IEEE 802.11ac: from channelization to multi-user MIMO

Bejarano, Oscar; Knightly, Edward W.; Park, Min-Young

doi:10.1109/mcom.2013.6619570

Cited by 191 publications

(104 citation statements)

References 2 publications

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“…It is assumed that each AP covers a cell with an 80 m radius, being facilitated with beamforming and MU-MIMO to support up to 8 spatial streams. Five radio channels are taken for the 80 MHz APs, following European Union regulations, [30]. In contrast to former RATs, APs use the same set of links for up-and download streams.…”

Section: Scenariomentioning

confidence: 99%

Modelling virtual radio resource management in full heterogeneous networks

Khatibi

Correia

2017

J Wireless Com Network

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Virtual radio access networks (RANs) is the candidate solution for 5G access networks, the concept of virtualised radio resources completing the virtual RAN paradigm. This paper proposes a new analytical model for the management of virtual radio resources in full heterogeneous networks. The estimation of network capacity and data rate allocation are the model's two main components. Based on the probability distribution of the signal-tointerference-plus-noise-ratio observed at the user terminal, the model leads to the probability distribution for the total network data rate. It considers different approaches for the estimation of the total network data rate, based on different channel qualities, i.e., optimistic, realistic and pessimistic. The second component uses the outcome of the first one in order to maximise the weighted data rate subject to the total network capacity, the SLAs (service level agreements) of Virtual Network Operators (VNOs), and fairness. The weights for services in the objective function of the resource allocation component enable the model to have prioritisation among services. The performance of the proposed model is evaluated in a practical heterogeneous access network. Results show an increase of 2.5 times in network capacity by implementing an access point at the centre of each cell of a cellular network. It is shown that the cellular network capacity itself can vary from 0.9 Gbps in the pessimistic approach up to 5.5 Gbps in the optimistic one. Finally, the isolation of service classes and VNOs by means of virtualisation of radio resources is clearly demonstrated.

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Section: Scenariomentioning

confidence: 99%

Modelling virtual radio resource management in full heterogeneous networks

Khatibi

Correia

2017

J Wireless Com Network

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“…In general, compute-intensive tasks require a small amount of data transfer compared to huge amount of complex processing. Moreover, the wireless communication technologies also have evolved to support several megabits for cellular networks to several Gigabits for Wi-Fi networks [12], [13], [14]. In a recent work [15] Khoda et al showed that task migration from a local mobile device to a remote cloud can maximise energy saving while maintaining strict latency requirements of user tasks in 5G systems.…”

Section: Proceedings Of the 23rd International Conference On Automatimentioning

confidence: 99%

Effective task migration to reduce execution time in mobile cloud computing

Saha

Hasan

2017

2017 23rd International Conference on Automation and Computing (ICAC)

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Abstract-With the advancements of mobile technologies, different compute-intensive tasks are emerging rapidly. However, due to resource constraints, these tasks are facing challenges to execute on mobile devices. As a solution to this problem, cloud migration has been introduced to execute a task on the cloud and then to return the results to the user mobile device. In this paper, a cloud migration decision making algorithm for compute-intensive tasks has been proposed to determine the feasibility of execution on a cloud server instead of a mobile device. Furthermore, the performances between mobile and cloud executions have been investigated which shows that the task completion time can be minimised by 6-8 times when a cloud server is utilised.

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“…Now we briefly introduce the MU-MIMO feature supported by 802.11ac [4], the latest amendment to the 802.11 protocol family. 802.11ac allows the AP to use MU-MIMO techniques to simultaneously transmit downlink streams to up to four of its served clients.…”

Section: Mu-mimo In 80211acmentioning

confidence: 99%

Combating inter-cell interference in 802.11ac-based multi-user MIMO networks

Bejarano

Lin

2014

Proceedings of the 20th Annual International Conference on Mobile Computing and Networking

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In an 802.11ac-based MU-MIMO network comprised of multiple cells 1 , inter-cell interference allows only a single AP to serve its clients at the same time, significantly limiting the network capacity. In this work, we overcome this limitation by letting the APs and clients in interfering cells coordinately cancel the inter-cell interference using their antennas for beamforming. To achieve such coordinated interference cancellation in a practical way, we propose a novel two-step optimization. First, without requiring any channel knowledge, each AP and client optimizes the use of its antennas for either data communication or inter-cell interference cancellation, in order to maximize the total number of deliverable streams in the MU-MIMO network. Second, with only partial channel knowledge, each AP and client optimizes their beamforming weights after the optimal antenna usage has been identified in the first step. Our solution, CoaCa, integrates this two-step optimization into 802.11ac with small modifications and negligible overhead, allowing each AP and client to locally perform the two-step optimization. Our experimental evaluation indicates that for a MU-MIMO network with two cells, by cancelling the inter-cell interference CoaCa can convert the majority of the expected number of streams increase (50%-67%) into network capacity improvement (41%-52%).

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IEEE 802.11ac: from channelization to multi-user MIMO

Cited by 191 publications

References 2 publications

Modelling virtual radio resource management in full heterogeneous networks

Modelling virtual radio resource management in full heterogeneous networks

Effective task migration to reduce execution time in mobile cloud computing

Combating inter-cell interference in 802.11ac-based multi-user MIMO networks

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