Cooperative Fair Bandwidth Scaling in Contentionbased Wireless Networks using Time Token Bucket Applications with Quality of Service (QoS) requirements such as networked control systems are not well supported by todayxe2x80x99s offtheshelf wireless standards such as IEEE 802.11 (WiFi). Specifying a range of minimally required base QoS and preferred QoS combined with dynamic scaling in this range can help to support QoS while improving network utilization. However, scaling is typically happening in a greedy and therefore unfair way, with each node trying to maximize its throughput. In this paper, we present a cooperative algorithm for dynamical cooperative bandwidth scaling using a time token bucket approach. We show that our approach improves bandwidth utilization in a fair way. Furthermore, it dynamically reacts to topology changes, e.g. due to node mobility, as well as to traffic of uncooperative (external) nodes. Finally, it achieves stable bandwidth usage of participating nodes, even in networks with external traffic. We evaluate our approach in an IEEE 802.11 testbed.

Proportional Fair RAT Aggregation in HetNets Heterogeneity in wireless network architectures (i.e., the coexistence of 3G, LTE, 5G, WiFi, etc.) has become a key component of current and future generation cellular networks. Simultaneous aggregation of each clientu0027s traffic across multiple such radio access technologies (RATs)  base stations (BSs) can significantly increase the system throughput, and has become an important feature of cellular standards on multiRAT integration. Distributed algorithms that can realize the full potential of this aggregation are thus of great importance to operators. In this paper, we study the problem of resource allocation for multiRAT traffic aggregation in HetNets (heterogeneous networks). Our goal is to ensure that the resources at each BS are allocated so that the aggregate throughput achieved by each client across its RATs satisfies a proportional fairness (PF) criterion. In particular, we provide a simple distributed algorithm for resource allocation at each BS that extends the PF allocation algorithm for a single BS. Despite its simplicity and lack of coordination across the BSs, we show that our algorithm converges to the desired PF solution and provide (tight) bounds on its convergence speed. We also study the characteristics of the optimal solution and use its properties to prove the optimality of our algorithmu0027s outcomes.