Using Buffers in Trust-Aware Relay Selection Networks With Spatially Random Relays

Gong, Yu; Chen, Gaojie; Xie, Tian

doi:10.1109/twc.2018.2850016

Cited by 20 publications

(10 citation statements)

References 24 publications

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“…Yet, the chosen pair may cause performance degradation as the nodes may not trust each other. Thus, Gong et al [121] proposed a scheme for a relaying network with spatially random relays. This scheme associates data transmission with both trusted and strong links, and introduces a set of parameters, ‘the target‐queuing‐lengths’.…”

Section: Review Of Max‐link Relay Selection Policy and Comparison Witmentioning

confidence: 99%

“…This scheme minimises packet delay, diversity order, and improves compromise between the diversity and delay. The scheme [121] is compared with MLRS of [4], reaching the following conclusions: • Better performance in both OP and APD is noticeable in the trust‐aware scheme for best outage performance. • Better performance in APD is noticeable in the trust‐aware scheme for minimum delay, and better outage performance is noticeable in MLRS. …”

Section: Review Of Max‐link Relay Selection Policy and Comparison Witmentioning

confidence: 99%

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Buffer‐aided relaying: a survey on relay selection policies

2020

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Ever since the idea of buffers was incorporated in wireless communications, buffer‐aided relaying has become an emerging breakthrough in the world of transmitting and receiving signals. Equipping the relays with buffers adds a new degree of freedom capable of enhancing numerous quality‐of‐service (QoS) metrics including throughput, outage probability, power efficiency and physical‐layer security. The QoS enhancement is achieved by compromising the end‐to‐end delay that is inflicted by storing the packets in the relays' buffers until a suitable source–relay or relay–destination link is selected. In this context, the selection of a relay for transmission/reception is important since it governs the QoS‐delay trade‐off that can be contemplated. In this survey, the authors review and analyse various link selection protocols in buffer‐aided relaying systems. These relaying strategies are categorised and contrasted according to their performance levels, limitations, applications, system model assumptions and complexity. Finally, they discuss current challenges, and highlight future research directions.

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Section: Review Of Max‐link Relay Selection Policy and Comparison Witmentioning

confidence: 99%

Section: Review Of Max‐link Relay Selection Policy and Comparison Witmentioning

confidence: 99%

Buffer‐aided relaying: a survey on relay selection policies

2020

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“…Asides the aforementioned, the consideration of trust which has recently attracted researchers interest [167] is a potential direction in socially-aware relay-assisted D2D communication. Trusts could be assigned values [168] where the willingness to cooperate is modelled as a value between 0 and 1.…”

Section: E Socially-aware Relayingmentioning

confidence: 99%

“…In this case, a trust level of 0 implies distrust and reluctance to cooperate while 1 is an indication of a high level of trust and willingness to cooperate. Another recently studied direction with regards to trust-aware relay selection is the consideration of a buffer-aided scheme [167], where relays can store data in buffers. Transmission of the stored data can be deferred to the time when the links are significantly strong.…”

Section: E Socially-aware Relayingmentioning

confidence: 99%

Relay-Assisted D2D Underlay Cellular Network Analysis Using Stochastic Geometry: Overview and Future Directions

et al. 2019

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Device-to-Device (D2D) communication is one of the enabling technologies for meeting the capacity requirements of the fifth-generation wireless systems (5G). It has diverse applications in traffic offloading, disaster management and content sharing, to mention a few. The network coverage and capacity further improve when relays are introduced to D2D communication. However, the interference becomes more severe since these devices also share resources with the traditional cellular users in the underlay. To take the benefits and avert the drawbacks of this spectrum sharing scenario, analytical tools capable of revealing the mathematical relationships among pertinent network design parameters are needed. This brings stochastic geometry (SG) into the picture. With SG-based analyses, designers can model concepts to understand, provide insights, and address the problems of spectrum sharing in relay-assisted D2D communication. Some of the key metrics of particular interest to network designers are the transmission capacity and spectral efficiency of D2D communication, as they reveal the performance gains and quantify the level of interference within the network. These enable them to properly correlate relevant cause-and-effect relationships before wealth and time are invested in network implementation. Despite the studies on the analysis of relay-assisted D2D underlay cellular networks using SG in recent years, there is no available survey material where researchers can find models, assumptions, key results and derived lessons to further comprehend this area and open up new research lines. This motivates the presentation of this paper which in addition to the aforementioned, gives elaborate discussions on promising areas for future research with respect to the recent advancements in D2D communication and SG research.

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“…Accordingly, in [7 ], the authors proposed a priority‐based buffer‐aided relay selection scheme to reduce packet delay in cooperative networks. Moreover, in [8 ], the authors proposed a buffer‐aided relay selection scheme and demonstrated its performance‐delay trade‐off. Despite the interesting schemes found in the literature, further improvement can still be obtained by introducing delay constraints when performing the selection, which has not been considered in any of the above work.…”

Section: Introductionmentioning

confidence: 99%

Novel deep reinforcement learning‐based delay‐constrained buffer‐aided relay selection in cognitive cooperative networks

et al. 2020

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In this Letter, a deep reinforcement learning‐based approach is proposed for the delay‐constrained buffer‐aided relay selection in a cooperative cognitive network. The proposed learning algorithm can efficiently solve the complicated relay selection problem, and achieves the optimal throughput when the buffer size and number of relays are large. In particular, the authors use the deep‐Q‐learning to design an agent to estimate a specific action for each state of the system, which is then utilised to provide an optimum trade‐off between throughput and a given delay constraint. Simulation results are provided to demonstrate the advantages of the proposed scheme over conventional selection methods. More specifically, compared to the max‐ratio selection criteria, where the relay with the highest signal‐to‐interference ratio is selected, the proposed scheme achieves a significant throughput gain with higher throughput‐delay balance.

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Using Buffers in Trust-Aware Relay Selection Networks With Spatially Random Relays

Cited by 20 publications

References 24 publications

Buffer‐aided relaying: a survey on relay selection policies

Buffer‐aided relaying: a survey on relay selection policies

Relay-Assisted D2D Underlay Cellular Network Analysis Using Stochastic Geometry: Overview and Future Directions

Novel deep reinforcement learning‐based delay‐constrained buffer‐aided relay selection in cognitive cooperative networks

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