Coordinated beamforming in multicell networks with Channel State Information exchange delays

Godana, Bruhtesfa; Gesbert, David

doi:10.1109/pimrc.2013.6666229

Cited by 5 publications

(5 citation statements)

References 18 publications

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“…In the network, we assume that CU and DU are co-located, so the portion of Xhaul under analysis in the paper is only between RU and DU. In this network, users are connected to RUs via CoMP beamforming [7], [8]. Each RU is located at a different geographical location on the (x,y) coordinate plane.…”

Section: Network Modelmentioning

confidence: 99%

“…The variation in the bit rate in the Xhaul will depend on factors such as the type of beamforming being used, user mobility, user allocation to RUs and thus to different paths in the Xhaul, and resource allocation in both the radio access network (RAN) and Xhaul. Delays in the Xhaul will influence the achievable beamforming gains and achievable user bit rate [7], [8].…”

Section: Introductionmentioning

confidence: 99%

“…With FlexE [9] incorporated into the Xhaul, the achievable user bit rate will depend on the over and under-allocation of slots in the FlexE calendar, as this will affect serialization and buffering delays and, in turn, the Xhaul's total latency. The Xhaul latency affects the delay in feeding back-channel state information via uplink to the DU/CU where beamforming vectors are computed, especially if coordinated multi-point (CoMP) is employed [8], [12]. Therefore, to maximize the achievable user bit rate, a resource allocation scheme that can anticipate the traffic in advance and appropriately allocate resources is required.…”

Section: Introductionmentioning

confidence: 99%

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User and resource allocation in latency constrained Xhaul via reinforcement learning

Chughtai¹,

Noor²,

Asimakopoulos³

et al. 2023

J. Opt. Commun. Netw.

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The Flexible Ethernet (FlexE) is envisioned for the provisioning of different services and hard slicing of the Xhaul in 5G and beyond networks. For efficient bandwidth utilization in the Xhaul, traffic prediction for slot allocation in FlexE calendars is required. Further, if coordinated multipoint (CoMP) is used, the allocation of users to remote units (RUs) with an Xhaul path of lower latency to the distributed unit/central unit will increase the achievable user bit rate. In this paper, the use of multi-agent deep reinforcement learning (DRL) for optimal slot allocations in a FlexE-enabled Xhaul, for traffic generated through CoMP, and for offloading users among different RUs is explored. In simulation results, the DRL agent can learn to predict input traffic patterns and allocate slots with the necessary granularity of 5 Gbps in the FlexE calendar. The resulting gains are expressed in terms of the reduction of mean over-allocation of slots in the FlexE calendar in comparison to the prediction obtained from an autoregressive integrated moving average (ARIMA) model. Simulations indicate that DRL outperforms ARIMA-based prediction by up to 11.6%

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Section: Network Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

User and resource allocation in latency constrained Xhaul via reinforcement learning

Chughtai¹,

Noor²,

Asimakopoulos³

et al. 2023

J. Opt. Commun. Netw.

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“…In this mechanism, there are two drawbacks. First, the exchange of CSIs between cooperating BSs incurs backhaul delay in addition to the feedback delay from UEs (refer [120,121] for further details). Secondly, in case the CSI is perturbed (due to quantization effects, noise etc) during exchange via backhaul, the interference at the neighboring cells cannot be perfectly removed resulting in sub-optimal performance [122].…”

Section: A Coordinated Beamformingmentioning

confidence: 99%

Separation Framework: An Enabler for Cooperative and D2D Communication for Future 5G Networks

Mustafa

Imran

Shakir

et al. 2016

IEEE Commun. Surv. Tutorials

113

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Soaring capacity and coverage demands dictate that future cellular networks need to migrate soon toward ultra-dense networks. However, network densification comes with a host of challenges that include compromised energy efficiency, complex interference management, cumbersome mobility management, burdensome signaling overheads, and higher backhaul costs. Interestingly, most of the problems that beleaguer network densification stem from legacy networks' one common feature, i.e., tight coupling between the control and data planes regardless of their degree of heterogeneity and cell density. Consequently, in wake of 5G, control and data planes separation architecture (SARC) has recently been conceived as a promising paradigm that has potential to address most of the aforementioned challenges. In this survey, we review various proposals that have been presented in the literature so far to enable SARC. More specifically, we analyze how and to what degree various SARC proposals address the four main challenges in network densification, namely: energy efficiency, system level capacity maximization, interference management, and mobility management. We then focus on two salient features of future cellular networks that have not yet been adapted in legacy networks at wide scale and thus remain a hallmark of 5G, i.e., coordinated multipoint (CoMP) and device-to-device (D2D) communications. After providing necessary background on CoMP and D2D, we analyze how SARC can particularly act as a major enabler for CoMP and D2D in context of 5G. This article thus serves as both a tutorial as well as an up-to-date survey on SARC, CoMP, and D2D. Most importantly, this survey provides an extensive outlook of challenges and opportunities that lie at the crossroads of these three mutually entangled emerging technologies

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“…There are currently proposed solutions to the outdated CQI challenge on cooperative diversity‐based networks. These include; the use of multiple relays instead of a single relay , the use of additional private messages with the originally sent signal , the use of an average signal to noise ratio (SNR) criterion in designing an optimal combination of beamformers , and the use of multiple estimates of signal quality from different frequencies or times . The signal quality estimation in was carried out using the mean square error technique while the estimation error was characterized as additive Gaussian noise.…”

Section: Introductionmentioning

confidence: 99%

Predictive relay-selection cooperative diversity in land mobile satellite systems

Awoyemi

Walingo

Takawira

2015

Int. J. Satell. Commun. Network.

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Abstract-Cooperative diversity protocols promise a new dimension of diversity that provides better communication by engaging nearby relays in forming a 'virtual' array of antennas for combined signal transmission. The current incremental cooperative diversity algorithms incrementally select best relay(s) to cooperate based on the channel quality reported by the relays. However, the algorithms do not take into consideration the fact that the chosen best relay(s) at estimation may not always be best at the time of communication. This is due to the time delay between the relay selection and its transmission of signal (problem of outdated Channel Quality Information). To solve this problem, the concept of channel prediction is introduced and employed whereby each relay determines a predicted value of its Channel Quality Information (CQI) based on its past measurements. The paper therefore develops a novel predictive relay-selection (PRS) cooperative diversity model which seeks to improve Land Mobile Satellite (LMS) communication through prediction protocols. In the model, the chosen best relay is the one with the best predicted CQI value instead of the traditional outdated one. Performance analysis of outage probability and average bit error probability for the newly developed PRS cooperation shows that the PRS cooperation is better than direct and outdated CQI relay communication.

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Coordinated beamforming in multicell networks with Channel State Information exchange delays

Cited by 5 publications

References 18 publications

User and resource allocation in latency constrained Xhaul via reinforcement learning

User and resource allocation in latency constrained Xhaul via reinforcement learning

Separation Framework: An Enabler for Cooperative and D2D Communication for Future 5G Networks

Predictive relay-selection cooperative diversity in land mobile satellite systems

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