A Threshold-Based Relay Switching Protocol for Enhanced Capacity and Resource Efficiency

Kim, Yeejung; Kim, Tae-Hoon; Kim, Hoon; Kim, Sunghun; Han, Youngnam

doi:10.1109/lcomm.2011.082011.111438

Cited by 6 publications

(6 citation statements)

References 4 publications

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“…For example, architectural aspects and routing protocols were studied in [16]- [19], and different relay selection schemes were recently proposed in [20]- [22].…”

Section: Related Workmentioning

confidence: 99%

“…(iii) There is an excessive load in AP A k , meaning that constraint (15) is not fulfilled. Consequently, the formulation of the reward function in (20) takes into account the constraints of the optimization problem.…”

Section: A Reward Computation 1) Reward W Apn (K) When a Ue Is Connmentioning

confidence: 99%

“…At initialization, i.e., when AP A k or BS S j have not been previously used by UE u n , the values of Q AP,n (k) and Q BS,n (j) can be computed using expressions similar to the reward (20), (23), but replacing the first condition (because there is no measured value of ˆn r ), as explained in the following. For the case of an AP, the initial value of Q AP,n (k) is defined as…”

Section: B Computation Of the Q Apn (K) And Q Bsn (J) Values At Inmentioning

confidence: 99%

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Power-Efficient Resource Allocation in a Heterogeneous Network With Cellular and D2D Capabilities

Perez-Romero

Sánchez–González

Agustí

et al. 2016

IEEE Trans. Veh. Technol.

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Abstract-This paper focuses on a heterogeneous scenario in which cellular and wireless local area technologies coexist and in which mobile devices are enabled with device-to-device communication capabilities. In this context, this paper assumes a network architecture in which a given user equipment (UE) can receive mobile service either by connecting directly to a cellular base station or by connecting through another UE that acts as an access point and relays the traffic from a cellular base station. The paper investigates the optimization of the connectivity of different UEs with the target to minimize the total transmission power. An optimization framework is presented, and a distributed strategy based on Q-learning and softmax decision making is proposed as a means to solve the considered problem with reduced complexity. The proposed strategy is evaluated under different conditions, and it is shown that the strategy achieves a performance very close to the optimum. Moreover, significant transmission power reductions of approximately 40% are obtained with respect to the classical approach, in which all UEs are connected to the cellular infrastructure. For multi-cell scenarios, in which the optimum solution cannot be easily known a priori, the proposed approach is compared against a centralized genetic algorithm. The proposed approach achieves similar performance in terms of total transmitted power, while exhibiting much lower computational requirements.

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“…For example, architectural aspects and routing protocols were studied in [16]- [19], and different relay selection schemes were recently proposed in [20]- [22].…”

Section: Related Workmentioning

confidence: 99%

Section: A Reward Computation 1) Reward W Apn (K) When a Ue Is Connmentioning

confidence: 99%

Section: B Computation Of the Q Apn (K) And Q Bsn (J) Values At Inmentioning

confidence: 99%

See 1 more Smart Citation

Power-Efficient Resource Allocation in a Heterogeneous Network With Cellular and D2D Capabilities

Perez-Romero

Sánchez–González

Agustí

et al. 2016

IEEE Trans. Veh. Technol.

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“…In other words, if relay switching occurs, time resource αP SW · L is used for signaling overhead in every single frame while the rest (1 − αP SW ) · L is for packet transmission. Here, P SW is the relay switching probability, which indicates that signaling overhead occurs for relay switching [6].…”

Section: B Tdma Structurementioning

confidence: 99%

“…Thus, some studies also considered these attributes in opportunistically selecting an RN . In [6], authors proposed threshold-based relay switching selection (TRSS) protocol based on the ORS protocol to reduce the impact of signaling overhead. Frequent relay switching results in dominant overhead while preparing to switch.…”

Section: Introductionmentioning

confidence: 99%

Opportunistic Relay Selection Protocol under Multi-User Environment

Yoo¹,

Han

2013

2013 IEEE 78th Vehicular Technology Conference (VTC Fall)

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In this paper, a relay selection issue is studied to improve data rate performance in a multi-user environment. Previous opportunistic relay selection protocols only consider channel condition in relay selection. However, the target performance is affected by other system attributes, such as delay and load in relay node (RN). The proposed relay selection scheme, which is based on analytic hierarchy process (AHP), adopts signal-to-noise ratio (SNR), switching, and the number of connections to RN as a selection criteria. Also, a special form of eigenvector is proposed and proved analytically. From the proposed eigenvector, the computational complexity is obviously reduced. Target system performance is examined under different weighting parameter values to the above attributes. This work shows that average data rate can be maximized corresponding to the number of RN. Simulation results reveal the benefits of the proposed scheme over the conventional opportunistic relay selection scheme and the threshold-based relay switching selection scheme. The performance of our scheme is verified in terms of average data rate and distribution of the data rate.

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Enhancing cellular coverage through opportunistic networks with learning mechanisms

Perez-Romero

Sallent

Agustí

2013

2013 IEEE Globecom Workshops (GC Wkshps)

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A Threshold-Based Relay Switching Protocol for Enhanced Capacity and Resource Efficiency

Cited by 6 publications

References 4 publications

Power-Efficient Resource Allocation in a Heterogeneous Network With Cellular and D2D Capabilities

Power-Efficient Resource Allocation in a Heterogeneous Network With Cellular and D2D Capabilities

Opportunistic Relay Selection Protocol under Multi-User Environment

Enhancing cellular coverage through opportunistic networks with learning mechanisms

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