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DC Field | Value | Language |
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dc.contributor.author | Kumar, P. | - |
dc.date.accessioned | 2022-06-15T09:28:40Z | - |
dc.date.available | 2022-06-15T09:28:40Z | - |
dc.date.issued | 2022-06-15 | - |
dc.identifier.uri | http://localhost:8080/xmlui/handle/123456789/3497 | - |
dc.description.abstract | In this thesis, we proposed the usage of a drone as relay node in Network Coded Cooperation (NCC) for enhancing the reliability as well as throughput for the next generation wireless communication systems. The usage of drone as relay node in place of static relay (placed along with nodes in the same horizon plane on the ground) gives better reliability by adjusting their vertical position. The analysis of the proposed Drone Assisted Network Coded Cooperation (DA-NCC) network, is divided into two parts. In the rst part, we formulated an analytical framework for the proposed DANCC by considering xed channel distribution (Rayleigh or Rician) for uplink as well as downlink. The rst part is further sub-divided into two problems namely problem 1 and problem 2. In the rst problem, initially we considered the Air-to- Ground (A2G) channel to be Rayleigh distributed for DA-NCC and derived the closed form expression of the outage probability using two approaches namely, analytical and semi-analytical. The relaying scheme used at drone is Amplify-and-Forward (AF) and the combining technique used at the destination node is Maximal Ratio Combining (MRC). The e ects of drone height on the system performance are also investigated through simulations by considering Air-to-Ground (A2G) links as Rician distributed. In the second problem, we analysed the performance of DA-NCC in Rician faded environment and derived the closed form expression of outage probability and rate by considering Decode-and-Forward (DF) at drone and Selection Combining (SC) at destination node. Due to the mathematical tractability, at last in the appendix section we consider only traditional NCC system having two source-destination pairs for analysing the e ect of DNC-noise in correlated NLoS environment. In the second part, we proposed a statistical channel model for drone assisted networks. Our proposed channel model takes the probability of occurrence of Line of Sight (LoS) along with other system parameters into account while deciding the appropriate statistical channel model for the A2G links between ground node(s) and drone. Probability based statistical channel model approach takes statistical independence of the links (uplink and downlink) into consideration. The second part is further divided into two sub problems named as problem 3 and problem 4. In problem 3, we proposed Drone Assisted Device to Device Cooperative Communication (DA-DDCC) system for critical situations during post-disaster management. The proposed DADDCC utilizes the autonomous mode of Device-to-Device (D2D) communication for setting up the link in the absence of a base station. For performance evaluation of the proposed network, we considered AF at drone and SC at destination and derived the closed form expressions of average outage probability and average rate using the proposed channel model. Problem 4 is an extension of problem 3 to a multi-user scenario in which we analysed the performance of DA-NCC by considering DF at drone and SC at destination node. Taking statistical independence of uplink and downlink into consideration, we derived the closed form analytical expressions of average outage probability and average rate for the proposed DA-NCC system. | en_US |
dc.language.iso | en_US | en_US |
dc.subject | Cooperation | en_US |
dc.subject | Network coding | en_US |
dc.subject | Network coding noise | en_US |
dc.subject | Rate | en_US |
dc.subject | Outage probability | en_US |
dc.subject | Drone/UAV | en_US |
dc.subject | Rayleigh channel | en_US |
dc.subject | Rician channel | en_US |
dc.subject | Statistical channel model | en_US |
dc.subject | Correlation | en_US |
dc.title | Drone assisted network coded cooperation for next generationwireless communication systems | en_US |
dc.type | Thesis | en_US |
Appears in Collections: | Year-2021 |
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