Efficient MAC protocol for JT CoMP in small cells

Lossow, Moritz; Jaeckel, Stephan; Jungnickel, Volker; Braun, Volker

doi:10.1109/iccw.2013.6649413

Cited by 13 publications

(16 citation statements)

References 15 publications

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“…Most algorithms operate at the single resource level, i.e., considering optimization over one sub-carrier or time-slot. Extensions to scenarios with multiple carriers or codes are straightforward, e.g., [39], [41], [73], [85], [92], [101], [109], [111], [114], [115], [116], [126], [147], [155], [171].…”

Section: Mu-mimo Schedulingmentioning

confidence: 99%

“…The other strategies are also implemented for distributed CS/CBF. Recent works have extended these transmission schemes to heterogeneous networks, e.g., [92], [93], [111].…”

Section: ) Joint Cs-cbfmentioning

confidence: 99%

“…Table VI summarizes the reviewed scheduling methods and their associated power allocation strategies: EPA, waterfilling, and adaptive methods. The references listed under the adaptive category depend on the particular objective function Utility-based [30], [57], [76], [80], [88], [95], [105], [108], [110], [111], [113], [118], [133], [135], [138], [141], [142], [143], [152], [157], [159], [162], [175], [178], [184] and system constraints, see Tables II and III. Those references jointly optimize P and W via conventional optimization methods [247] and iterative algorithms.…”

Section: Power Allocationmentioning

confidence: 99%

See 2 more Smart Citations

An Overview on Resource Allocation Techniques for Multi-User MIMO Systems

Castañeda

Silva

Gameiro

et al. 2017

IEEE Commun. Surv. Tutorials

184

123

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Abstract-Remarkable research activities and major advances have been occurred over the past decade in multiuser multipleinput multiple-output (MU-MIMO) systems. Several transmission technologies and precoding techniques have been developed in order to exploit the spatial dimension so that simultaneous transmission of independent data streams reuse the same radio resources. The achievable performance of such techniques heavily depends on the channel characteristics of the selected users, the amount of channel knowledge, and how efficiently interference is mitigated. In systems where the total number of receivers is larger than the number of total transmit antennas, user selection becomes a key approach to benefit from multiuser diversity and achieve full multiplexing gain. The overall performance of MU-MIMO systems is a complex joint multi-objective optimization problem since many variables and parameters have to be optimized, including the number of users, the number of antennas, spatial signaling, rate and power allocation, and transmission technique. The objective of this literature survey is to provide a comprehensive overview of the various methodologies used to approach the aforementioned joint optimization task in the downlink of MU-MIMO communication systems.

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Section: Mu-mimo Schedulingmentioning

confidence: 99%

“…The other strategies are also implemented for distributed CS/CBF. Recent works have extended these transmission schemes to heterogeneous networks, e.g., [92], [93], [111].…”

Section: ) Joint Cs-cbfmentioning

confidence: 99%

Section: Power Allocationmentioning

confidence: 99%

See 1 more Smart Citation

An Overview on Resource Allocation Techniques for Multi-User MIMO Systems

Castañeda

Silva

Gameiro

et al. 2017

IEEE Commun. Surv. Tutorials

184

123

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“…Here, we summarize our adaptive physical layer (PHY) and medium access control (MAC) algorithms for JT-CoMP [18]. Clusters are built based on the Top-N list.…”

Section: Adaptive Algorithms For Jt-compmentioning

confidence: 99%

Backhaul requirements for inter-site cooperation in heterogeneous LTE-Advanced networks

Jungnickel

Manolakis

Jaeckel

et al. 2013

2013 IEEE International Conference on Communications Workshops (ICC)

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In this paper, we provide an overview on backhaul requirements in mobile networks featuring inter-site cooperation. We consider a heterogeneous deployment, where small cells are embedded into macro-cells. For reducing inter-cell interference, we consider intra- and inter-site joint transmission (JT)-coordinated multi-point (CoMP) in fully flexible cooperation clusters. Backhaul traffic for a macro-cell site scales linearly with the number of small cells and multiplicative by introducing cooperation. All features in LTE-Advanced together push the backhaul traffic up to 100 Gbit/s per site. JT-CoMP is sensitive to the feedback delay.We consider an exemplary rural fiber to the home (FTTH) network as mobile backhaul and find that delays between adjacent sites are below 1 ms. Impairment effects due to the delay can be reduced by feedback compression, shorter delays in the air interface and Doppler-delay based channel prediction

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“…In a cellular system, network MIMO has been studied in the name of coordinated multi-point (CoMP). CoMP is expected to be used in next-generation cellular systems such as long-term evolution (LTE)-Advanced [5]- [7]. CoMP allows cellular base stations to do network MIMO cooperation to mitigate inter-cell interference.…”

Section: Related Workmentioning

confidence: 99%

TARC: Throughput-Aware Random Scalable Clustering for Network MIMO

Kobayashi¹,

Ng²,

Seah³

et al. 2014

2015 IEEE Global Communications Conference (GLOBECOM)

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Current one-to-one wireless communications is reaching the Shannon limit. Previous works have studied space multiplexing schemes, e.g., superposition coding and successive interference cancellation, to overcome the limitation. In this paper, we focus on a network multiple-input multiple-output (MIMO) architecture, which is one such space multiplexing scheme, where multiple access points in the network MIMO system cooperate with each other to improve wireless communications capacity. However, channel sounding overhead for estimating the channel state information of every path between multiple access points and multiple clients is a significant problem. Likewise, the computational overhead for deciding which paths to use for the network MIMO transmission is also high. We propose Throughput-Aware Random Clustering (TARC) of access points to reduce the network MIMO overhead. TARC takes a cross-layer approach for choosing access points to participate in network MIMO transmission on the physical layer based on throughput in the data link layer. From our validation using simulations, we show that the proposed method is able to achieve approximately 2.5 times higher throughput than using all access points in the network and 1.4 times better throughput than adopting static cluster sizes.

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Efficient MAC protocol for JT CoMP in small cells

Cited by 13 publications

References 15 publications

An Overview on Resource Allocation Techniques for Multi-User MIMO Systems

An Overview on Resource Allocation Techniques for Multi-User MIMO Systems

Backhaul requirements for inter-site cooperation in heterogeneous LTE-Advanced networks

TARC: Throughput-Aware Random Scalable Clustering for Network MIMO

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