Through massive deployment of additional small cell infrastructure, Dense Small cell Networks (DSNs) are expected to help meet the foreseen increase in traffic demand on cellular networks. Performance assessment of architectural and protocol solutions tailored to DSNs will require system and network level simulators that can appropriately model the complex interference environment found in those networks. This paper identifies the main features of DSN simulators, and guides the reader in the selection of an appropriate simulator for their desired investigations. We extend our discussion with a comparison of representative DSN simulators.

In this work we investigate the reassignment of User Equipments (UEs) between adjacent small cells to concurrently enable spatial multiplexing gains through Multi-User MIMO (MU-MIMO) and reductions in energy consumption though switching emptied small cells to a sleep state. We consider a case where UEs can be reassigned between adjacent small cells provided that the targeted neighbouring cell contains a UE with which the reassigned UE can perform MU-MIMO without experiencing excessive multi-user interference, and whilst achieving a minimum expected gain in spectral efficiency over the previous original cell transmissions as a result. We formulate the selection decision of which UEs to reassign as a set covering problem with the objective of maximising the number of small cell base stations to switch to a sleep state. Our results show that, for both indoor and outdoor LTE small cell scenarios, the proposed MU-MIMO-based reassignments achieve significant reductions in the required number of active small cell base stations, whilst simultaneously achieving increases in spectral efficiency.

This paper presents switched Multi-Element Antennas (MEAs) as a simple, yet effective, method of enhancing the performance of small cell heterogeneous networks and compensating for the small cell base station sub-optimal placement. The switched MEA system is a low-cost system which enables the small cell to dynamically direct its transmission power toward locations of high user density, in other words demand hotspots. Our simulation results show that small cell base stations equipped with switched MEA systems offer greater performance than base stations equipped with omni-directional antennas in terms of both the number of users that can be served (and hence offloaded from the macrocell network) and in terms of overall network capacity. We also compare the performance of the switched MEA with fixed directional antennas and show that fixed-directional antennas can only outperform the switched MEA if the misalignment between their direction of transmission and the direction to the demand hotspot is less than 22.5°.

The main contribution of this work is the proposal and assessment of the MU-MIMO across Small Cells concept. MU-MIMO is the spatial multiplexing of multiple users on a single time-frequency resource. In small cell networks, where the number of users per cell is low, finding suitable sets of users to be co-scheduled for MU-MIMO is not always possible. In these cases we propose MU-MIMO-based cell reassignments of users into adjacent cells to enable MU-MIMO operation. From system level simulations we found that, when the initial number of users per small cell is four, cell reassignment results in a 21.7% increase in the spectral efficiency gain attributed to MU-MIMO, and a higher percentage increase when the initial number of users per cell is lower. Going forward, we will extend this work to also consider energy savings through switching off small cells which are emptied by the reassignment process.

We explore the question of when learning improves the performance of opportunistic dynamic channel selection by characterizing the primary user (PU) activity using the concept of Lempel-Ziv complexity. We evaluate the effectiveness of a reinforcement learning algorithm by testing it with real spectrum occupancy data collected in the GSM, ISM, and DECT bands. Our results show that learning performance is highly correlated with the level of PU activity and the amount of structure in the use of spectrum. For low levels of PU activity and/or high complexity in its utilization of channels, reinforcement learning performs no better than simple random channel selection. We suggest that Lempel-Ziv complexity might be one of the features considered by a cognitive radio when deciding which channels to opportunistically explore.