PHY6  – Massive and ultra-massive MIMO

Wednesday, 7 June 2023, 16:00-17:30, Room J1

Session Chair: Han Yu (Chalmers University of Technology, Sweden)

High-Resolution Channel Sounding and Parameter Estimation in Multi-Site Cellular Networks
Junshi Chen (Lund University & Terranet AB, Sweden); Russ Whiton, Xuhong Li and Fredrik Tufvesson (Lund University, Sweden)
Understanding of electromagnetic propagation properties in real environments is necessary for efficient design and deployment of cellular systems. In this paper, we show a method to estimate high-resolution channel parameters with a massive antenna array in real network deployments. An antenna array mounted on a vehicle is used to receive downlink long-term evolution (LTE) reference signals from neighboring base stations (BS) with mutual interference. Delay and angular information of multipath components is estimated with a novel inter-cell interference cancellation algorithm and an extension of the RIMAX algorithm. The estimated high-resolution channel parameters are consistent with the movement pattern of the vehicle and the geometry of the environment and allow for refined channel modeling and precise cellular positioning.

Performance Analysis of Centralized and Distributed Massive MIMO for MTC
Eduardo Noboro Tominaga, Onel L. A. López and Hirley Alves (University of Oulu, Finland); Richard Demo Souza (Federal University of Santa Catarina, Brazil); Leonardo Terças (University of Oulu, Finland)
Massive Multiple-Input Multiple-Output (mMIMO) is one of the essential technologies introduced by the Fifth Generation (5G) of wireless communication systems. However, although mMIMO provides many benefits for wireless communications, it cannot ensure uniform wireless coverage and suffers from inter-cell interference inherent to the traditional cellular network paradigm. Therefore, industry and academia are working on the evolution from conventional Centralized mMIMO (CmMIMO) to Distributed mMIMO (DmMIMO) architectures for the Sixth Generation (6G) of wireless networks. Under this new paradigm, several Access Points (APs) are distributed in the coverage area, and all jointly cooperate to serve the active devices. Aiming at Machine-Type Communication (MTC) use cases, we compare the performance of CmMIMO and different DmMIMO deployments in an indoor industrial scenario considering regular and alarm traffic patterns for MTC. Our simulation results show that DmMIMO’s performance is often superior to CmMIMO. However, the traditional CmMIMO can outperform DmMIMO when the devices’ channels are highly correlated.

Antenna Array Structures for Enhanced Cluster Index Modulation
Mahmoud Raeisi (Koc University, Turkey); Asil Koc (McGill University, Canada); Ibrahim Yildirim (Istanbul Technical University, Turkey); Ertugrul Basar (Koc University, Turkey); Tho Le-Ngoc (McGill University, Canada)
This paper investigates the effect of various antenna array structures, i.e., uniform linear array (ULA), uniform rectangular array (URA), uniform circular array (UCA), and concentric circular array (CCA), on cluster index modulation (CIM) enabled massive multiple-input multiple-output (mMIMO) millimeter-wave (mmWave) communications systems. As the CIM technique indexes spatial clusters to convey additional information bits, the different radiation characteristics caused by different array structures can significantly affect system performance. By analyzing the effects of array characteristics such as radiation pattern, array directivity, half-power beam width (HPBW), and radiation side lobes on bit error rate (BER) performance, we reveal that URA achieves better error performance than its counterparts in a CIM-enabled mmWave system. We demonstrate that narrower beams alone cannot guarantee better BER performance in a CIM-based system. Instead, other radiation characteristics, especially radiation side lobes, can significantly influence system performance by entailing extra interference in the non-intended directions. Illustrative results show that URA owes its superiority to its lower side lobes. We also propose an algorithm to implement fixed phase shifters (FPS) as a hardware- efficient (HE) analog network structure (beamformer/combiner) to reduce cost and energy consumption in mmWave systems and investigate the effect of a non-ideal analog network on the BER performance for different array structures. It is demonstrated that HE systems with a few FPSs can achieve similar BER performance compared to the optimum (OP) analog network structure.

Misfocus-Reduction in RIS-Assisted Ultra-Wideband Wireless Communication
Zeyu Huang, Richard Prüller, Stefan Schwarz and Markus Rupp (TU Wien, Austria)
Misfocus or beam squint, is a fundamental problem for reconfigurable intelligent surface (RIS)-assisted beamforming. In this work, we investigate two methods for misfocusreduction based on the stationary phase method (SPM) and the semi-definite relaxation (SDR), respectively. The proposed algorithms can provide a misfocus robust phase shift configuration. In the SPM-based algorithm, a closed-form expression is given, which reduces the computational complexity and has high efficiency when the RIS elements number is large. We use the circuit model to characterize the frequency selectivity of RIS elements. We investigate our methods under several critical factors: the number of RIS elements, the beamfoming bandwidth and the impact of the frequency selectivity of RIS itself.

Largest Generalized Eigenvector Precoder for CoMP-JT Massive MIMO Systems
Xianglong Yu (Huawei Technologies CO., Ltd, China); Hanqing Wang (Huawei Technologies Co., Ltd., China); Yiling Yuan (Huawei Technologies, China); Xiaohan Wang (Huawei Technologies Co., Ltd., China); Hao Chen (Peng Cheng Laboratory, China)
In this paper, we investigate the downlink (DL) precoder design in coordinated multi-point joint transmission massive multiple-input multiple- output systems. The DL precoder design problem is formulate to maximize the sum-rate under the per base station transmit power constraint. Utilizing the firstorder condition, the structure of the optimal precoder is derived, involving the generalized eigenvectors of a pair of matrices. In accordance with this, the largest generalized eigenvector (LGEV) precoder is proposed to solve the first-order condition in an iterative manner, which involves solving the complicated generalized eigenvalue problem in each iteration. Specifically, we propose to solve the eigenvalue problem numerically for lowcomplexity implementation based on the inverse free Krylov subspace method. Simulation results demonstrate that the proposed LGEV precoder achieves satisfactory performances with fast convergences within a couple of iterations.

Go to Top