Computing Resource Constraint in Wireless M2M Communications

Abstract

Featured by massive and ubiquitous connection, s-mall data transmissions, and vast applications range, machine-to-machine (M2M) communications has become as an increasingly important source of traffic in current wireless networks. However, as the cloud computing based wireless architectures, e.g., cloud-RAN, emerge and densely deployed, the vast connection and stringent QoS requirements of machine type devices (MTDs) pose severe challenges on the central processing capability, making the computing resource in the baseband unit (BBU) pool another dimension of limited resource. In this paper, we first characterize computing resource consumption by each transmission from H2H/M2M device, and then focus on maximizing the achievable sum-rate of the M2M aggregators, which can be regarded as an indicator of how much MTDs can be potentially supported. Upon the sum rate maximization, a relaxation-based iterative algorithm is proposed. Numerical results show the significant impact of computing resource on the achievable sum-rate of the M2M aggregators. Especially, the scarcity of computing resource can be a significant impediment on the MTDs achievable sum-rate.