Periodic Reporting for period 1 - FUTURE-MOBILE (Distributed Massive MIMO for Next Generation Wireless Communications) Période du rapport: 2016-10-01 au 2018-09-30 Résumé du contexte et des objectifs généraux du projet Future mobile networks will have to support a multitude of new applications with a wide variety of requirements, including high peak data rates (i.e. 50 Gbps for low mobility and 1 Gbps for high mobility), a 1000 times more traffic, high spectral efficiency and so on. In the proposed distributed massive MIMO architecture, massive geographically remote radio heads (RRHs) equipped with compact massive MIMO arrays with low hardware front-end complexity are dispersed over a coverage area and connected to a central unit (CU) via a radio over fibre high capacity low latency fronthaul. The main objectives of this proposal are: 1) To develop a unified theoretical framework for the fundamental limits of distributed massive MIMO under various practical constraints; 2) To investigate characters of transmit signals, and design spectral/energy efficient beamforming algorithms; 3) To design distributed RRH-based caching techniques for reducing downloading delay with optimization and consideration of capacity constraints on the fronthaul; 4) To setup the testbed and carry out experimental evaluation for the proposed solutions. Travail effectué depuis le début du projet jusqu’à la fin de la période considérée dans le rapport et principaux résultats atteints jusqu’à présent The following tasks were carried out:(1) In Task 1.1 a basic architecture and topology of the distributed massive MIMO network was agreed using particular use-case scenarios. (2) Task 1.2 was dedicated to investigate signal transport mechanisms.(3) Task 1.3 focused on characterising the fundamental limits of distributed massive MIMO architectures for single cell scenario.(4) Task 1.4 analysed the fundamental limits of distributed massive MIMO architectures for multiple cells scenarios.(5) Task 2.1 was dedicated to defining user clusters and antenna selection methods in terms of instantaneous received power in order to reduce feedback overhead.(6) Task2.2 investigated the prefixed precoding/beam searching distributed massive MIMO systems for high-speed train communications.(7) Task 2.3 investigated the precoding design and interference mitigation for distributed massive MIMO systems coexisting with satellite networks.(8) Task 3.1 focused on studying the distributed file downloading problem and development of distributed caching algorithm to enhance download performance.(9) In Task 3.2 the caching performance was analysed to obtain insightful understanding of the caching algorithm.(10) Task 4.1 was to provide experimental verification of the error performance and throughput enhancements of key techniques.The main achieved results are listed as follows:(1) Tasks 1.1 proposed a framework of the distributed massive MIMO network. Under the framework, technical specifications, technological boundaries and set of realistic technical use-cases were devised.(2) Task 1.2 investigated signal transport mechanisms. The effect on latency/jitter, availability of signalling bandwidth, and impact on user data traffic were studied. (3) Task 1.3 investigated the fundamental limits of distributed massive MIMO systems. The coverage performance was analysed for distributed massive MIMO systems using millimeter wave (mmWave) band signals with imperfect beam alignment.(4) Task 1.4 extended the analyses of fundamental limits to multiple cells scenarios. Tractable analytical expressions were obtained for the coverage probability and ergodic rate of distributed massive MIMO systems.(5) Task 2.1 defined user clusters and antenna selection methods to reduce feedback overhead. Moreover, a randomized algorithm was proposed to minimize the number of possible handovers between different MEC regions by carefully dividing a metropolitan area into disjoint cluster.(6) By exploiting the historic information of propagation channels, Task 2.3 proposed a bandit inspired beam searching scheme to accelerate the prefixed precoding/beam searching process of channel estimation.(7) Task 2.4 investigated distributed massive MIMO systems coexisting with satellite networks. Spectral/energy efficient beamforming algorithms were proposed to maximize the secrecy rate with the rate and power consumption constraints.(8) Task 3.1 examined the effect of dynamic association between RRH and user equipment (UE) and the impact of channel conditions. (9) Task 3.2 considered the transmission scheduling of delay-sensitive data over Rayleigh fading channels with imperfect channel state information (CSI), the buffer state, the transmit power, and the modulation and coding schemes are optimized to maximize the energy efficiency, and minimize the transmission delay and overflow probability jointly.(10) In Task 4.1 a testbed was set up study the impact of practical effects radio propagation on the proposed solutions. Progrès au-delà de l’état des connaissances et impact potentiel prévu (y compris l’impact socio-économique et les conséquences sociétales plus larges du projet jusqu’à présent) In FUTURE-MOBILE, an innovative concept of distributed massive MIMO and a generic analytical framework was introduced to ensure that the optimal resource allocation methods and beamforming schemes can be developed with the consideration of transmission rate requirements and power constraints. During the two-year project time, FUTURE-MOBILE has: 1) established as novel framework for developing practical precoding schemes with high spectrum and energy efficiencies for distributed massive MIMO communications; 2) developed practical beamforming schemes and resource allocation methods for distributed massive MIMO communications; 3) investigated users clusters and antenna selection methods, and proposed spectral/energy efficient beamforming schemes; 4) investigated RRH content caching, analysed the caching performance, and developed the optimal schemes to improve the downloading performance; 5) proposed machine learning based beamforming schemes and resource allocation method for distributed massive MIMO systems; 6) demonstrated the feasibility of distributed massive MIMO in future mobile communications. The results can be applied to provide high QoS for mobile networking.During the project period, Dr Wang has visited several operators and technology companies, including China Mobile, Huawei and ZTE. They have shown great interest in adopting distributed massive MIMO communications into their future technical development. Hence, the research work carried out in FUTURE-MOBILE is of great importance for the valuable market of mobile networking.The FUTURE-MOBILE project has also contributed significantly to the career development for the fellow, Dr Wang, through the following successful trainings: 1) Training in research areas has enhanced the fellow’s skills in data transmission via distributed massive MIMO communications, and testbed development for the experimentation of mobile communications. 2) Training in transferable skills also benefit the fellow significantly. During the project period, she has delivered several talks and participated in two industrial workshops and two international conference, and made visit to several key technology companies in related field and operators. He is also currently supervising three PhD students. As a major participant, he has successful applied for one major project from National Natural Science Foundation of China, entitled “Millimetre-Wave Massive MIMO Transmission Technologies for Wide-Area Mobile Networks” , and one EU Horizon 2020 ICT-22-2018 EU-China 5G Collaboration project, entitled “5G HarmoniseD Research and TrIals for serVice Evolution between EU and China (5G-DRIVE).” das.jpg
