Openwifi : a free and open-source IEEE802.11 SDR implementation on SoC

Open source Software Defined Radio (SDR) project, such as srsLTE and Open Air Interface (OAI), has been widely used for 4G/5G research. However the SDR implementation of the IEEE802.11 (Wi-Fi) is still difficult. The Wi-Fi Short InterFrame Space (SIFS) requires acknowledgement (ACK) packet being sent out in 10μs/16μs(2.4 GHz/5GHz) after receiving a packet successfully, thus the Personal Computer (PC) based SDR architecture hardly can be used due to the latency (≥100μs) between PC and Radio Frequency (RF) front-end. Researchers have to do simulation, hack a commercial chip or buy an expensive reference design to test their ideas. To change this situation, we have developed an open-source full-stack IEEE802.11a/g/n SDR implementation — openwifi. It is based on Xilinx Zynq Systemon-Chip (SoC) that includes Field Programmable Gate Array (FPGA) and ARM processor. With the low latency connection between FPGA and RF front-end, the most critical SIFS timing is achieved by implementing Physical layer (PHY) and low level Media Access Control (low MAC) in FPGA. The corresponding driver is implemented in the embedded Linux running on the ARM processor. The driver instantiates Application Programming Interfaces (APIs) defined by Linux mac80211 subsystem, which is widely used for most SoftMAC Wi-Fi chips. Researchers could study and modify openwifi easily thanks to the modular design. Compared to PC based SDR, the SoC is also a better choice for portable and embedded scenario

Screening interacting factors in a wireless network testbed using locating arrays

Wireless systems exhibit a wide range of configurable parameters (factors), each with a number of values (levels), that may influence performance. Exhaustively analyzing all factor interactions is typically not feasible in experimental systems due to the large design space. We propose a method for determining which factors play a significant role in wireless network performance with multiple performance metrics (response variables). Such screening can be used to reduce the set of factors in subsequent experimental testing, whether for modelling or optimization. Our method accounts for pairwise interactions between the factors when deciding significance, because interactions play a significant role in real-world systems. We utilize locating arrays to design the experiment because they guarantee that each pairwise interaction impacts a distinct set of tests. We formulate the analysis as a problem in compressive sensing that we solve using a variation of orthogonal matching pursuit, together with statistical methods to determine which factors are significant. We evaluate the method using data collected from the w-iLab.t Zwijnaarde wireless network testbed and construct a new experiment based on the first analysis to validate the results. We find that the analysis exhibits robustness to noise and to missing data

Pareto Analysis and Multi-Objective Optimization in Wireless Networks

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Surrogate modeling based cognitive decision engine for optimization of WLAN performance

Due to the rapid growth of wireless networks and the dearth of the electromagnetic spectrum, more interference is imposed to the wireless terminals which constrains their performance. In order to mitigate such performance degradation, this paper proposes a novel experimentally verified surrogate model based cognitive decision engine which aims at performance optimization of IEEE 802.11 links. The surrogate model takes the current state and configuration of the network as input and makes a prediction of the QoS parameter that would assist the decision engine to steer the network towards the optimal configuration. The decision engine was applied in two realistic interference scenarios where in both cases, utilization of the cognitive decision engine significantly outperformed the case where the decision engine was not deployed

Throughput optimization strategies for large-scale wireless LANs

Thanks to the active development of IEEE 802.11, the performance of wireless local area networks (WLANs) is improving by every new edition of the standard facilitating large enterprises to rely on Wi-Fi for more demanding applications. The limited number of channels in the unlicensed industrial scientific medical frequency band however is one of the key bottlenecks of Wi-Fi when scalability and robustness are points of concern. In this paper we propose two strategies for the optimization of throughput in wireless LANs: a heuristic derived from a theoretical model and a surrogate model based decision engine

IEEE 802.11ah Restricted Access Window Surrogate Model for Real-Time Station Grouping

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