Spectral and Energy Spectral Efficiency Optimization of Joint Transmit and Receive Beamforming Based Multi-Relay MIMO-OFDMA Cellular Networks

Cheung, Kent Tsz Kan; Yang, Shaoshi; Hanzo, Lajos

doi:10.1109/twc.2014.2348996

Cited by 33 publications

(20 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Note that in Fig. 2 the optimal system capacity is attained, when employing the maximization algorithm of [1], since the ESGA is capable of enumerating all possible SMC groupings satisfying (1) for the corresponding α. The 'normalized optimality gap' is then defined as (β/β * ) − 1, where β * is the optimal capacity obtained from employing Figure 3: The average achievable capacity of the OCGA with random group selection and equal power allocation.…”

Section: Numerical Results and Discussionmentioning

confidence: 99%

“…Additionally, a final step is performed to remove the specific groups, which result in effective channel gains that are less than or equal to that of another group, while having the same transmitters. 6 More details related to this algorithm may be found in [1]. Number of groups found using OCGA Number of groups found using ESGA Gap to optimality when using OCGA Gap to optimality when using ESGA Semi-orthogonality parameter, α Number of groups found Normalized optimality gap The optimality gap and total number of SMC groups found when employing the ESGA and OCGA, and using the parameters in Table I Therefore, this final step does not reduce the attainable system performance, but reduces the number of possible groups, thus alleviating computational complexity.…”

Section: Algorithm 1: Exhaustive Search-based Grouping Algorithm (Esga)mentioning

confidence: 99%

“…Hence, the BS and RNs do not have to share H BU n,k or H RU n,k via the wireless channel and do not have to solve (2) again. As a result, we accomplish the goal of creating effective channel matrices that contain rows aiming to satisfy (1). Additionally, the columns of R U,T2 n,k have been normalized so that the power assigned for each SMC remains unaffected.…”

Section: B Bf Design In the Second Transmission Phasementioning

confidence: 99%

See 2 more Smart Citations

Joint transmit and receive beamforming for multi-relay MIMO-OFDMA cellular networks

Cheung

Yang

Hanzo

2016

2016 IEEE International Conference on Communications (ICC)

Self Cite

View full text Add to dashboard Cite

Abstract-A novel transmission protocol is conceived for a multi-user, multi-relay, multiple-input-multiple-output orthogonal frequency-division multiple-access (MIMO-OFDMA) cellular network based on joint transmit and receive beamforming. More specifically, the network's MIMO channels are mathematically decomposed into several effective multiple-input-singleoutput (MISO) channels, which are spatially multiplexed for transmission. For the sake of improving the attainable capacity, these MISO channels are grouped using a pair of novel grouping algorithms, which are then evaluated in terms of their performance versus complexity trade-off 1 .

show abstract

Section: Numerical Results and Discussionmentioning

confidence: 99%

Section: Algorithm 1: Exhaustive Search-based Grouping Algorithm (Esga)mentioning

confidence: 99%

Section: B Bf Design In the Second Transmission Phasementioning

confidence: 99%

See 1 more Smart Citation

Joint transmit and receive beamforming for multi-relay MIMO-OFDMA cellular networks

Cheung

Yang

Hanzo

2016

2016 IEEE International Conference on Communications (ICC)

Self Cite

View full text Add to dashboard Cite

show abstract

“…• Apart from this, mitigation of interference is an another way to reduce transmit power as well as minimize power in spectrum with the help of fractional frequency reuse [164], [165], networked MIMO, massive MIMO [166], [167], beamforming [168] techniques.…”

Section: Conclusion and Future Research Issuesmentioning

confidence: 99%

Energy Efficiency Tradeoff Mechanism Towards Wireless Green Communication: A Survey

Mahapatra¹,

Nijsure

Kaddoum

et al. 2016

IEEE Commun. Surv. Tutorials

191

View full text Add to dashboard Cite

Abstract-Energy efficient (EE) communication has earnedtremendous interest in recent years due to ever increasing number of wireless devices operating in shrinking cells, while demanding high data rates with high Quality of Services (QoS) and Quality of Expectation (QoE). To support these objectives, energy is consumed in every protocol layer. Establishing and maintaining a successful wireless communication link to simultaneously achieve all these objectives becomes challenging since the energy consumption requirements of the user and network are different for different objectives. Thus, there is a need for tradeoff techniques to achieve energy efficiency in each protocol layer. In this paper, we provide a survey of different tradeoff mechanisms proposed in the literature. The EE tradeoffs have been classified based on each protocol layer and discussed its affect in the network energy efficiency. These other QoS parameters include spectral efficiency, deployment, delay, routing, scheduling, bandwidth and coding etc. This survey also discusses the various EE techniques to improve energy-efficiency in infrastructure mode. Finally, the work provides an discussion, where impact of EE tradeoffs have been presented based on different wireless architecture towards realizing a green wireless communication network.

show abstract

“…An energy-efficient carrier aggregation scheme is considered in [17], while large-scale MIMO based systems are discussed in [18]. Similarly, the designs of [19]- [21] are either for singlecell or fixed networks. However, these existing algorithms are only based on local system performance metrics, but ignore large-scale mobile-traffic variations, such as the temporal and geographic fluctuations of the users' behaviors.…”

Section: A Related Workmentioning

confidence: 99%

Joint Energy-Spectral-Efficiency Optimization of CoMP and BS Deployment in Dense Large-Scale Cellular Networks

Zhao

Chen

Zhao

et al. 2017

IEEE Trans. Wireless Commun.

Self Cite

View full text Add to dashboard Cite

Abstract-In this paper, the energy-spectral efficiency (ESE) benefiting from the joint optimization of coordinated multipoint (CoMP) transmission and base station (BS) deployment are evaluated in the context of dense large-scale cellular network. We first derive a closed-form network ESE expression for a largescale CoMP-enhanced network, which allows us to quantify the influence of key network parameters on the achievable network ESE, including the BS density and the cooperation activation probability characterized by a CoMP activation factor as well as the users' behaviors, such as their geographical mobile-traffic intensity and average user rate. With the aid of this tractable ESE expression and for a given BS density, we next formulate a cellular-scenario-aware CoMP activation optimization problem whilst considering the users' outage probability as constraints to maximize the network's ESE. We then further jointly optimize the CoMP activation factor and the BS density to maximize the network ESE, again under the constraint of the users' outage probability. Our simulation results confirm the accuracy of our analysis and verify the impact of several key parameters on the network ESE. Finally, the ESE improvement of our proposed strategies is evaluated under diverse scenarios, which provides valuable insight into joint CoMP and BS deployment optimization in dense large-scale cellular networks.

show abstract

Spectral and Energy Spectral Efficiency Optimization of Joint Transmit and Receive Beamforming Based Multi-Relay MIMO-OFDMA Cellular Networks

Cited by 33 publications

References 39 publications

Joint transmit and receive beamforming for multi-relay MIMO-OFDMA cellular networks

Joint transmit and receive beamforming for multi-relay MIMO-OFDMA cellular networks

Energy Efficiency Tradeoff Mechanism Towards Wireless Green Communication: A Survey

Joint Energy-Spectral-Efficiency Optimization of CoMP and BS Deployment in Dense Large-Scale Cellular Networks

Contact Info

Product

Resources

About