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Spectrum-Efficient Distributed Compressed Sensing Based Channel Estimation For Ofdm Systems Over Doubly Selective Channels
Abstract:
Sensing and power strategy optimization is important research topics in cognitive radio systems that hold the promise of advancing green communication. This concept gives us a brief overview of the existing power allocation design in the literature and unifies them into a general power allocation framework. Based on the closed-form solution derived for this general problem, the impact of network topology on the system performance is highlighted, which motivates us to propose a novel location-aware strategy that intelligently utilizes frequency and space opportunities and minimizes the overall power consumption while maintaining the quality of service of the primary system. This work shows that in addition to exploring spectrum holes in time and frequency domains, spatial opportunities can be utilized to further enhance energy efficiency for CR systems. Spectrum-Efficient Distributed Compressed Sensing Based Channel Estimation For Ofdm Systems Over Doubly Selective Channels
Existing Method:
- Hard-decision resource allocation (HDRA)
- Sensing-free resource allocation (SFRA)
Drawbacks:
- Severe fading over the distant end primary will leads much loss of bits on the primary receiver so the primary user should take a decision would leads efficient secondary network sensing and connection capacity over the particular user.
- Already done technique would leads abrupt spectrum releasing and continue over the other spectrum sensing is done over the network provided in it to new network as a secondary user
Proposed Method:
- Location-Aware relay based Resource Allocation
Block Diagram:
Fig: location awareness sensing system?
Advantages:
- Spectrum sensing can be done over the network based on a relay leads to high data transmission rate over the user allocated
- Lowers the spectrum sensing time over the network provided in sensing
- User will be able to transmit the data without any eavesdropper?s attack under the network it is available in the sensing area
Applications:
- Hospital ambulance and urgency aware service scheme systems can apply these technique in there user equipment topology
Software Requirement:-
- MATLAB 7.8 or above versions
References:-
[1] Uwaechia A N, Mahyuddin N M. “A review on sparse channel estimation in ofdm system using compressed sensing”. IETE Technical Review, vol. 34, no. 5, pp.514-531, 2017.
[2] Uwaechia A N, Mahyuddin N M. “Collaborative framework of algorithms for sparse channel estimation in OFDM systems”. Journal of Communications and Networks, vol. 20, no. 1, pp.9-19, 2018. [3] Sheng, Z., Tuan, H.D., Nguyen, H.H. and Fang, Y., 2017. Pilot Optimization for Estimation of High-Mobility OFDM Channels. IEEE Transactions on Vehicular Technology, 66(10), pp.8795-8806.
[4] Cooklev T, Dogan H, Cintra R J, Yildiz H. “A generalized prefix construction for OFDM systems over quasi-static channels”. IEEE Transactions on Vehicular Technology, vol. 60, no. 8, pp. 3684-3693, 2011. [5] Cheng P, Chen Z, Rui Y, Guo Y J, Gui L, Tao M, Zhang Q T. “Channel estimation for OFDM systems over doubly selective channels: A distributed compressive sensing based approach”. IEEE Transactions on Communications, vol. 61, no. 10, pp.4173-4185, 2013.
[6] Ma X, Yang F, Liu S, Song J, Han Z. “Sparse Channel Estimation for MIMO-OFDM Systems in High-Mobility Situations”. IEEE Transactions on Vehicular Technology, vol. 67, no. 7, pp. 6113 – 6124, 2018.
[7] Choi, Y. and Lee, J.H., 2017, June. Pilot structure design to increase wireless channel capacity for high-speed railway. In Networks and Communications (EuCNC), 2017 European Conference on (pp. 1-5). [8] Kuai, X., Sun, H., Zhou, S. and Cheng, E., 2016. Impulsive noise mitigation in underwater acoustic OFDM systems. IEEE Transactions on Vehicular Technology, 65(10), pp.8190-8202.
[9] Lin, X., Wu, S., Jiang, C., Kuang, L., Yan, J. and Hanzo, L., 2018. Estimation of Broadband Multiuser Millimeter Wave Massive MIMOOFDM Channels by Exploiting Their Sparse Structure. IEEE Transactions on Wireless Communications, 17(6), pp.3959-3973.
[10] Nguyen-Le H, Le-Ngoc T. “Pilot-aided joint CFO and doubly-selective channel estimation for OFDM transmissions”. IEEE Transactions on Broadcasting, vol. 56, no. 4, pp.514-522, 2010
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