ASPIRE Workshop 2024
1Beam-Space Expansion with Broad-Range Null-Steering for
V2I Multiuser MIMO Transmission
Yuki SASAKI†a) , Sojin OZAWA† , Kabuto ARAI†† , Student Members, Jin NAKAZATO††† ,Manabu TSUKADA††† , Members, and Kazuki MARUTA† , Senior Member

SUMMARY
This paper proposes a novel inter-user interference (IUI)
suppression approach in multiuser MIMO (MU-MIMO) with time-varying
channel environments. It introduces location based beam-space expansion(BSE) scheme for vehicle-to-infrastructure (V2I) MU-MIMO transmission.
This scheme expands the main lobe of the beam based on vehicle location
information. MU-MIMO enables to enhance spectral efficiency in V2I bymultiplexing a number of user terminals (UTs) in spatial domain. However,UTs move at high speed, which causes IUI. Null-space expansion (NSE)and broad-range null-steering (BRNS) improves IUI suppression capability
by only steering nulls to interfering users. The proposed BSE expands thebeam for the desired user. The performance of NSE, BRNS and BSE arecompared through computer simulation to show the effectiveness of BSE.
key words: V2I, CAVs, Multiuser MIMO, Beam-space expansion

1.

Fig. 1

instant, its acquisition interval, and the elapsed time fromt0 , respectively. We consider MU-MIMO downlink in V2X
with time-varying channel as depicted in Fig. 1. Base station(BS) with Nt antennas simultaneously transmits Nu signal
streams to Nu moving UTs as vehicle with single antenna at
the time instant t0 + τ. The channel matrix H ∈ CNu ×Nt is
composed of individual channel vectors for UT hk ∈ C1×Nt .
In line-of-sight (LoS) propagation environment, the channelcoefficient between the k-th UT and to j-th antenna of BS,
hk j , is given by,!
2πdk jgλhk j =
exp −j,(1)
4πdk jλIntroduction

Coordination of connected autonomated vehicles (CAVs)
through the integration of communications is gaining momentum as part of vehicle-to-everything (V2X) communication [1]. It will require large amounts of sensor information,such as LiDAR, to be distributed via wireless links. Supposing employing millimeter-wave massive array, previous
studies only focused on single-user beamforming. It is necessary to multiple vehicular communications for versatility.
Assuming MU-MIMO downlink, elimination of IUI througha precoding is essential. However, vehicles are moving athigh speeds and thus steered nulls based on channel state
information (CSI) are outdated. NSE has been proposed
as a technique to improve the performance of MU-MIMO
transmission in such a time-varying channel environment by
exploiting past CSI estimates [2]. Suppose a line-of-sight(LoS) channel for moving vehicles, the angular-based NSE;
BRNS is also proposed [3]. This paper additionally proposes BSE that can further enhance tolerance to the mobilityof vehicles with NSE and BRNS.
2.

System structure of multiuser MIMO.

where dk j denotes the geometrical distance, and λ is thewavelength. g stands for the antenna gain. The precoding
weight matrix Wt ∈ CNt ×Nu is written as,hi
Wt = w1 w2 · · · wNu ,(2)
where wk ∈ CNt ×1 is the weight vectors for the k-th UT.
3.

Proposal: Beam-space expansion

BSE is a beam spreading scheme using the vehicle dataobtained from cooperative awareness message (CAM) at
the desired UT. BSE prepares the estimated channels arriving from multiple directions and performs beamforming through maximum-ratio combining. The beamforming
weights aimed at L-desired AoDs are represented by the following equation,

Multiuser MIMO

Throughout the paper, matrices and vectors are written by
boldfaced capital letters and lowercase letters. On the notation of time variables, let t0 , T , and τ denote CSI acquisition†The authors are Graduate School of Engineering, Departmentof Electrical Engineering, Tokyo University of Science, Tokyo,Japan††The author is Advanced Wireless and Communication Research Center, The University of Electro-Communications, Tokyo,Japan†††The authors are Graduate School of Information Science andTechnology, The University of Tokyo, Tokyo, Japana) E-mail: yuki.sasaki@m.ieice.orgĥθϕ =LXh̃θl ϕl .

(3)lNext, we outline a weight generation scheme incorporating BSE and BRNS. This study employs Gram-Schmidt Orthogonalization (GSO) with QR decomposition (QR), which

Copyright © 2023 The Institute of Electronics, Information and Communication Engineers
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ASPIRE Workshop 2024
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Table 1Simulation parameters.

Parameters

ValueFrequency
Transmission bandwidth
Number of BS / UT Antennas
Number of selected UTs
BS/ UT antenna gain
BS transmission power
Symbol duration
Channel model
CPM interval
Feeder loss

28 GHz400 MHz
16 / 148/0 dBi
48 dBm
17.84 µs
LoS
10 ms (5600 symbols) [5]3dB

maintains beam gain at each terminal and requires less computation than traditional GSO. This scheme consists of twosteps: forming nulls for the interfering channel, and second, creating beams for the desired channel. To extend nulls
to the k-th UT in BRNS, we define the extension matrix(Nu −1+Q)×Nt
Hintas,k ∈Ch TTT iTintbrns Tbrns
Hk = H̄k hbrns, (4)k,θ1 ,ϕ1 · · · hk,θ2 ,ϕ2 · · · Hk,θQ ,ϕQhiTH̄k = h1 · · · hk−1 hk+1 · · · hNu.
(5)

Fig. 2

Simulation environment.

Fig. 3

CDF of received SINR.

The null formation weight Vk ∈ CNt ×Nt for the augmented
matrix Hint
k is expressed using the orthogonal matrix Q ∈CNt ×(Nu −1+Q) , derived from QR decomposition of Hint
k , asshown in the following equation,
Vk = INt ×Nt − Qk QkH .

(6)

The designed null formation weight Vk ensures that Hintk V=O, fullfills the orthogonalization condition of BRNS. Beamforming weights are also determined using (3) Based on this,the transmit weight wt,k is expressed as follows,wt,k =4.

(ĥk Vk )H||(ĥk Vk )H ||.

scheme and demonstrated its effectiveness through computer
simulation.

(7)

Acknowledgments

Computer simulation

This research is supported in part by the Strategic Information and Communications R&D Promotion Programme(SCOPE) by Ministry of Internal Affairs and Communications, Japan (receipt number JP235003002), in part by JST
ASPIRE Grant Number JPMJAP2325, Japan.

Effectiveness of the proposed scheme is examined througha joint implementation using MATLAB and Simulation ofUrban Mobility (SUMO) [4]. MATLAB has the role ofbeamforming and calculation of SINR as the evaluation
metric in multiuser MIMO. SUMO is applied to simulate
realistic vehicle movement and provide essential vehicle information, such as angles and positions, required for theproposed scheme. Simulation parameters are summarizedin Table 1. We assume the BS is equipped with linear arrayin two-dimensional space as shown in Fig. 2. Fig. 3 shows
the cumulative distribution functions (CDF) of SINR. The
proposed scheme combining BSE and BRNS exhibits thehighest SINR in the region where the CDF is approximately
50% or higher. Although the performance is partly inferior
in the low CDF region, this is due to the fact that the smallest
speed of the vehicle in this urban environment is about 0km/h and the change in AoD is slight.
5.

References[1] A. Yu et al., “Autowarev2x: Reliable v2x communication and collective perception for autonomous driving,” 2023 IEEE 97th VehicularTechnology Conference (VTC2023-Spring), pp.1–7, 2023.
[2] T. Iwakuni et al., “Null-space expansion for multiuser massive MIMO
inter-user interference suppression in time varying channels,” IEICE
Trans. Commun., vol.E100.B, no.5, pp.865–873, 2017.
[3] Y. Sasaki, S. Ozawa, K. Arai, J. Nakazato, M. Tsukada, and K. Maruta,“Location-based broad-range null-steering in V2X multiuser MIMO
transmission,” IEEE Consumer Communications Networking Conference (CCNC2024), January 2024.
[4] P.A. Lopez, et al., “Microscopic traffic simulation using SUMO,” 2018
21st International Conference on Intelligent Transportation Systems(ITSC), pp.2575–2582, 2018.
[5] ETSI, “Intelligent transport system (ITS); vehicular communications;basic set of applications; collective perception service; release 2,”
Tech. Rep. TS 103 324, jun 2023.

Conclusion

This paper proposed an angular-based beam-space expansion
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