Massive Multiple Input Multiple Output (MIMO) is an essential component for future wireless cellular networks. One of its biggest advantages is to use the 5G spectrum more intelligently by extending both coverage (via high gain adaptive beamforming) and capacity (via high order spatial multiplexing). In this paper, we evaluate the performance of Time-division duplex (TDD)-based massive MIMO deployment scenario in one of the commercial sites in Turkey. Our experimental results reveal three major contributions: (i) TDD-based massive MIMO in 10 Mhz reveals up to 212% and 50% higher cell throughput compared to Frequency-division duplex (FDD)-based MIMO deployments with 10 Mhz and 20 Mhz respectively. The Downlink (DL) throughput is also observed to be better in mid/far points. (ii) Together with the usage of TDD-based massive MIMO inside the same commercial site, median values of total cell traffic, Uplink (UL) Spectral Efficiency (SE) and DL schedule Transmission Time Interval (TTI) duty cycle have improved 38%, 9% and 14.5% compared to FDD-based MIMO scenario respectively. (iii) Finally, we address some of the challenges of the massive MIMO deployments and the possible tradeoffs that can be observed in terms of Radio Resource Control (RRC)-connected User Equipments (UEs), cell throughput, available Sounding Reference Signal (SRS) resources and pairing opportunities provided by massive MIMO.INDEX TERMS Experiments, massive MIMO, measurements, real-world testbed, TDD, FDD.

Proliferation of data services has made it mandatory for operators to be able identify geographical regions with 3G connectivity discontinuity in a scalable and cost-efficient manner. The currently used methods for such analysis are either costly -such as in drive tests, partly unreliable -such as in network simulation approaches, or are not precise enough-such as in base station key performance indicators (KPI) based approaches. In this paper, towards addressing these inadequacies, we propose a 3G coverage analysis method that makes use of "big data" processing schemes and the vast amounts of network data logged in mobile operators. In the proposed scheme, the BSSAP mobility and radio resource management messages between the BSS and MSC nodes of the operator network are processed to identify inter-technology handovers from 3G (WCDMA) access to 2G (EDGE, GPRS, GSM). Demonstrative examples show that the proposed mechanism produces accurate and precise results, outperforming the base station KPI-based approach.

No abstract

Differentiated quality-of-Service (QoS) techniques are widely used to distinguish between different service classes and prioritize service needs in mobile networks. Mobile Network Operators (MNOs) utilize QoS techniques to develop strategies that are supported by the mobile network infrastructure. However, QoS deployment strategy can ensure that the radio resources provided by the base station are easily consumed if it is not used correctly or when different techniques are used all together. In this paper, we propose a scheduling algorithm and compare two different QoS deployment strategies for prioritized User Equipment (UEs) (with higher scheduling rates and dedicated bandwidth) that MNOs can use in the current infrastructure, using a commercial real-time Long Term Evolution (LTE) network in different test scenarios. Moreover, we expose the real-time user experience in terms of uplink throughput and analyze results of the UE's real-time key performance indicators (KPIs) in detail. Experiment results are evaluated considering the implications of different QoS support types on network coverage and capacity planning optimizations. Our results demonstrate that even though preconfigured resource allocations can be given to prioritize UEs, the experience of all the UEs can be affected unexpectedly in the presence of many UEs who have received different QoS deployment support. Our experimental observations have revealed that location of the UEs with respect to Base Station (BS) and the availability of dedicated bandwidth UEs inside cell may have implications on the apriori defined resource allocation strategies of the other UEs. INDEX TERMS QoS, differentiated services, mobile networks, experiments.

No abstract