Research On Multiple Access For Massive Machine-type Communication

With the rapid growth of the number of connected devices in the Internet of Things,the massive machine-type communication(m MTC)has become one of the three major scenarios of the fifth generation communication system.Unlike the human type communication,the m MTC typically has the characteristics of massive number of devices,small-sized packets,low or no mobility,sporadic transmission,uplink-dominated transmission and low energy consumption.The traditional grant-based random access method is not appropriate for the m MTC,as the excessive control packets will cause low spectral efficiency,redundant handshaking procedure will cause large latency and energy consumption,and a limited number of orthogonal preambles per cell sill cause serious collision.Therefore,in order to satisfy the m MTC scenario requirements,it calls for a novel method for the multiple access and the data transmission in m MTCFirstly,in this thesis,the massive access model is transformed into an optimization problem.To solve the proposed optimization problem,we proposed an algorithm to jointly conduct the active device detection,channel estimation and data recovery.Specifically,the proposed method improves the performance of the data recovery by exploiting the checking mechanism of channel coding.Simulation results demonstrate that the proposed method has improved the performance of data recovery.Secondly,the massive access model is further transformed into a new optimization problem considering the constraints that i)the amount of data transmitted by different active devices is different,and ii)the set of modulation symbols is a finite set.In order to solve the proposed optimization problem,we proposed an algorithm to jointly conduct the activity level estimation,activity detection,channel estimation and data recovery.Specifically,the proposed method conducts the activity level estimation in a backward manner and improves its performance by exploiting the data length diversity information.Furthermore,the proposed method improves the performance of active device detection,channel estimation and data recovery by taking the joint sparsity information of pilot and data symbols and the modulation constellation information into account.Simulation results demonstrate the superiority of the proposed solution in comparison to other existing methods.Finally,we propose the optimization problem and the algorithm considering all the enhancing mechanisms of the above two proposed methods.The experimental results also show that the proposed method has better system performance than the existing typical method.Furthermore,we provide the theoretical analysis on the convergence of the proposed method,the rationale of the improvement by exploiting the data length diversity and the computational complexity.