Simple Contention Resolution via Multiplicative Weight Updates

We consider the classic contention resolution problem, in which devices conspire to share some common resource, for which they each need temporary and exclusive access. To ground the discussion, suppose (identical) devices wake up at various times, and must send a single packet over a shared multiple-access channel. In each time step they may attempt to send their packet; they receive ternary feedback {0,1,2^+} from the channel, 0 indicating silence (no one attempted transmission), 1 indicating success (one device successfully transmitted), and 2^+ indicating noise. We prove that a simple strategy suffices to achieve a channel utilization rate of 1/e-O(epsilon), for any epsilon>0. In each step, device i attempts to send its packet with probability p_i, then applies a rudimentary multiplicative weight-type update to p_i. p_i <- { p_i * e^{epsilon} upon hearing silence (0), p_i upon hearing success (1), p_i * e^{-epsilon/(e-2)} upon hearing noise (2^+) }. This scheme works well even if the introduction of devices/packets is adversarial, and even if the adversary can jam time slots (make noise) at will. We prove that if the adversary jams J time slots, then this scheme will achieve channel utilization 1/e-epsilon, excluding O(J) wasted slots. Results similar to these (Bender, Fineman, Gilbert, Young, SODA 2016) were already achieved, but with a lower constant efficiency (less than 0.05) and a more complex algorithm

On Optimizing the Backoff Interval for Random Access Schemes

To improve the channel throughput and the fairness of random access channels, we propose a new backoff algorithm, namely, the sensing backoff algorithm (SBA). A novel feature of the SBA scheme is the sensing mechanism, in which every node modifies its backoff interval according to the results of the sensed channel activities. In particular, every active node sensing the successful transmission decreases its backoff interval by an additive factor of the transmission time of a packet. In order to find the optimum parameters for the SBA scheme, we have studied the optimum backoff intervals as a function of different number of active nodes (N) in a single transmission area with pure ALOHA-type channels.We have found that the optimum backoff interval should be 4N times the transmission time of a packet when the random access channel operates under a pure ALOHA scheme. Based on this result, we have numerically calculated the optimum values of the parameters for SBA, which are independent of N. The SBA scheme operates close to the optimum backoff interval. Furthermore, its operation does not depend on the knowledge of N. The optimum backoff interval and the SBA scheme are also studied by simulative means. It is shown that the SBA scheme out-performs other backoff schemes, such as binary exponential backoff (BEB) and multiplicative increase linear decrease (MILD). As a point of reference, the SBA scheme offers a channel capacity of 0.19 when N is 10, while the MILD scheme can only offer 0.125. The performance gain is about 50%

Situation-Aware Rate and Power Adaptation Techniques for IEEE 802.11

The current generation of IEEE 802.11 Wireless Local Area Networks (WLANs) provide multiple data rates from which the different physical (PHY) layers may choose. The rate adaptation algorithm (RAA) is an essential component of 802.11 WLANs which completely determines the data rate a device may use. Some of the key challenges facing data rate selection are the constantly varying wireless channel, selecting the data rate that will result in the maximum throughput, assessing the conditions based on limited feedback and estimating the link conditions at the receiver. Current RAAs lack the ability to sense their environment and adapt accordingly. 802.11 WLANs are deployed in many locations and use the same technique to choose the data rate in all locations and situations. Therefore, these RAAs suffer from the inability to adapt the method they use to choose the data transmission rate. In this thesis, a new RAA for 802.11 WLANs is proposed which provides an answer to the many challenges faced by RAAs. The proposed RAA is termed SARA which stands for Situation-Aware Rate Adaptation, and combines the use of the received signal strength and packet error rate to enable situational awareness. SARA adapts to the current environmental situation experienced at the moment to rapidly take advantage of changing channel conditions. In addition to SARA, a method to optimize the transmission power for, but not limited to, IEEE 802.11 WLANs is proposed which can determine the minimum transmission power required by a station (STA) or base station (BS) for successful transmission of a data packet. The technique reduces the transmission power to the minimum level based on the current situation while maintaining QoS constraints. The method employs a Binary Search to quickly determine the minimum transmission power with low complexity and delay. Such a technique is useful to conserve battery life in mobile devices for 802.11 WLANs. Both algorithms are implemented on an Atheros device driver for the FreeBSD operating system. SARA is compared to the benchmark algorithm SampleRate while an estimate of the energy consumed as well as the energy saved is provided for the minimum transmission power determination