) from the ultra-wide band (UWB) positioning system, the established BIM is used to achieve training-free beam alignment by directly providing the beam indexes for the transmitter and receiver. Three typical scenarios are considered in the experiment, including quasi-static environment with line-of-sight (LoS) link, quasistatic environment without LoS link and dynamic environment. Besides, the receiver orientation measured from the gyroscope is also used to help CKM predict more accurate beam indexes. The experiment results show that compared with the benchmark location-based beam alignment strategy, the CKM-based beam alignment strategy can achieve much higher received power, which is close to that achieved by exhaustive beam search, but with significantly reduced training overhead.

Channel Estimation for Delay Alignment Modulation

Delay alignment modulation (DAM) is a promising technology to eliminate inter-symbol interference (ISI) without relying on sophisticated equalization or multi-carrier transmissions. The key ideas of DAM are delay pre-compensation and path based beamforming, so that the multi-path signal components will arrive at the receiver simultaneously and constructively, rather than causing the detrimental ISI. However, the practical implementation of DAM requires channel state information (CSI) at the transmitter side. Therefore, in this letter, we propose an efficient channel estimation method for DAM based on block orthogonal matching pursuit (BOMP) algorithm, by exploiting the block sparsity of the channel impulse response (CIR) vector. Based on the imperfectly estimated CSI, the delay pre-compensations and path-based beamforming are designed for DAM, and the resulting performance is studied. Simulation results demonstrate that with the proposed channel estimation method, the CSI can be effectively acquired with low training overhead, and the performance of DAM based on estimated CSI is comparable to the ideal case with perfect CSI.

Environment-Aware Beam Selection for IRS-Aided Communication with Channel Knowledge Map

Intelligent reflecting surface (IRS)-aided communication is a promising technology for beyond 5G (B5G) systems, to reconfigure the radio environment proactively. However, IRS-aided communication in practice requires efficient channel estimation or passive beam training, whose overhead and complexity increase drastically with the number of reflecting elements/beam directions. To tackle this challenge, we propose in this paper a novel environment-aware joint active and passive beam selection scheme for IRS-aided wireless communication, based on the new concept of channel knowledge map (CKM). Specifically, by utilizing both the location information of the user equipment (UE), which is readily available in contemporary wireless systems with ever-increasing accuracy, and the environment information offered by CKM, the proposed scheme achieves efficient beam selection with either no real-time training required (training-free beam selection) or only moderate training overhead (light-training beam selection). Numerical results based on practical channels obtained using commercial ray tracing software are presented, which demonstrate the superior performance of the proposed scheme over various benchmark schemes.