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Performance Analysis And Joint Optimization Of MIMO Successive Relaying System

Posted on:2019-05-19Degree:MasterType:Thesis
Country:ChinaCandidate:C R SuFull Text:PDF
GTID:2428330596964627Subject:Information and Communication Engineering
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The rapid development of wireless communication technologies and the continuous improvement of network infrastructure have promoted the continued growth of mobile broadband users and brought about exponential growth in the wireless communications industry.The continuous growth of communication users will inevitably bring about the problem of shortage of spectrum resources.However,in the field of communications,spectrum resources are very scarce.On the other hand,the increase in the number of mobile broadband users will also result in a decrease in the communication rate and affect the quality of communications.Therefore,how to increase the utilization of spectrum resources and increase the communication rate has become an urgent problem to be solved.Wireless relay technology and MIMO technology are effective technologies to solve this problem.MIMO technology is equipped with multiple antennas at the transmitting and receiving ends of the communication system to obtain spatial diversity and multiplexing gains,improve system spectrum utilization,and reduce system error probability.The relay technology can improve the communication distance,increase the network coverage area,and improve the communication system performance by using the intermediate nodes of the system to forward information.The two-slot relaying protocol and the successive relaying protocol are two working protocols of the relay system.In the two-slot relaying protocol,the relay node alternately receives/fowards the signals transmitted by the source node in the odd/even time slots.Two-slot relaying protocol will bring about half loss of spectral efficiency.In order to solve this drawback,the successive relaying protocol alternately receives and forwards data from the source node through two different relays,so that the relay in the system works approximately in a full-duplex mode,so as to achieve the goal of compensating the spectrum loss.Although there are many researches on the successive relaying protocol,the current research mainly focuses on the situation of two relay with single-antenna or multi-antenna,and there are few studies on the successive relaying protocol under multi-relay multi-antenna.In this paper,we study the design,performance analysis,and optimization of the successive relay transmission scheme in a multi-antenna multi-relay system.First,we design a multi-antenna successive relay transmission scheme based on amplify-and-forward and match-filtering,including the relay-forwarding rules,the source node,and the multi-antenna precoding matrix of the relay node.The advantage of this scheme is that the source node(base station)and the destination node(user)only need statistic channel state information,and the relay node only needs its own real-time channel state information.Secondly,through theoretical analysis and formula derivation,we have obtained the system achievable rate and capacity scaling law of the transmission scheme.For comparison,we derived the system achiveble rate and capacity scaling law using the two-slot relaying protocol.We show that the proposed successive transmission scheme achieves twice capacity scaling law as the two-slot relaying protocol.This shows that when the number of relays reaches a certain size,the successive relay protocol algorithm can effectively compensate for the loss of spectrum efficiency caused by the two-slot relaying protocol.Finally,we analyzed various factors that may affect system performance in the system and designed a joint resource optimization algorithm based on differential evolution.The algorithm iteratively optimizes the power distribution matrix and relay grouping(including the order of relays and boundaries between relays),which can further increase the system's reachable transmission rate.
Keywords/Search Tags:mimo, successive relaying, scaling law, differential evolution algorithm
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