No abstract

In order to keep and/orexpand its share of the wireless communication market and decrease churn, it is important for network operators to keep their users (clients) satisfied. The problem to be solved is how to increase the number of satisfied non-real time (NRT) and real time (RT) users in the downlink of the radio access network of an orthogonal frequency division multiple access system. In this context, the present work proposes a method to solve the referred problem using a unified radio resource allocation (RRA) framework based on utility theory. This unified RRA framework is particularized into two RRA policies that use sigmoidal utility functions based on throughput or delay and are suitable for NRT and RT services, respectively. It is demonstrated by means of system-level simulations that a step-shaped sigmoidal utility function combined with a channel-aware opportunistic scheduling criterion is effective toward the objective of user satisfaction maximization.

This paper addresses multi-user multi-cluster massive multiple-input-multiple-output (MIMO) systems with non-orthogonal multiple access (NOMA). Assuming the downlink mode, and taking into consideration the impact of imperfect successive interference cancellation (SIC), an in-depth analytical analysis is carried out, in which closed-form expressions for the outage probability and ergodic rates are derived. Subsequently, the power allocation coefficients of users within each sub-group are optimized to maximize fairness. The considered power optimization is simplified to a convex problem, which makes it possible to obtain the optimal solution via Karush-Kuhn-Tucker (KKT) conditions. Based on the achieved solution, we propose an iterative algorithm to provide fairness also among different sub-groups. Simulation results alongside with insightful discussions are provided to investigate the impact of imperfect SIC and demonstrate the fairness superiority of the proposed dynamic power allocation policies. For example, our results show that if the residual error propagation levels are high,

Abstract-In this work, we study the problem of allocating resources in a multi-service cellular network aiming at maximizing the total system rate while providing suitable Quality of Experience (QoE) to the network users. In our formulation, we try to satisfy at least a certain number of users per service plan, which is an important constraint from the mobile network operators' perspective. We manage to reformulate this nonlinear optimization problem as an Integer Linear Problem (ILP), that can be solved by standard methods. However, due to the exponentially high complexity to solve large instances of this problem, we propose and evaluate a suboptimal algorithm with a much lower complexity, called Rate Maximization under Experience Constraints (RMEC), whose main idea is to divide the problem into three smaller subproblems with reduced complexity. By means of computational simulations, we show that our proposed algorithm presents a near optimal performance and outperforms the state-of-art solution of the literature.

We study the impact of scheduling algorithms on the provision of multiple services in the long term evolution (LTE) system. In order to measure how well the services are provided by the system, we use the definition of joint system capacity. In this context, we claim that scheduling strategies should consider the current satisfaction level of each service and the offered load to the system by each service. We propose a downlink-scheduling strategy according to these ideas named capacity-driven resource allocation (CRA). The CRA scheduler dynamically controls the resource sharing among flows of different services such as delay-sensitive and rate demanding ones. Moreover, CRA scheduler exploits the channel-quality knowledge to utilize the system resources efficiently. Simulation results in a multicell scenario show that the CRA scheduler is robust regarding channel quality knowledge and that it provides significant gains in joint system capacity in single and mixed service scenarios.