On Designing Efficient High-Speed Wireless LANs

Abstract

In the last decade, wireless LANs (WLANs) based on the IEEE 802.11 standards have become ubiquitous in our daily lives. During this time we have seen more than 10-fold increase in usage and the number of wireless devices. To satisfy ever increasing demands, physical layer (PHY) data rates in WLANs have scaled from a few Mbits/sec in 802.11g to hundreds of Mbits/sec in IEEE 802.11n to over Gbits/sec in the IEEE 802.11ac standard. In addition, due to the emergence of popular online services, such as YouTube and Netflix, there has been persistent traffic growth due to real-time applications (e.g., video streaming). These trends bring about new performance challenges that are likely to become problematic for high-speed WLANs: These challenges include (a) achieving high user-level throughputs at high PHY data rates and (b) meeting the quality of service requirements of diverse applications (e.g., video streaming, web surfing, and bulk transfers) when they co-exist in a WiFi network. Due to the shared nature of the wireless medium, a carrier sensing based random access protocol is used in all 802.11-based standards. To arbitrate access to the channel, wireless access protocols introduce overheads like backoffs, preambles, and acknowledgements that lower performance efficiency at high data rates thereby resulting in low throughput. To address this inefficiency, recentWiFi standards (e.g., 802.11n/ac) allow (a) frame aggregation, whereby multiple frames are transmitted as a single aggregate frame on every channel access, and (b) block acknowledgements, whereby a single frame is to used for acknowledging the receipt of several frames. These features amortize the contention overhead over multiple frames and thus improve efficiency. At high data rates, frame aggregation introduces two challenges. First, sending large aggregate frames in a single transmission increases the opportunity cost of losing a frame, which leads to greater degradation in performance. In WiFi networks, frame losses can occur due to a weak signal, collisions, or hidden nodes. The MAC layer should respond differently to different types of losses. To achieve high performance, it is essential to infer the cause of frame loss accurately. We propose, implement and evaluate BLMon, a framework for loss differentiation that uses loss patterns within aggregate frames and their retries to achieve loss differentiation accurately and with low overhead. The second challenge arises in the presence of a mix of traffic, ranging from delay sensitive real-time applications to bulk file transfers that require high throughput. We show that using QoS mechanisms in high-speed WLANs presents a tradeoff between maximizing the performance of real-time applications and achieving high throughput. We design SlickFi; a service differentiation scheme that addresses this tradeoff and simultaneously maximizes the performance of real-time applications and network throughput. SlickFi achieves this by (a) isolating different types of traffic in non-overlapping parts of the spectrum by mapping them to different radios; and (b) adapting channel width on a per-frame basis to make efficient use of the wireless channel. The proposed solutions are readily deployable on commodity devices using only software level changes. We demonstrate the validity of our solutions by performance evaluation over a real testbed in diverse scenarios.