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Modern wireless systems are evolving toward a heterogeneous environment where multiple radio access technologies (RATs) coexist. Most of the RATs in current use operate independently of each other, however, there is an increasing demand for coordination among different RATs in order to support exponentially growing wireless traffic. Our goal in this paper is to design a super-MAC that integrates multiple RATs in a tightly coupled manner. To this end, we propose an architecture for super-MAC, and develop an algorithm that splits IP traffic into multiple RATs and controls each RAT to exploit multi-RAT diversity. We prove the asymptotic optimality of the super-MAC algorithm by introducing a novel reference system that enables the analysis of the multi-RAT system where there is a mixture of slotted (such as cellular) and unslotted (such as WiFi) RATs. Through extensive simulations we show that the super-MAC algorithm can enhance the quality of service including throughput, energy efficiency, and fairness. [ABSTRACT FROM AUTHOR]

We consider the problem of providing inter-access-point (AP) fairness guarantee in dense AP deployments where starvation can occur. In particular, we develop a framework for providing robust minimum throughput guarantee for each AP under the uncertainty of user distributions. Our framework consists of an AP throughput provisioning scheme and a distributed CSMA algorithm. The throughput provisioning scheme computes a robust feasible minimum AP throughput vector based on a random AP-level conflict graph and chance-constrained optimization. By incorporating the minimum throughput vector, we develop a distributed CSMA algorithm that fulfills the minimum requirement for each AP and is compatible with the IEEE 802.11 standard. We show through extensive simulations that our framework addresses the AP starvation problem by guaranteeing minimum throughput for each AP. [ABSTRACT FROM PUBLISHER]

Due to the tradeoff between capacity and fairness in a wide variety of networks, increasing one objective may deteriorate the other. This paper investigates the possibility of improving capacity and fairness at the same time, specifically in two-hop cellular networks. First, we prove that an achievable capacity region can be enlarged by using relay, i.e., the capacity and fairness tradeoff relationship is alleviated. To achieve the Pareto-efficient boundary of this enlarged capacity region, an appropriate scheduling algorithm should be developed. Thus, second, we propose a generalized opportunistic scheduling algorithm for relaying mode that can improve both capacity and fairness. Furthermore, we propose a heuristic algorithm with low complexity and signaling overhead for relaying mode. Extensive simulations under various configurations demonstrate that the proposed algorithm not only increases the throughput of each user but also effectively alleviates unfairness among users. [ABSTRACT FROM PUBLISHER]