Cooperative Sequential Sensing of Radio Transmissions in 5G with Improved Cost-Delay Tradeoff

The fifth-generation (5G) wireless networks are generally anticipated to be heterogeneous, consisting of macro cells, wireless local area networks, device-to-device networks, ad hoc networks, and so forth. The spectrum occupancy varies on spatial and temporal basis. So sensing the variation of spectrum occupancy and informing the resource scheduler can optimize the utilization efficiency of spectrum. Especially for deployment of cognitive radio in 5G, spectrum sensing is regarded as a key technique. In this work, we study the cooperative sensing in 5G heterogeneous wireless networks with a centralized control module. By formulating this cooperative sensing problem as a sequential binary hypothesis test problem, the number of unnecessary data samples and the associated cost is substantially reduced, with guaranteed detection precision. We develop a cooperative sequential detection algorithm, in which multiple geographically diverse sensors are sequentially set up to measure and transmit measurement results on demand. Furthermore, we consider different sensor sampling schemes to address the cost-delay tradeoff problems and then propose a conditional mean activation and sampling algorithm, in which the number of required samples is predicted based on the quality of the collected samples. The performances of different sensor sampling schemes are demonstrated under different sensing environments.