MCMC-Based Detection for Random Access With Preambles in MTC

Abstract

Random access has been considered for machine-type communication (MTC). In particular, a random access scheme with a pool of preambles is widely studied, which is similar to multichannel ALOHA, to support a number of MTC devices. In this paper, we study optimal approaches for user activity detection in random access with preambles over fading channels. Since the computational complexity grows exponentially with the number of preambles, a low-complexity detector is derived using Markov chain Monte Carlo (MCMC) approaches that can approximately solve an optimal maximum a posteriori detection problem. The resulting MCMC detector can enjoy a trade-off between performance and complexity, while its complexity to obtain a sample is linearly proportional to the number of preambles. A performance analysis for optimal detection is also studied to see the optimal performance. Simulation results confirm that the MCMC detector performs better than compressive sensing-based approaches and can provide a near optimal performance under certain conditions with a reasonable computational complexity.