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Evaluation of Rate Adaptation Algorithms in IEEE 802.11 Networks electronics Article Evaluation of Rate Adaptation Algorithms in IEEE 802.11 Networks Ibrahim Sammour *,† and Gerard Chalhoub † LIMOS, University of Clermont-Auvergne, 63170 Aubière, France; [email protected] * Correspondence: [email protected]; Tel.: +33-6-5932-5635 † These authors contributed equally to this work. Received: 31 July 2020; Accepted: 29 August 2020; Published: 3 September 2020 Abstract: Wireless technologies are being used in various applications for their ease of deployment and inherent capabilities to support mobility. Most wireless standards supports multiple data rates that may vary between few Mbps to few Gbps. Reaching the maximum supported data rate is what most application seek for. Nevertheless, the choice of data rates is very closely related to the quality of communication links and their stability. IEEE 802.11 standard introduced multi-rate support, since then, a lot of research has been done on rate adaptation, dealing with the different parameters that lead to an estimation of the channel conditions and the metrics that affect the network performance. In this paper, we present some of the popular rate adaptation schemes and summarize their characteristics. We categorize them as well into different categories according to their design and functionalities in terms of the strategies that are used to estimate channel conditions and decision making. We implemented some algorithms from the different categories in the network simulator NS-3 in order to evaluate their performance under different scenarios in Ad hoc and infrastructure modes. We present the lessons learned as well as our insights for future research work that can enhance the current approaches in the literature. Keywords: rate adaptation algorithms; wifi; IEEE 802.11; mobility; interference 1. Introduction Wireless communication technologies are a very attractive solution for establishing connectivity between mobile nodes with minimal cable deployment. Many applications rely on such technologies in order to operate such as C-ITS (Cooperative—Intelligent Transport Systems) applications for vehicular networks [1,2], precision farming with mobile connected engines [3,4], the wide variety of applications used on mobile phones using cellular networks. Most of these applications require the support of mobile nodes while maximizing the throughput. Maximum throughput is achieved when the communication technology is able to reach its maximum possible physical data rates. The communication links should be of very good quality and stable in order to reach these data rates. This is a very challenging aspect to guarantee under mobility and in the presence of interference. Indeed, mobility will have a negative impact on the Received Signal Strength (RSS) when the distance between the transmitter and the receiver increases, and interference will decrease the Signal to Interference plus Noise Ratio (SINR). Hence, most communication technologies adopt dynamic rate adaptation algorithms in order to choose the best possible data rate that allows the establishment of communications [5–8]. Rate Adaptation is one of the key aspects of the functionalities of IEEE 802.11’s physical layer. It works by assessing the channel conditions and taking a decision to adapt the transmission rate by selecting a combination of transmission features, such as the modulation and coding schemes (MCS), guard interval, and channel width. These features can be selected depending on the version Electronics 2020, 9, 1436; doi:10.3390/electronics9091436 www.mdpi.com/journal/electronics Electronics 2020, 9, 1436 2 of 16 of the standard being used. When the signal is strong when compared to the level of noise and interference, an algorithm can select a high transmission rate, which leads to less occupancy of the channel and higher throughput. On the other hand, when the signal is weak as compared to the level of noise and interference, a lower transmission rate will ensure the delivery of the packets, but leads to more occupancy time. A lot of factors affect the decision making, such as node mobility, interference, and channel fading. Selecting the appropriate transmission rate after estimating channel conditions is an important task in improving the performance of wireless links. Rate adaptation schemes rely on certain metrics, such as Frame Loss Ratio (FLR) and SINR. A good rate adaptation scheme is defined by its responsiveness to fast channel changes and achieving the highest possible throughput. Rate adaptation schemes have been an active research topic for about two decades, many algorithms have been proposed in the literature. Very few of them have been tested in fully mobile IEEE 802.11 networks. The primary contributions of this paper are (i) categorization of rate adaptation schemes proposed in the literature, (ii) evaluation through simulation of the performance of four different rate adaptation algorithms under different scenarios highlighting the impact of mobility and interference in Ad hoc and infrastructure modes using NS-3 simulator, and (iii) presenting the lessons learned as well as our insights for future research work that can enhance existing approaches. The rest of the paper is organized, as follows. In Section2, we list and categorize several rate adaptation schemes explaining the algorithms focusing on how the channel conditions are estimated and their decision making. In Section3, we present our simulation scenarios and analyze the obtained results. In Section4, we present the lessons learned as well as our insights for future research work on rate adaption techniques. Finally, we conclude the paper in Section5. 2. Related Work In this section, we will present a brief summary of the main characteristics of existing rate adaptation algorithms (RAA). RAAs can be classified into various categories according to the metrics that they use to evaluate the channel or link quality such as frame loss and SNR in [9], or consecutive transmission count, frame loss ratio, transmission time, throughput, SNR, bit error rate, and combined metrics in [10]. We chose to classify RAAs into three categories: (i) Explicit Feedback, in which RAAs base their bit rate adaptation on the receiver feedback, (ii) Implicit Feedback, in which RAAs base their adaptation on the information available on the sender side, and (iii) Hybrid, which is a category that combines information from feedback of the receiver and information available to the sender. In what follows we will describe each of these categories by selecting representative RAAs of each category. Table1 summarizes the information about the different RAA algorithms presented in this section. 2.1. Explicit Feedback Explicit Feedback is known as a receiver-driven rate adaptation scheme where the receiver takes a decision that is based on its estimation of the channel conditions and relays it back to the sender via different approaches using control frames, such as Clear To Send (CTS) and Acknowledgements (ACK). On Demand Feedback Rate Adaptation (OFRA) [11] is a receiver based rate adaptation algorithm (RAA), where the channel quality is estimated at the receiver based on SINR values. The receiver selects the optimal bit rate from a lookup table created previously. It contains a set of thresholds at which data rates should be changed. This information is fed back to the sender on demand while using ACK frames. In case of an ACK-less traffic, OFRA uses a specially designed feedback frame. OFRA presents some limitations, such as modifying the ACK frame that is a violation of the standard, introduction of additional overhead with the special feedback frame, which is sent at the lowest data rate. SINR-aware Intra-frame Rate Adaptation (SIRA) [12] selects two rates for a single Aggregate MAC Protocol Data Unit (A-MPDU) transmission. It finds the starting symbol “I” when the rate should be changed. When the condition SINRi < SINRth is met, the symbol “I” is found. SINRth is Electronics 2020, 9, 1436 3 of 16 the minimum SINR at which the theoretical Bit Error Rate (BER) of the primary rate is less than 10−4. Subsequently, “I” is fed back to the sender via the BlockAck. The main drawback of SIRA is that it only determines two rates for an aggregated frame, which may not be enough for a fast-changing channels. An Ideal RAA is implemented in the famous network simulator NS-3. This RAA initially creates a table of SINR and MCS pairs. The SINR thresholds in this table ensure selecting an MCS that leads to a BER below a certain value. For example, the default value is 10−5), the SINR is fed back from the receiver to the transmitter via a perfect out-of-band mechanism. The main drawback of this mechanism is the use of an out-of-band channel for sending back the feedback which is not available in Industrial, Scientific, and Medical (ISM) bands used by IEEE 802.11. Table 1. Summary of existing rate adaptation algorithms. Modify Name Category Metrics Pros Cons Standard Explicit Instantaneous feedback and Uses perfect out-of-band Ideal SINR Yes Feedback rate decision mechanism for feedback Explicit Feedback is sent only when a Additional frame is sent for OFRA SINR Yes Feedback new rate is selected feedback at the lowest rate In a fast-changing channel Uses 2 rates for a single we may need more than 2 Explicit SIRA SINR A-MPDU transmission to rates for a single A-MPDU No Feedback adapt with channel changes transmission to adopt to channel changes Prefers to use an optimal Implicit Does not take interference Strale Throughput A-MPDU length instead
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