In this paper we investigate the effect of Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) propagation on the Area Spectral Efficiency (ASE) and on the energy efficiency of dense small-cell networks. We show that including both LOS and NLOS propagation in the path-loss model provides a completely different picture of the behaviours of ASE and energy efficiency than what would be observed in case of either LOS or NLOS propagation only. In particular, with combined LOS/NLOS pathloss, the ASE exhibits superlinear and sublinear behaviour at low and high cell densities, respectively. In addition, the energy efficiency as a function of the cell density has a global maximum and is not a monotonically increasing function like in case of LOS or NLOS propagation only. Based on our findings, we claim that Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) propagations play an important role in studying the performance of extremely dense small-cell networks. Index Terms-Dense networks, small-cells, area spectral efficiency, energy efficiency, transmit power, LOS, NLOS.

The focus of this paper is to provide a unique perspective on new network architectures supporting novel spectrum sharing mechanisms. A Cloud-based MD-MIMO RAN (Massive Distributed Multiple-Input Multiple-Output Radio Access Network) is proposed for exploitation of Licensed Shared Access (LSA) spectrum sharing concept. In order to deliver a particular service, the virtual network operator leases spectrum and antennas from the LSA spectrum pool and the Cloud-RAN platform following the pay-per-use model. The spectrum is turned into a consumable resource along with the infrastructure resources and there is a tradeoff in the system between using more antennas or more bandwidth. In this paper we propose a physical platform that can consume these resources dynamically.

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Software-defined radio (SDR) describes radio transceivers implemented in software that executes on general-purpose hardware. SDR combined with cloud computing technology will reshape the wireless access infrastructure, enabling computing resource sharing and centralized digital-signal processing (DSP). SDR clouds have different constraints than general-purpose grids or clouds: real-time response to user session requests and real-time execution of the corresponding DSP chains. This article addresses the SDR cloud computing resource management problem. We show that the maximum traffic load that a single resource allocator (RA) can handle is limited. It is a function of the RA complexity and the call setup delay and user blocking probability constraints. We derive the RA capacity analytically and provide numerical examples. The analysis demonstrates the fundamental tradeoffs between short call setup delays (few processors) and low blocking probability (many processors). The simulation results demonstrate the feasibility of a distributed resource management and the necessity of adapting the processor assignment to RAs according to the given traffic load distribution. These results provide new insights and guidelines for designing data centers and distributed resource management methods for SDR clouds.

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Testbeds are essential for experimental evaluation as well as for product development. In the context of LTE networks, existing testbed platforms are limited either in functionality and/or extensibility or are too complex to modify and customise. In this work we present srsLTE, an open-source platform for LTE experimentation designed for maximum modularity and code reuse and fully compliant with LTE Release 8. We show the potential of the srsLTE library by extending the baseline code to allow LTE transmissions in the unlicensed bands and coexistence with WiFi. We also expand previous results on this emerging research area by showing how different vendor-specific mechanisms in WiFi cards might affect coexistence.

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