Emerging 5G wireless networks need to support sufficient connectivity for a large number of machine-type communication devices, which may have poor radio-frequency (RF) circuit quality due to cost- and energy-efficient design. In this paper, the connectivity is analyzed for a training-based large-scale antenna system employing both non-orthogonal pilots and dirty-RFs on both the transmitter and receiver sides. By considering the effects of imperfect hardware and interference caused by non-orthogonal pilots, the performance of the linear minimum mean-square error (MMSE) channel estimator is derived, and the corresponding average rate of the maximum ratio combiner (MRC) is obtained. From these results, the connectivity maximization problem is formulated and the closed-form solutions for the optimal training length and the optimal number of simultaneously served users are provided. Asymptotic analysis further reveals that allowing only orthogonal pilots limits both the connectivity even when a large number of antenna is employed. However, by allowing non-orthogonal pilots, both the connectivity and the energy efficiency can be improved significantly, even when dirty-RFs are taken into account.
Bibliographical noteFunding Information:
This work was supported in part by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea Government (MSIT) (2015-0-00300, Multiple Access Technique with Ultra-Low Latency and High Efficiency for Tactile Internet Services in IoT Environments), and in part by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (NRF-2014R1A2A2A01007254).
All Science Journal Classification (ASJC) codes
- Information Systems
- Computer Networks and Communications