TY - GEN
T1 - Distributed resource allocation for cognitive radio network with imperfect spectrum sensing
AU - Li, Hanqing
AU - Guo, Qing
AU - Tang, Tao
AU - Li, Qingzhong
PY - 2013
Y1 - 2013
N2 - In this paper, we investigate the resource allocation problem for the scenario where a satellite based primary network and an orthogonal frequency division multiplexing (OFDM) based multiuser cognitive radio (CR) secondary network coexist. The resource allocation aims to maximize the throughput of CR users, and we develop a resource allocation algorithm based on game theory, which seeks to improve the spectrum utilization in a distributed fashion under the constraints of the transmit power and symbol error rate limits of CR users. The primary user interference and spectrum sensing errors are also taken into consideration. A gradient projection based algorithm is used to solve the distributed game and a compressive sensing technique is used to acquire the channel and interference parameters needed for resource allocation. Simulation results show that although implemented in a distributed way, the performances of the proposed algorithm are comparable to a centralized heuristic allocation method which represents the optimal allocation.
AB - In this paper, we investigate the resource allocation problem for the scenario where a satellite based primary network and an orthogonal frequency division multiplexing (OFDM) based multiuser cognitive radio (CR) secondary network coexist. The resource allocation aims to maximize the throughput of CR users, and we develop a resource allocation algorithm based on game theory, which seeks to improve the spectrum utilization in a distributed fashion under the constraints of the transmit power and symbol error rate limits of CR users. The primary user interference and spectrum sensing errors are also taken into consideration. A gradient projection based algorithm is used to solve the distributed game and a compressive sensing technique is used to acquire the channel and interference parameters needed for resource allocation. Simulation results show that although implemented in a distributed way, the performances of the proposed algorithm are comparable to a centralized heuristic allocation method which represents the optimal allocation.
KW - Compressive sensing
KW - Distributed resource allocation
KW - Game theory
KW - Spectrum sensing errors
UR - https://www.scopus.com/pages/publications/84893221762
U2 - 10.1109/VTCFall.2013.6692165
DO - 10.1109/VTCFall.2013.6692165
M3 - 会议稿件
AN - SCOPUS:84893221762
SN - 9781467361873
T3 - IEEE Vehicular Technology Conference
BT - 2013 IEEE 78th Vehicular Technology Conference, VTC Fall 2013
T2 - 2013 IEEE 78th Vehicular Technology Conference, VTC Fall 2013
Y2 - 2 September 2013 through 5 September 2013
ER -