TY - GEN
T1 - The achievable capacity scaling laws of 3D cognitive radio networks
AU - Wei, Zhiqing
AU - Feng, Zhiyong
AU - Yuan, Xin
AU - Feng, Xinyu
AU - Zhang, Qixun
AU - Wang, Xin
N1 - Publisher Copyright: © 2016 IEEE.
PY - 2016/7/12
Y1 - 2016/7/12
N2 - The exploitation of spectrum opportunities in the dimension of height will bring another transmission degree of freedom for wireless networks. Besides, the modern wireless networks are deployed in the three dimensional (3D) space, which need cognitive radio technologies to enhance their performances. With these motivations, the capacity of 3D cognitive radio networks (CRNs) is addressed in this paper. Since there is one additional dimension of interference in 3D CRNs, the network protocols need to be designed to coordinate the interference and guarantee the connectivity of CRNs. Then the link capacity and routing density of 3D CRNs are investigated. Finally, we have derived the per-node capacity of primary network and secondary network respectively. We have verified that the path loss factor α has an impact on the capacity of 3D CRNs, namely, α = 3 is a watershed of capacity scaling laws. Besides, when α > 2.5, the capacity of 3D CRNs is higher than 2D CRNs with the same amount of nodes asymptotically. Therefore our results may provide an insight into the design of 3D cognitive radio networks.
AB - The exploitation of spectrum opportunities in the dimension of height will bring another transmission degree of freedom for wireless networks. Besides, the modern wireless networks are deployed in the three dimensional (3D) space, which need cognitive radio technologies to enhance their performances. With these motivations, the capacity of 3D cognitive radio networks (CRNs) is addressed in this paper. Since there is one additional dimension of interference in 3D CRNs, the network protocols need to be designed to coordinate the interference and guarantee the connectivity of CRNs. Then the link capacity and routing density of 3D CRNs are investigated. Finally, we have derived the per-node capacity of primary network and secondary network respectively. We have verified that the path loss factor α has an impact on the capacity of 3D CRNs, namely, α = 3 is a watershed of capacity scaling laws. Besides, when α > 2.5, the capacity of 3D CRNs is higher than 2D CRNs with the same amount of nodes asymptotically. Therefore our results may provide an insight into the design of 3D cognitive radio networks.
KW - 3D Cognitive Radio Networks
KW - Capacity Scaling Laws
KW - Height Dimension
UR - https://www.scopus.com/pages/publications/84981320992
U2 - 10.1109/ICC.2016.7511544
DO - 10.1109/ICC.2016.7511544
M3 - Conference contribution
T3 - 2016 IEEE International Conference on Communications, ICC 2016
BT - 2016 IEEE International Conference on Communications, ICC 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE International Conference on Communications, ICC 2016
Y2 - 22 May 2016 through 27 May 2016
ER -