Max-Max Relay Selection for Relays with Buffers

Ikhlef, Aïssa; Michalopoulos, Diomidis S.; Schober, Robert

doi:10.1109/twc.2012.011012.110682

Cited by 285 publications

(267 citation statements)

References 15 publications

Supporting

Mentioning

266

Contrasting

Unclassified

Order By: Relevance

“…Correspondingly, the energy dissipation ED of SD the hop is given by (11). Similarly, the activation probability and the energy dissipation related to all other channels may be calculated, leading to similar closed-form expressions.…”

Section: Linear Taps Division For a Finite Maximum Transmission Powermentioning

confidence: 99%

“…Similarly, the activation probability and the energy dissipation related to all other channels may be calculated, leading to similar closed-form expressions. The TAPS-based Algorithm 1 should be updated for the finite maximum transmission power by substituting (5) and (7) with (10) and (11).…”

Section: Linear Taps Division For a Finite Maximum Transmission Powermentioning

confidence: 99%

“…Both our simulation results and theoretical analysis demonstrated that MHD transmissions are capable of achieving a substantial selection diversity gain. Very recently, a relayrelation scheme was proposed in [11], while full-duplex relaying was discussed in [12]. As a further advance, adaptive link selection was proposed in [13].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

On Buffer-Assisted Opportunistic Routing Relying on Linear Transmission Activation Probability Space Partitioning for Relay-Aided Networks

Dong

Zuo

Yang

et al. 2014

2014 IEEE 80th Vehicular Technology Conference (VTC2014-Fall)

View full text Add to dashboard Cite

Abstract-In this paper buffer-aided Opportunistic Routing (OR) was designed with the aid of the novel concept of linear Transmission Activation Probability Space (TAPS) partitioning invoked for relayassisted networks, which combines the benefits of both OR [1] and of buffer-aided transmissions [2]. More specifically, a packet may be transmitted from the Source Node (SN) to the Destination Node (DN) either directly or indirectly via one of the M Relay Nodes (RNs), depending on the instantaneous channel qualities. The above-mentioned linear multi-dimensional TAPS partitioning concept is proposed for partitioning the transmission space into (2M + 1) transmission regions plus an outage region, while ensuring that the number of input packets is equal to the number of output packets at each RN's buffer. The benefit of having a buffer and tolerating the associated delay is that the best channel is activated for transmission based on our linear TAPS partitioning method.

show abstract

Section: Linear Taps Division For a Finite Maximum Transmission Powermentioning

confidence: 99%

Section: Linear Taps Division For a Finite Maximum Transmission Powermentioning

confidence: 99%

See 1 more Smart Citation

On Buffer-Assisted Opportunistic Routing Relying on Linear Transmission Activation Probability Space Partitioning for Relay-Aided Networks

Dong

Zuo

Yang

et al. 2014

2014 IEEE 80th Vehicular Technology Conference (VTC2014-Fall)

View full text Add to dashboard Cite

show abstract

“…In general, fixed relaying schemes developed under non-buffer relaying setting can be modified to exploit the relay buffering. However, the resulting relaying schemes may fail to achieve the maximum diversity gain offered by buffer-aided relaying over the non-buffer relaying since the relay still receives and transmits sequentially in every time slot [43], [78]- [80]. Thus, to exploit the transmission flexibility offered by the relay buffering capability, adaptive link selection relaying must be considered, where the relay transmission and reception schedule is not fixed.…”

Section: Half-duplex Relaying With Adaptive Link Selectionmentioning

confidence: 99%

Radio Resource Allocation Over Fading Channels Under Statistical Delay Constraints

Phan¹,

Le‐Ngoc²

2017

SpringerBriefs in Electrical and Computer Engineering

View full text Add to dashboard Cite

Wireless data transmission suffers from the fading nature of wireless channels, where the instantaneous channel conditions and hence transmission rates randomly fluctuate over time. Consequently, data arrivals at each transmitter might not be transmitted instantly. To cope with this situation, the transmitter employs buffer to store the data temporarily for later transmission. While data buffering enables more efficient radio resource allocation to opportunistically select the favorable fading conditions for transmission, it introduces queuing delay that needs to be controlled in order to meet the end-to-end delay quality-ofservice (QoS) requirements in supporting delay-sensitive communications. In this thesis, we study and develop radio resource allocation schemes for buffer-aided communications over wireless fading channels under statistical delay constraints. Using the buffering capability as a means to exploit the fading diversity, the nodes (source and relay) perform resource allocation, and adapt their transmissions to the channel state information (CSI) in order to enhance the system throughput while maintaining the statistical delay QoS requirements in terms of upper-bounded average delay or delay-outage probability.The thesis starts by considering a source-destination communications link over a fading channel with data arriving at the source transmission buffer. In the first scenario, the source is assumed to have a maximum power constraint and an average delay constraint. We consider admission control applied on random data arrivals to the source buffer in order to avoid constraint violation, and study the joint optimal data admission control and power allocation (AC-PA) problem for throughput maximization. In the second scenario, we consider an energy-harvesting (EH) source, where random amounts of energy are harvested from renewable energy sources, and stored in a battery during the course of data transmission. In every transmission time slot, the source is constrained to use at most the amount of energy currently stored. We then explore optimal power allocation problems for such EH systems under average delay or delay-outage constraints. We formulate the problems as infinite horizon constrained Markov decision process (MDP) problems, which incorporate the random variations of the fading channel, data arrival, and EH processes. A novel solution approach based on post-decision state-value function is proposed to study the properties of the optimal solutions. We also propose online allocation algorithms when the statistical knowledge of the random processes is unknown, which is typical in real-life communications. Illustrative results demonstrate the effectiveness of the proposed algorithms over existing approaches iii under similar power and delay constraints.The thesis continues with a source-relay-destination communications link over fading channels with buffers available at both source and relay, as part of a multi-hop network. We investigate and develop optimal resource allocation schemes ...

show abstract

“…However, in practice, the transceiver hardware is imperfect due to phase noise, I/Q imbalance and amplifier nonlinearities.  Half versus full duplex: Most schemes consider half-duplex (HD) relays where reception and transmission occur separately, resulting in resource loss in time and/or frequency [21][22][23]. In order to alleviate this loss, buffer-aided successive schemes have been proposed where two relays are concurrently activated, one for reception and the other for transmission, thus mimicking full-duplexity but at the cost of inter-relay interference (IRI).…”

Section: Relay Selection Algorithmsmentioning

confidence: 99%

Survey on Relay Selection Techniques in Cognitive Networks

Bouallegue

Sethom

2016

IRA-JTE

View full text Add to dashboard Cite

Cognitive radio holds great promise as a research area nowadays. By increasing the spectral efficiency, it is applicable to various areas I. INTRODUCTIONSince the technology of wireless communication is quickly developing; heterogeneous wireless communication system must be adjusted in reduced frequency bandwidth. The efficiency of the conventional orthogonal spectral separation of heterogeneous system is not sufficient to sustain the unceasing growth of wireless device deploying exigency, making spectrum sharing amongst heterogeneous systems increasingly important. Two main challenges for the future of wireless systems are raised: the high data rate request of the growing number of users and the wireless spectrum availability. By reusing the underused licensed frequency bands, Cognitive Radio (CR) is presented as a new facet of telecommunication system to resolve the issue of spectrum availability. In such system, unlicensed wireless users (secondary users) are dynamically granted access to the licensed bands under the compulsion of tolerable interference to the Primary Users (PU). To facilitate the spectrum sharing among Secondary Users SUs and PUs, there are two existing techniques: The spectrum overlay and the spectrum underlay. In the first method, the SU shares some its power resources with the PU to give a relay-assisted transmission. Hence, the secondary network offsets the forced interference by the increase in the signal to interference plus noise ratio (SINR) of primary receivers. Thenceforth, the core idea of the overlay approach is to assign power and channel assets to the entire network, while using the requirements of PUs. To prevail over the spectral efficiency loss of one-way half duplex, [1] implements a two-way relaying.

show abstract

Max-Max Relay Selection for Relays with Buffers

Cited by 285 publications

References 15 publications

On Buffer-Assisted Opportunistic Routing Relying on Linear Transmission Activation Probability Space Partitioning for Relay-Aided Networks

On Buffer-Assisted Opportunistic Routing Relying on Linear Transmission Activation Probability Space Partitioning for Relay-Aided Networks

Radio Resource Allocation Over Fading Channels Under Statistical Delay Constraints

Survey on Relay Selection Techniques in Cognitive Networks

Contact Info

Product

Resources

About