Adaptive thresholding based optimal rate and MIMO mode selection scheme for IEEE 802.11n WLAN

The emergence of multiple antenna technology in IEEE 802.11n WLAN  has resulted in performance improvement in terms of throughput as well as transmission reliability as compared to legacy standards. Link adaptive transmission is critical to WLAN. Most of the existing algorithms for MIMO mode adaptation (between spatial multiplexing and diversity encoding) use fixed SNR switching thresholds for rate selection. The use of a fixed threshold in both MIMO modes, however, can only provide smaller throughput gain. The present studies on link adaptation do not consider the fundamental characteristic difference in the diversity encoding and spatial multiplexing encoding for MIMO. In this paper we propose a novel adaptive thresholding based optimal rate and MIMO mode (ORMM) algorithm for 802.11n wireless network. The proposed scheme adaptively switches between two SNR switching threshold vectors, separately determined for each MIMO mode analytically. Simulations over the Rayleigh fading channel shows that ORMM outperforms the existing approach of MIMO rate adaptation based on the use of fixed switching thresholds for rate selection

Experimental Evaluation of Large Scale WiFi Multicast Rate Control

WiFi multicast to very large groups has gained attention as a solution for multimedia delivery in crowded areas. Yet, most recently proposed schemes do not provide performance guarantees and none have been tested at scale. To address the issue of providing high multicast throughput with performance guarantees, we present the design and experimental evaluation of the Multicast Dynamic Rate Adaptation (MuDRA) algorithm. MuDRA balances fast adaptation to channel conditions and stability, which is essential for multimedia applications. MuDRA relies on feedback from some nodes collected via a light-weight protocol and dynamically adjusts the rate adaptation response time. Our experimental evaluation of MuDRA on the ORBIT testbed with over 150 nodes shows that MuDRA outperforms other schemes and supports high throughput multicast flows to hundreds of receivers while meeting quality requirements. MuDRA can support multiple high quality video streams, where 90% of the nodes report excellent or very good video quality

AARF-HT: Adaptive Auto Rate Fallback for High-Throughput IEEE 802.11n WLANs

Wireless Local Area Network (WLAN) has been progressing rapidly. The IEEE 802.11n Physical (PHY) layer provides wider channel bandwidth, shorter guard interval, and up to four data streams. Therefore PHY 802.11n has a maximum of 128 data rate options from 6.5 Mbps to 600 Mbps. In addition, Medium Access Control (MAC) has been added Aggregate MAC Protocol Data Unit (AMDPU) scheme. If AMPDU is transmitted with a data rate corresponding to the channel conditions, then the probability AMPDU is received without error becomes increased. MAC determines the data rate used for transmitting AMPDU using a rate adaptation algorithm. Therefore some papers have proposed rate adaptation algorithms based on channel conditions. In this paper we propose a new rate adaptation algorithm that we call Adaptive Auto Rate Fallback for High Throughput (AARF-HT). Our development is done using NS-3 simulator version 3.26. AARF-HT algorithm performance is also tested through a number of simulations extensively. The simulation results show the data rate adaptation function based on the channel width, guard interval and the number of spatial streams in IEEE 802.11n WLAN has functioned well. The test results also show the AARF-HT algorithm resulted in higher throughput compared to the AARF algorithm

Optimal Rate Sampling in 802.11 Systems

In 802.11 systems, Rate Adaptation (RA) is a fundamental mechanism allowing transmitters to adapt the coding and modulation scheme as well as the MIMO transmission mode to the radio channel conditions, and in turn, to learn and track the (mode, rate) pair providing the highest throughput. So far, the design of RA mechanisms has been mainly driven by heuristics. In contrast, in this paper, we rigorously formulate such design as an online stochastic optimisation problem. We solve this problem and present ORS (Optimal Rate Sampling), a family of (mode, rate) pair adaptation algorithms that provably learn as fast as it is possible the best pair for transmission. We study the performance of ORS algorithms in both stationary radio environments where the successful packet transmission probabilities at the various (mode, rate) pairs do not vary over time, and in non-stationary environments where these probabilities evolve. We show that under ORS algorithms, the throughput loss due to the need to explore sub-optimal (mode, rate) pairs does not depend on the number of available pairs, which is a crucial advantage as evolving 802.11 standards offer an increasingly large number of (mode, rate) pairs. We illustrate the efficiency of ORS algorithms (compared to the state-of-the-art algorithms) using simulations and traces extracted from 802.11 test-beds.Comment: 52 page

TOWARDS A HOLISTIC RATE ADAPTION FOR 802.1 1N/AC MIMO SYSTEMS

Master'sMASTER OF SCIENC

NeuRA: Using Neural Networks to Improve WiFi Rate Adaptation

Although a variety of rate adaptation algorithms have been proposed for 802.11 devices, sampling-based algorithms are preferred and used in practice because they only require frame loss information which is available on all devices. Unfortunately, sampling can impose significant overheads because it can lead to excessive frame loss or the choice of suboptimal rates. In this thesis, we design a novel neural network based rate adaptation algorithm, called NeuRA. NeuRA significantly improves the efficiency of sampling in rate adaptation algorithms by using a neural network model to predict the expected throughput of many rates, rather than sampling their throughput. Furthermore, we propose a feature selection technique to select the best set of rates to sample. Despite decades of research on rate adaptation in 802.11 networks, there are no definitive results which determine which algorithm is the best or if any algorithm is close to optimal. We design an offline algorithm that uses information about the fate of future frames to make statistically optimal frame aggregation and rate adaptation decisions. This algorithm provides an upper bound on the throughput that can be obtained by practical online algorithms and enables us to evaluate rate adaptation algorithms with respect to this upper bound. Our trace-based evaluations using a wide variety of real-world scenarios show that NeuRA outperforms the widely-used Minstrel HT algorithm by up to 24% (16% on average) and the Intel iwl-mvm-rs algorithm by up to 32% (13% on average). Moreover, the upper bound given by the offline optimal algorithm shows a throughput up to 58% (30% on average) higher than Minstrel HT and up to 31% (12% on average) higher than NeuRA. Hence, NeuRA reduces the gap in throughput between Minstrel HT and the offline optimal algorithm by half. Additionally, our results show that several-fold improvements over Minstrel HT shown in previous work are unlikely to be obtained in real-world scenarios. Finally, we implement NeuRA using the Linux ath9k driver to show that the neural network processing requirements are sufficiently low to be practical and that NeuRA can be used to obtain statistically significant improvements in throughput when compared with Minstrel HT

A channel model and coding for vehicle to vehicle communication based on a developed V-SCME

Over the recent years, VANET communication has attracted a lot of attention due to its potential in facilitating the implementation of 'Intelligent Transport System'. Vehicular applications need to be completely tested before deploying them in the real world. In this context, VANET simulations would be preferred in order to evaluate and validate the proposed model, these simulations are considered inexpensive compared to the real world (hardware) tests. The development of a more realistic simulation environment for VANET is critical in ensuring high performance. Any environment required for simulating VANET, needs to be more realistic and include a precise representation of vehicle movements, as well as passing signals among different vehicles. In order to achieve efficient results that reflect the reality, a high computational power during the simulation is needed which consumes a lot of time. The existing simulation tools could not simulate the exact physical conditions of the real world, so results can be viewed as unsatisfactory when compared with real world experiments. This thesis describes two approaches to improve such vehicle to vehicle communication. The first one is based on the development of an already existing approach, the Spatial Channel Model Extended (SCME) for cellular communication which is a verified, validated and well-established communication channel model. The new developed model, is called Vehicular - Spatial Channel Model Extended (V-SCME) and can be utilised for Vehicle to Vehicle communication. V-SCME is a statistical channel model which was specifically developed and configured to satisfy the requirements of the highly dynamic network topology such as vehicle to vehicle communication. V-SCME provides a precise channel coefficients library for vehicle to vehicle communication for use by the research community, so as to reduce the overall simulation time. The second approach is to apply V-BLAST (MIMO) coding which can be implemented with vehicle to vehicle communication and improve its performance over the V-SCME. The V- SCME channel model with V-BLAST coding system was used to improve vehicle to vehicle physical layer performance, which is a novel contribution. Based on analysis and simulations, it was found that the developed channel model V-SCME is a good solution to satisfy the requirements of vehicle to vehicle communication, where it has considered a lot of parameters in order to obtain more realistic results compared with the real world tests. In addition, V-BLAST (MIMO) coding with the V-SCME has shown an improvement in the bit error rate. The obtained results were intensively compared with other types of MIMO coding