Selective Fair Scheduling over Fading Channels

Imposing fairness in resource allocation incurs a loss of system throughput, known as the Price of Fairness ($PoF$). In wireless scheduling, $PoF$ increases when serving users with very poor channel quality because the scheduler wastes resources trying to be fair. This paper proposes a novel resource allocation framework to rigorously address this issue. We introduce selective fairness: being fair only to selected users, and improving $PoF$ by momentarily blocking the rest. We study the associated admission control problem of finding the user selection that minimizes $PoF$ subject to selective fairness, and show that this combinatorial problem can be solved efficiently if the feasibility set satisfies a condition; in our model it suffices that the wireless channels are stochastically dominated. Exploiting selective fairness, we design a stochastic framework where we minimize $PoF$ subject to an SLA, which ensures that an ergodic subscriber is served frequently enough. In this context, we propose an online policy that combines the drift-plus-penalty technique with Gradient-Based Scheduling experts, and we prove it achieves the optimal $PoF$. Simulations show that our intelligent blocking outperforms by 40$\%$ in throughput previous approaches which satisfy the SLA by blocking low-SNR users

Scheduling M2M traffic over LTE uplink of a dense small cell network

We present an approach to schedule Long Term Evolution (LTE) uplink (UL) Machine-to-Machine (M2M) traffic in a densely deployed heterogeneous network, over the street lights of a big boulevard for smart city applications. The small cells operate with frequency reuse 1, and inter-cell interference (ICI) is a critical issue to manage. We consider a 3rd Generation Partnership Project (3GPP) compliant scenario, where single-carrier frequency-division multiple access (SC-FDMA) is selected as the multiple access scheme, which requires that all resource blocks (RBs) allocated to a single user have to be contiguous in the frequency within each time slot. This adjacency constraint limits the flexibility of the frequency-domain packet scheduling (FDPS) and inter-cell interference coordination (ICIC), when trying to maximize the scheduling objectives, and this makes the problem NP-hard. We aim to solve a multi-objective optimization problem, to maximize the overall throughput, maximize the radio resource usage and minimize the ICI. This can be modelled through a mixed-integer linear programming (MILP) and solved through a heuristic implementable in the standards. We propose two models. The first one allocates resources based on the three optimization criteria, while the second model is more compact and is demonstrated through numerical evaluation in CPLEX, to be equivalent in the complexity, while it performs better and executes faster. We present simulation results in a 3GPP compliant network simulator, implementing the overall protocol stack, which support the effectiveness of our algorithm, for different M2M applications, with respect to the state-of-the-art approaches

Joint User‑Centric Clustering and Multi‑cell Radio Resource Management in Coordinated Multipoint Joint Transmission

Coordinated multipoint joint transmission (JT-CoMP) is a promising solution to address inter-cell interference in dense future wireless networks due its strength in converting interfering signals into useful signals, thereby enhancing capacity especially at the cell edge. However, allowing all user equipments (UEs) to operate using the JT-CoMP mode reduces the availability of radio resources. This paper develops an efficient algorithm that can identify which UEs will benefit from operating in a JT-CoMP mode and how to efficiently allocate radio resources from multiple base stations. Joint user-centric JT-CoMP clustering and multi-cell resource management is used in two steps where user-centric clusters are constructed as a first step and according to the clustering results obtained, resources are assigned. This paper also provides a new user-centric clustering approach that allows a user to utilize the JT-CoMP technique only if JT-CoMP boosts its rate above a certain threshold level. A multi-cell resource allocation scheme that can address the resource mismatching problem between cooperative BSs that happens due to load imbalance is proposed. Simulation results show that the proposed user-centric clustering algorithm outperforms the traditional power level difference scheme in terms of the system’s overall throughput as well as the throughput of cell-edge users. Also, results show that the performance of JT-CoMP is mainly affected by the user-centric approach and the amount of physical radio resources assigned to CoMP UEs