초저지연 V2X 시스템을 위한 딥러닝 기반 지능형 반사평면 위상 제어 기법 연구

학위논문(석사) -- 서울대학교대학원 : 공과대학 전기·정보공학부, 2022.2. 심병효.Recently, the mission critical vehicle-to-everything (V2X) services, such as safety alarming, remote deriving, and vehicle platooning, play a vital role in the future intel- ligent transportation systems (ITS). To achieve high reliability and low latency V2X services, the intelligent reflecting surface (IRS) has received much attention due to its ability to reconfigure the wireless environment by adjusting the phase of the in- cident signal. By employing IRS, the wireless environment can be improved to meet the reliability and latency requirements of various V2X services. However, finding the optimal solution of the IRS phase shift matrix, multi-vehicle scheduling, and resource allocation are very arduous, due to their joint optimization problem is mixed-integer linear programming. In this paper, we propose a deep learning-based IRS phase shift and power allocation control (D-PPC) scheme, that minimizes the transmission latency while guaranteeing the quality-of-service (QoS). Specifically, we exploit the convolu- tional layer to determine the phase shift and power allocation in consideration of the spatial characteristics of the channel passing through IRS. From the simulation re- sults, we demonstrate that the proposed deep learning-based scheme outperforms the benchmark schemes by a large margin.지능형 교통 시스템의 급속한 발전과 함께 자율주행, 군집 주행 및 안전 경보 서 비스와같은주행안전,주행편의서비스가미래중요기술로주목받고있다.이러한 서비스를 지원하기 위해서는 높은 신뢰도와 낮은 지연시간을 가지는 통신이 매우 중요하다. 다양한 차량 서비스에서 요구하는 신뢰도 조건과 지연시간 조건을 충족 시키는 방법으로 지능형 반사 평면 (IRS) 을 이용하여 무선 통신 환경을 개선하고자 한다. IRS를 적용함으로써 다양한 차량 서비스의 신뢰성 및 지연시간 요구사항을 충족하도록 무선 환경을 개선할 수 있다. 그러나 IRS의 위상변이와 스케줄링, 무선 자원 할당을 동시에 최적화하는 문제는 혼합 정수 선형 계획법 (mixed-integer linear programming)에 해당하여 최적의 해를 찾아내는 데 어려움이 있다. 또한 최적해를 찾아낸다고 해도 이를 실시간으로 결정하지 못한다면 지능형 반사 평면의 성능을 제한하는 요인이 된다. 본 논문에서는 서비스 요구조건을 만족시키면서 전송 지연 시간을 최소화하는 딥러닝 기반 IRS 위상변이 및 기지국 전력 할당을 결정 기법을 제안한다. 심층 신경망 네트워크 내에 사용된 합성곱 신경망은 IRS를 지나는 무선 채널의 공간적인 특성을 고려하여 위상변이와 전력 할당을 결정한다. 실험을 통하 여 제안하는 딥러닝 기반의 위상변이 및 전력 할당 기법이 다른 비교 기법들이 비해 지연시간 성능을 나타냄을 보인다.1 INTRODUCTION 1 2 System Model and Problem Formulation 5 2.1 IRS-Aided V2X Communication System Model 5 2.2 Latency Minimization Problem Formulation 8 3 Deep Learning-Based Phase Shift and Power Control 10 3.1 D-PPC Network Training 11 3.2 D-PPC Network Architecture 12 3.2.1 Phase Shift Network 14 3.2.2 Power Control Network 15 4 Simulation 18 4.1 Simulation Setup 18 4.2 Simulation Result 19 5 Conclusion 24석

Interference in Multi-beam Antenna System of 5G Network

Massive multiple-input-multiple-output (MIMO) and beamforming are key technologies, which significantly influence on increasing effectiveness of emerging fifth-generation (5G) wireless communication systems, especially mobile-cellular networks. In this case, the increasing effectiveness is understood mainly as the growth of network capacity resulting from better diversification of radio resources due to their spatial multiplexing in macro- and micro-cells. However, using the narrow beams in lieu of the hitherto used cell-sector brings occurring interference between the neighboring beams in the massive-MIMO antenna system, especially, when they utilize the same frequency channel. An analysis of this effect is the aim of this paper. In this case, it is based on simulation studies, where a multi-elliptical propagation model and standard 3GPP model are used. We present the impact of direction and width of the neighboring beams of 5G new radio gNodeB base station equipped with the multi-beam antenna system on the interference level between these beams. The simulations are carried out for line-of-sight (LOS) and non-LOS conditions of a typical urban environment

Massive MIMO Performance - TDD Versus FDD: What Do Measurements Say?

Downlink beamforming in Massive MIMO either relies on uplink pilot measurements - exploiting reciprocity and TDD operation, or on the use of a predetermined grid of beams with user equipments reporting their preferred beams, mostly in FDD operation. Massive MIMO in its originally conceived form uses the first strategy, with uplink pilots, whereas there is currently significant commercial interest in the second, grid-of-beams. It has been analytically shown that in isotropic scattering (independent Rayleigh fading) the first approach outperforms the second. Nevertheless there remains controversy regarding their relative performance in practice. In this contribution, the performances of these two strategies are compared using measured channel data at 2.6 GHz.Comment: Submitted to IEEE Transactions on Wireless Communications, 31/Mar/201

Hierarchical Beamforming: Resource Allocation, Fairness and Flow Level Performance

We consider hierarchical beamforming in wireless networks. For a given population of flows, we propose computationally efficient algorithms for fair rate allocation including proportional fairness and max-min fairness. We next propose closed-form formulas for flow level performance, for both elastic (with either proportional fairness and max-min fairness) and streaming traffic. We further assess the performance of hierarchical beamforming using numerical experiments. Since the proposed solutions have low complexity compared to conventional beamforming, our work suggests that hierarchical beamforming is a promising candidate for the implementation of beamforming in future cellular networks.Comment: 34 page

Network Coding for Distributed Antenna Systems

The mushroom growth of devices that require connectivity has led to an increase in the demand for spectrum resources as well as high data rates. 5G has introduced numerous solutions to counter both problems, which are inherently interconnected. Distributed antenna systems (DASs) help in expanding the coverage area of the network by reducing the distance between radio access unit (RAU) and the user equipment. DASs that use multiple-input multiple-output (MIMO) technology allow devices to operate using multiple antennas, which lead to spectrum efficiency. Recently, the concept of virtual MIMO (VMIMO) has gained popularity. VMIMO allows single antenna nodes to cooperate and form a cluster resulting in a transmission flow that corresponds to MIMO technology. In this chapter, we discuss MIMO-assisted DAS and its utility in forming a cooperative network between devices in proximity to enhance spectral efficiency. We further amalgamate VMIMO-assisted DAS and network coding (NC) to quantify end-to-end transmission success. NC is deemed to be particularly helpful in energy constrained environments, where the devices are powered by battery. We conclude by highlighting the utility of NC-based DAS for several applications that involve single antenna empowered sensors or devices