A contention-based access scheme, Distributed Coordination Function (DCF), is the basic access technology and it works as the basis for 802.11 MAC and its extensions. Using the Carrier-Sense Multiple Access with Collision Avoidance (CSMA/CA) mechanism and the Binary Exponential Backoff (BEB) mechanism, DCF can efficiently avoid multiple stations to transmit data at the same time and thus reduces the collision probability. In addition to BEB, Exponential Increase Exponential Decrease (EIED) is another well known backoff mechanism to avoid retransmission collision. In literature, many researches show that BEB algorithm may result in a poor throughput in a heavy load environment, while the EIED scheme does not perform as well as BEB under a light traffic condition. The emphasis of this paper is to address the shortcomings of the above two schemes and pose a solution to select a better random backoff timer in order to maximize the throughput under various traffic load. In this work, a novel backoff mechanism, Optimal Backoff (OB) mechanism, is proposed. OB can choose an optimal contention window according to current traffic conditions. Analytical and simulation results show that proposed Optimal Backoff mechanism always has highest throughput and lowest packet delay than those of the BEB and EIED mechanisms under both light and heavy traffic scenarios. With the deployment of the proposed OB, we believe that Wireless LAN is able to work perfectly as an extension of legacy mobile networks in t he context of upcoming Next Generation Networks.
In recent years, there has been tremendous interests and progresses in the field of wireless communications. Call admission control (CAC) is the key component to maximize the system utilization under certain QoS constraints such as call blocking rates. Among the CACs, Markov decision process (MDP) approach is a popular method to optimize certern objectives of interest. However, the computation complexity for deriving optimal policies make this approach less accessible to those with large problem size. In this paper, we will address this issue of how the optimal solutions fluctuate as the traffic condition changed using sensitivity analysis technique, in order to cut down unnecessary computing time if optimal policy did not change as the traffic conditions vary. First of all, the LP problem is solved by simplex method to examine the best policy when the optimal solution is found, then the sensitivity analysis technique is used by adding perturbation on traffic parameters to indicate the range to which optimal bases are invariant. The analytical results for computation complexity reduction is shown to analyze the performance under various traffic conditions.