The IEEE 802.11 universe

Hiertz, Guido R.; Denteneer, Dee; Stibor, L.; Zang, Yunpeng; Costa, Xavier Pérez; Walke, Bernhard

doi:10.1109/mcom.2010.5394032

Cited by 248 publications

(104 citation statements)

References 6 publications

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“…One of the key issues in WMN standardization is the adaptation of legacy distributed medium access schemes to share the medium which has inherent unfairness in achieving concurrent transmissions between mesh nodes in a multi-hop mesh network. However, it is important that WMN standards should address these challenging issues without compromising the compatibilities of WMNs to continue to evolve as a cost-effective backhauling technology for WLANs [2] [3] [4].…”

Section: Wmn Design Challenges In Distributed Medium Accessmentioning

confidence: 99%

Achieving Transmission Fairness in Distributed Medium Access Wireless Mesh Networks: Design Challenges, Guidelines and Future Directions

Undugodage¹,

Sarkar²

2013

IJWMN

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Section: Wmn Design Challenges In Distributed Medium Accessmentioning

confidence: 99%

Achieving Transmission Fairness in Distributed Medium Access Wireless Mesh Networks: Design Challenges, Guidelines and Future Directions

Undugodage¹,

Sarkar²

2013

IJWMN

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“…Since its debut in 1997, it comes a way from megabits per second to the upcoming gigabits per second [2], which was achieved by the cable technology not long ago. The currently ongoing IEEE 802.11ac amendment [3] aims to provide an aggregated multi-station throughput of at least 1 gigabit per second in the 5-GHz band.…”

Section: Introductionmentioning

confidence: 99%

Performance analysis of IEEE 802.11ac wireless backhaul networks in saturated conditions

Liao

Bellalta

Barceló

et al. 2013

J Wireless Com Network

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According to the ongoing IEEE 802.11ac amendment, the wireless network is about to embrace the gigabit-per-second raw data rate. Compared with previous IEEE standards, this significant performance improvement can be attributed to the novel physical and medium access control (MAC) features, such as multi-user multiple-input multiple-output transmissions, the frame aggregation, and the channel bonding. In this paper, we first briefly survey the main features of IEEE 802.11ac, and then, we evaluate these new features in a fully connected wireless mesh network using an analytic model and simulations. More specifically, the performance of the MAC scheme defined by IEEE 802.11ac, which employs the explicit compressed feedback (ECFB) mechanism for the channel sounding, is evaluated. In addition, we propose an extended request-to-send/clear-to-send scheme that integrates the ECFB operation to compare with the IEEE 802.11ac-defined one in saturated conditions. The comparison of the two MAC schemes is conducted through three spatial stream allocation algorithms. A simple but accurate analytical model is derived for the two MAC schemes, the results of which are validated with simulations. The observations of the results not only reveal the importance of spatial stream allocations but also provide insight into how the newly introduced features could affect the performance of IEEE 802.11ac-based wireless mesh networks.

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“…In particular, the capacity of point-to-point MIMO channel increases linearly with the minimum of the number of transmit and receive antennas [1], [2]. This has led to the use of MIMO systems in wireless communications standards such as the IEEE 802.11n/802.11ac [3] and LTE [4]. One of the main impediments to successful deployment of MIMO systems is the increased complexity in decoding the received signals.…”

Section: Introductionmentioning

confidence: 99%

Boosting MMSE Receivers Using AdaBoost

Dabeer

Nagaraja

Chockalingam

2013

2013 IEEE 78th Vehicular Technology Conference (VTC Fall)

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Abstract-MIMO systems with several antennas at the transmitter and receiver have the potential to enable high throughputs. One of the challenges in realizing this potential is the design of receivers that scale in terms of computation and performance. Towards this end, in this paper, we propose a receiver that uses a committee of linear receivers, whose parameters are estimated from training data using a variant of the AdaBoost algorithm, a celebrated supervised classification algorithm in machine learning. We call our receiver boosted MMSE (B-MMSE) receiver and we study its performance via simulations. The channel matrix is estimated from the pilot using the MMSE technique. We find that for a 4 × 4 system with a Rayleigh fading channel, using BPSK at a bit error rate (BER) of 10 −2 , our receiver using the estimated channel matrix needs 2 dB less power than the plain MMSE receiver based on the estimated channel matrix. On an average, excluding the training phase, the receiver complexity equals 1.25 linear receivers. Thus the B-MMSE receiver is slightly more complex than the MMSE receiver and substantially less complex than the maximum-likelihood receiver. We also show that the coded BER performance with an outer rate-3/4 Turbo-code has a gain of 5 dB at 10 −4 coded BER when the demodulated output from the proposed method is used in place of plain MMSE detection with MMSE estimated channel.

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The IEEE 802.11 universe

Cited by 248 publications

References 6 publications

Achieving Transmission Fairness in Distributed Medium Access Wireless Mesh Networks: Design Challenges, Guidelines and Future Directions

Achieving Transmission Fairness in Distributed Medium Access Wireless Mesh Networks: Design Challenges, Guidelines and Future Directions

Performance analysis of IEEE 802.11ac wireless backhaul networks in saturated conditions

Boosting MMSE Receivers Using AdaBoost

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