Network slicing provides a key functionality in emerging 5G networks, and offers flexibility in creating customized virtual networks and supporting different services on a common physical infrastructure. This capability critically relies on a MAC scheduler to deliver performance targets in terms of aggregate rates or resource shares for the various slices. A crucial challenge is to enforce such guarantees and performance isolation while allowing flexible sharing to avoid resource fragmentation and fully harness channel variations. In the present paper we propose a MAC scheduler which meets these objectives and preserves the basic structure of utility-based schedulers such as the Proportional Fair algorithm in terms of per-user scheduling metrics. Specifically, the proposed scheme involves counters tracking the aggregate rate or resource allocations for the various slices against pre-specified targets, and computes offsets to the scheduling metrics accordingly. This design provides transparency with respect to other scheduling modules, such as link adaptation and beam-forming. We analytically establish that the proposed scheme achieves optimal overall throughput utility subject to the various slicing constraints. In addition, extensive 3GPP-compliant simulation experiments are conducted to assess the impact on best-effort applications and demonstrate substantial gains in overall throughput utility over baseline approaches.

The deployment of pico cells to cover traffic hot spots within the footprint of a macro cell provides a powerful approach to meet the massive growth in traffic demands fueled by smartphones and bandwidth-hungry applications. Joint optimization of resource allocation and user association is critical to achieve the maximum capacity benefits and performance gains in such heterogeneous network deployments (HetNets). In order to gain insight in the achievable capacity gains, we examine in the present paper the stability and performance of a HetNet system in the presence of flow-level dynamics. The stability condition reveals that in stationary traffic conditions the maximum capacity can be achieved with a static resource split and traffic association rule, provided that these are suitably selected. This suggests that dynamic adaptation on time scales commensurate with the variations in traffic parameters suffices to extract most of the achievable capacity gains. For the case of static cell boundaries and Proportional Fair scheduling, we also present a method for evaluating the flow-level performance in terms of the distribution of the number of active file transfers and expected transfer delay.

Maximizing system throughput and bandwidth utilization while guaranteeing fairness among users is a core technical challenge in the design of next generation cellular networks. In this context, fractional frequency reuse (FFR) is a helpful tool if it is flexible in terms of resource and power allocation as well as scheduling. Complementary to this tool, a well-chosen association of users to base station sectors can yet be another important factor, particularly when users are distributed unevenly in space. Here we propose a load balancing scheme that combines flexible FFR with an unrestricted association of users to sectors or base stations, thereby effectively increasing the user data rates in the dense regions of the network. In a first step, we perform virtual joint power allocation and scheduling so as to determine the best sectors for maximizing the throughput for a given user. In a second step, users are actually associated to the best sectors, which are not necessarily the ones providing the strongest signal. Simulation results show that our proposed mechanism provides fairness by load balancing while still maintaining the throughput over all users.

A simulation study on the downlink signaling performance of intra-LTE intra-frequency mobility load balancing in the macro-cellular context will be presented in this paper. The results identify the HO command message as the most critical signaling message. In addition the range of the cell individual offset for different UE speeds and signaling error thresholds will be identified.

An optimal vertical antenna tilt in a wireless access network plays a key role for coverage and capacity in the system. The autonomous (re-)optimization of tilts bears a large saving potential for operators as manual intervention is particularly costly. In this paper, we present a heuristic variant of the gradient ascent method to continuously optimize antenna tilts with quick convergence. We show that the average spectral efficiency in the system increases by 10% and that the spectral efficiency at the cell edge (5% quantile) increases by 100% after optimization. Changing conditions during the operational phase of the network, as for instance cell outage, can successfully be detected and compensated by an autonomous re-optimization.

The present study assesses via LTE system level simulations the upper bound of the maximal reachable performance gain of LTE intra carrier load balancing. The investigation focuses on the three load balancing mechanisms: handover parameter adaptation, antenna tilt adaptation and inter cell interference coordination. The simulation results show that these techniques are complementary. Handover parameter variations and antenna tilt adaptation excels for overloaded cells in low and medium load environments, while inter cell interference coordination significantly improves the performance especially in high load environments.

Wireless mesh networks (WMNs) with inband backhauling use the same antennas for the backhaul as well as for the access. Therefore antennas of next hop neighbours need to be directed to each other. However, such a configuration is not possible in a three-sectorized hexagonal cell deployment. In this paper we derive several alternative topologies that are suitable for WMNs with inband backhauling. We show that a topology with four directional antennas per node and backhaul connectivity between indirect neighbours outperforms competing topologies in terms of handover rate, optimal maximum power, and system capacity.

This paper introduces a solution to enable self-optimization of coverage and capacity in LTE networks through base stations' downtilt angle adjustment. The proposed method is based on fuzzy reinforcement learning techniques and operates in a fully distributed and autonomous fashion without any need for a priori information or human interventions. The solution is shown to be capable of handling extremely noisy feedback information from mobile users as well as being responsive to the changes in the environment including self-healing properties. The simulation results confirm the convergence of the solution to the global optimal settings and that the proposed scheme provides up to 20% performance improvement when compared with an existing fuzzy logic based reinforcement learning approach.

Careful modeling of the radio channel characteristics is an important issue for system level simulations. While a number of detailed path loss, shadowing and fast fading models can be found in literature, many of them entail large computational efforts and hence lead to long simulation times, or require huge amounts of memory. In this paper, we focus on shadowing models with two-dimensional correlation properties. We present handover performance evaluations as a use case from which requirements to the modeling of the shadow fading are derived. For different implementations of the shadow fading in system level simulations, we discuss their pros and cons and then focus on the sum of sinusoid model proposed by Cai and Giannakis. Our main contribution lies in a new frequency sampling method to determine the coefficients of the sinusoidal waveforms. We show that this new method, denoted as Power Sampling Method (PSM), allows for a significant reduction of the number of sinusoids while the correlation properties of the shadow fading channel are preserved.