On Game Theory-Based Coordination Schemes for Mobile Small Cells

Georgakopoulos, Panagiotis; Akhtar, Tafseer; Kotsopoulos, Stavros

doi:10.1109/camad.2019.8858428

Cited by 7 publications

(6 citation statements)

References 13 publications

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“…The clustering of cooperating transmission points in JT-CoMP has been identified as a significant challenge in various studies [9], [10]. As shown in our previous studies [11], [12], incautious clustering can result in reduced spectral efficiency and data rates for the UEs not in the cell edges (or nonedge UEs) as there is a high probability of committing too many resources to cell edge UEs (or edge UEs), especially if their percentage in the network is high. Also, JT-CoMP stresses the links between the BBU pool and the core network, i.e.…”

Section: Introductionmentioning

confidence: 90%

“…Therefore, a dynamic coalition formation algorithm needs to be constructed that can adapt to possible changes. Our proposed algorithm is a modified version of the coalition formation algorithm based on merge and split operations in [11], [12] and is divided in three stages:…”

Section: Proposed Coalition Formation Algorithmmentioning

confidence: 99%

“…Our previous works [11], [12] examine the performance of JT-CoMP in a C-RAN small cell scenario with a backhaul constraint and variable number of deployed RRHs, where the UEs are assumed still for the duration of the proposed coalition formation algorithm. Vienna LTE-A Downlink System Level Simulator [14] was used for realizing the simulations.…”

Section: Introductionmentioning

confidence: 99%

“…In this study, the performance and adaptability of a modified version of the proposed algorithm of [11], [12] is tested in a scenario where a high-speed user is moving between cells, activates handovers and changes the involved cells' load. Furthermore, we enhance the simulation accuracy capabilities of LTE-A Downlink System Level Simulator by importing physical layer parameters from TruNET wireless [15].…”

Section: Introductionmentioning

confidence: 99%

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Efficient cooperative transmissions with dynamic clustering in realistically designed small cells

Georgakopoulos¹,

Akhtar²,

Tselios³

et al. 2020

2020 IEEE 25th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD)

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Modern wireless networks promise high performance and quality of service to the ever-growing number of mobile users. However, limited radio resources, high interference and poor channel conditions are significant obstacles for reliable and high quality service provision. To tackle these issues, a scheme where JT-CoMP is deployed in a Cloud Radio Access Network (C-RAN) small cell topology is proposed, where the clustering of the remote radio heads (RRHs) is based on a dynamic coalition formation algorithm. To obtain more reliable and accurate results of the JT-CoMP deployment, TruNET wireless was used alongside Vienna LTE-A Downlink System Level Simulator to model the propagation environment. To test our proposed scheme's performance, the throughput of immobile and high-speed moving users is examined in separate scenarios.

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Section: Introductionmentioning

confidence: 90%

Section: Proposed Coalition Formation Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Efficient cooperative transmissions with dynamic clustering in realistically designed small cells

Georgakopoulos¹,

Akhtar²,

Tselios³

et al. 2020

2020 IEEE 25th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD)

Self Cite

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“…In a cooperative environment, it can produce the upper bound efficiency in multicasting. The future networks with small cell environment featuring device to device communication and cooperative environment of devices, try to ensure that every user in the network will be fairly provided with the required services [16]- [18]. In such a cooperative environment, as shown in fig 2, the receivers would receive partial streams from the channel and cooperate with the neighbouring nodes to create the complete information.…”

Section: Introductionmentioning

confidence: 99%

On Security Against Pollution Attacks in Network Coding Enabled 5G Networks

2020

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Future communication networks need to harness the available spectrum more efficiently to cater the requirements of the ever-increasing digital devices. Higher data rate with low latency is a major requirement of future communication networks. Network coding is arising once more as an enabler for satisfying the bandwidth requirements of future multimedia and resource hungry services. However, network coding techniques suffer from security vulnerabilities that will eliminate any bandwidth profits. Specific attacks in network coding like pollution attacks are extremely dangerous due to the nature of encoding and spreading inside the whole network. They deteriorate the bandwidth efficiency and even disrupt proper decoding of any message at the receiving end. Further, in a wireless environment, the authenticity of intermediate nodes is not easy to ensure, making it easier for an attacker to be part of the network. Thus counteracting pollution attacks in network coding becomes very important for practical applications of network coding enabled networks in the future generations of mobile communication. There has been a lot of research interest in this direction resulting in a few interesting approaches for secure network coding. However, most of the schemes fail to meet the expected standards or incur high overheads to the system. Schemes addressing the dense heterogeneous networks efficiently are yet to be proposed. This study surveys the security vulnerabilities of network coding, particularly those imposed by pollution attacks, as well as, the corresponding countermeasures. The survey goes a step further and includes a potential secure implementation of network coding enabled 5G networks, based on cooperating small cells. INDEX TERMS 5G mobile communication, cryptography, network coding, network security.

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