arXiv subjects

Songjie Yang

Publications and source records attributed to Songjie Yang.

42 records · Page 3Linked to original sources

arXiv2022

Bayesian Optimization-Based Beam Alignment for MmWave MIMO Communication Systems

Due to the very narrow beam used in millimeter wave communication (mmWave), beam alignment (BA) is a critical issue. In this work, we investigate the issue of mmWave BA and present a novel beam alignment scheme on the basis of a machine learning strategy, Bayesian optimization (BO). In this context, we consider the beam alignment issue to be a black box function and then use BO to find the possible optimal beam pair. During the BA procedure, this strategy exploits information from the measured beam pairs to predict the best beam pair. In addition, we suggest a novel BO algorithm based on the gradient boosting regression tree model. The simulation results demonstrate the spectral efficiency performance of our proposed schemes for BA using three different surrogate models. They also demonstrate that the proposed schemes can achieve spectral efficiency with a small overhead when compared to the orthogonal match pursuit (OMP) algorithm and the Thompson sampling-based multi-armed bandit (TS-MAB) method.

eess.SP↗

arXiv2022

Channel Estimation for Reconfigurable Intelligent Surface-Assisted Cell-Free Communications

Recent research has focused on reconfigurable intelligent surface (RIS)-assisted cell-free systems with the goal of enhancing coverage and lowering the cost of cell-free networks. However, current research makes the assumption that the perfect channel state information is known. Channel acquisition is, certainly, a difficulty in this case. This work is aimed at investigating RIS-assisted cell-free channel estimation. Toward this end, two unique characteristics are pointed out: 1) For all users, a common channel exists between the base station (BS) and the RIS; and 2) For all BSs, a common channel exists between the RIS and the user. Based on these two characteristics, cascaded and two-timescale channel estimation concerns are studied. Subsequently, two solutions for tackling with the two issues are presented respectively: a three-dimensional multiple measurement vector (3D-MMV)-based compressive sensing technique and a multi-BS cooperative pilot-reduced methodology. Finally, simulations illustrate the effectiveness of the schemes we have presented.

eess.SP↗

arXiv2022

Low-complexity Sparse Array Synthesis Based on Off-grid Compressive Sensing

A novel sparse array synthesis method for non-uniform planar arrays is proposed, which belongs to compressive sensing (CS)-based systhesis. Particularly, we propose an off-grid refinement technique to simultaneously optimize the antenna element positions and excitations with a low complexity, in response to the antenna position optimization problem that is difficult for standard CS. More importantly, we take into account the minimum inter-element spacing constraint for ensuring the physically realizable solution. Specifically, the off-grid Orthogonal Match Pursuit (OMP) algorithm is first proposed with low complexity and then off-grid Look Ahead Orthogonal Match Pursuit (LAOMP) is designed with better synthesis performance but higher complexity. In addition, simulation results have shown the proposed schemes have more advantages in computational complexity and synthesis performances compared with the related method.

eess.SP↗

arXiv2022

Weighted Sum Age of Information Minimization in Wireless Networks with Aerial IRS

In this letter, we analyze a terrestrial wireless communication network assisted by an aerial intelligent reflecting surface (IRS). We consider a packet scheduling problem at the ground base station (BS) aimed at improving the information freshness by selecting packets based on their AoI. To further improve the communication quality, the trajectory of the unmanned aerial vehicle (UAV) which carries the IRS is optimized with joint active and passive beamforming design. To solve the formulated non-convex problem, we propose an iterative alternating optimization problem based on a successive convex approximation (SCA) algorithm. The simulation results shows significant performance improvement in terms of weighted sum AoI, and the SCA solution converges quickly with low computational complexity.

eess.SP↗

arXiv2021

An Off-grid Compressive Sensing Strategy for the Subarray Synthesis of Non-uniform Linear Arrays

With the increasing popularity of large-scale antenna arrays, the subarraying technology becomes more attractive. In this paper, we propose two effective subarraying methods right after formulating the subarray synthesis as a compressive sensing (CS) problem: i) Orthogonal matching pursuit based subarray synthesis (OMP-SS), a common CS approach which can be used for the subarray synthesis to attain the subarray information (the subarray number, the number of elements per subarray and corresponding excitation coeffcients) and ii) Off-grid orthogonal matching pursuit based subarray synthesis (OGOMP-SS), an advanced approach for optimizing antenna elements positions and the subarray information mentioned above simultaneously. In addition, two user-defined modes are designed for different application scenarios, wherein, mode-1 is to optimize the pattern synthesis performance for the given the number of subarrays, and mode-2 is to obtain the minimum number of subarrays for the cases when the pattern synthsis accuracy is satisfied. Finally, our simulation results reveal that it is of paramount significance to optimize antenna elements positions for the subarray synthesis performance on the one hand and demonstrate the excellent performances of proposed schemes in comparison with other competitive state-of-the-art subarray synthesis methods on the other hand.

eess.SP↗

arXiv2021

Efficient Multi-Beam Training For Terahertz Wireless communications

Although Terahertz communication systems can provide high data rates, it needs high directional beamforming at transmitters and receivers to achieve such rates over a long distance. Therefore, an efficient beam training method is vital to accelerate the link establishment. In this study, we propose a low-complexity beam training scheme of terahertz communication system which uses a low-cost small-scale hybrid architecture to assist a large-scale array for data transmission. The proposed scheme includes two key stages: (1) coarse AoAs/AoDs estimation for beam subset optimization in auxiliary array stage, and (2) accurate AoAs/AoDs estimation by exploiting channel sparsity in data transmission array stage. The analysis shows that the complexity of the scheme is linear with the number of main paths, and thus greatly reduces the complexity of beam training. Simulation results have verified the better performance in spectral efficiency of the proposed scheme than that of the related work.

cs.IT↗