Fair power and transmission rate control in wireless networks In third generation mobile networks, transmission rates can be assigned to both real time and non real time applications. We address in this paper the question of how to allocate transmission rates in a manner that is both optimal and fair. As optimality criterion we use the Pareto optimality notion, and as fairness criterion we use a general concept of which the maxmin fairness (which is the standardized fairness concept in ATM networks) and the proportional fairness (which characterizes fairness obtained by some transport protocols for the Internet) are special cases.We show that the problem is a joint optimization system of the transmission rate and the power. We formulate the fair allocation problem as an optimization problem and propose both exact as well as approximating solutions. We consider both uplink and downlink problems and study also macrodiversity.

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.