Research On Energy-saving Multicast In Wireless Communication Networks

Currently, the energy and environment problem is becoming increasingly serious around the world, and the energy consumption from information and communication industry is increasing year by year. Hence, the green com-munication has been acknowledged as the inevitable development tendency of future networks. Meanwhile, along with the improvement of mobile commu-nication technologies and the popularity of smart devices, multi-media service is becoming more and more prosperous, serving as the most important driver for the dramatic increase of mobile traffic. As the main bearing technology for multi-media service, multicast has become one indispensable part of wireless communication networks. As a result, it is more urgent to study the energy con-sumption problem for multicast, and the research of energy-saving multicast is of great significance, contributing a lot to improving both transmission rate and energy efficiency for multicast service.This dissertation investigates and summarizes the research status of multi-cast and green communications, based on which, the overall research outline is formed: by extending the available resources for multicast transmission in mul-tiple dimensions, including spectrum bands, spatial degrees of freedom (DoFs)and energy supply, we can greatly alleviate the contradiction between two goals,i.e., the increase of transmission rate and the decrease of power consumption,and hence result in a better trade-off. The detailed research contents are de-scribed as follows.In the first part, the extension of available spectrum resources is discussed,and energy-saving multicast in cognitive radio networks (CRNs) is studied.We derive the closed-form expressions for the outage probability constraint on secondary users (SUs) and over-interference probability constraint on primary users (PUs), and propose two-step optimization algorithm and semi-definite re-laxation (SDR)/penalty function (PenFun)-based algorithms to implement re-source allocation for coded multicast and cooperative multicast on unlicensed bands. With regard to coded multicast, the spectrum sensing errors and channel feedback errors are considered, based on which the long-term interference con-straints for PUs and the services quality maximization problem are established.Then the two-step algorithm is proposed to optimize the subcarrier/power al-location and rate selection. Compared with existing researches, the proposed algorithm provides better control on the average interference on PUs and greatly improves the total transmission quality of multicast services. With regard to co-operative multicast, the channel feedback error is considered, based on which the outage probability constraint on SUs and over-interference probability con-straint on PUs are established. Then the closed-form expressions for these two constraints and the optimal structure for forwarding matrix at relays are de-rived, and the SDR-based/PenFun-based algorithms are proposed for problem solving. Compared with existing researches, the proposed scheme is able to guarantee the normal communication of PUs and successful transmission of multicast services, and meanwhile reduce the total power consumption greatly.In the second part, the extension of spatial DoFs is discussed, and energy-saving multicast in massive multi-input-multi-output (Massive MIMO) systems is studied. We derive the average user transmission rate in the presence of hardware impairments and the gradually optimal precoding vector for multi-cast services, and propose alternative optimization (AO)-based algorithm and bisection-search (BS)-based algorithm to achieve the optimal parameter selec-tion. In detail, we first consider the unicast transmission, and derive the chan-nel estimation model and average user rate when hardware impairments exist,following which the AO-based algorithm is proposed to design the optimal pa-rameter selection strategy for Massive MIMO systems with hardware impair-ments. Through numerical simulations, the correctness of derived rate expres-sions is verified and the superiority of the proposed algorithm is also illustrated.Then we return back to multicast transmission, model the available transmission rate and system power consumption, and derive the closed-form expression for gradually optimal precoding vectors, following which the BS-based algorithm is proposed to optimize the parameter selection. Simulation results verify the proposed gradually optimal beamforming vectors are reasonable, and illustrate the optimal parameter settings for different cell radiuses and different numbers of multicast users.In the final part, the extension of energy supply is discussed, and energy-saving multicast in radio frequency (RF) energy harvesting systems is studied.We establish the independent power splitting (PS) model for multi-antenna re-ceivers, and propose a low-complexity bi-level iteration algorithm for robust and secure multicast transmission in RF energy harvesting systems. In detail,we first study the receiver-side signal processing strategy and establish the in-dependent PS model for multi-antenna receivers, following which the optimal PS vector optimization algorithm is presented based on fixed-point iteration or bi-exclusion. Compared with existing uniform PS model, the proposed in-dependent PS model enhances the RF energy utilization markedly. Then we study the physical-layer security problem in RF energy harvesting systems and model the multi-party eavesdropper-collusion and imperfect channel feedback,following which the low-complexity bi-level iteration algorithm is proposed to design resource allocation strategy for robust and secure multicast in RF energy harvesting systems. Compared with existing researches, the proposed algorithm provides robust and good suppress on the signal decoding at eavesdroppers, and hence greatly improves the achievable secure multicast rate.