Joseph Isabona

Coverage and Link Quality Trends in Suburban Mobile Broadband HSPA Network Environments

Radio coverage shrinkage is one of the key factors that places limitation on link data transmission rate quality in mobile broadband networks, especially on users at cell edge. This occurs due to multipath radio signal fading, frequent changes in radio propagation conditions and base station equipment deterioration. Other key limiting factors includes intercellular/intracellular interference and random background noise in the network environment. Within this context, the importance of carrying out a routine independent assessments of deployed mobile communication networks in a realistic environment has also increased. This is to evaluate their robustness and performance capability in different radio propagation scenarios from one location to the other. This research work deals with practical performance monitoring and evaluation of deployed commercial mobile broadband HSPA networks, considering the radio coverage and service quality aspects in typical suburban environments. The aim is to correlate cell coverage with radio link quality performance indicators in different propagation environments. The aim have been accomplished by carrying out field test measurement campaign and a drill down post data analysis on the HSPA networks and this enable us to critically examined and relate vital QoS parameters at the cell site specific level. Finally, based on Block Error rate performance threshold, the cell radius has been estimated for each measurement routes. The results could form the basis for performance optimisation of mobile cellular networks.

Hybrid neural network approach for predicting signal propagation loss in urban microcells

In the last few years, the need for connectivity far and wide, coupled with the continuous increase in the number of cellular network subscribers globally, has stirred the development and evolution of diverse cellular communication standards. This in turn has led to speedy installations of base station transmitters, thus making the process of planning and fine-tuning the location of these BS transmitters very difficult. To plan and optimize mobile cellular networks for acceptable level of service coverage and quality at the mobile station terminals, radio network engineers rely on propagation loss prediction models. This research work investigates the application of a neural hybridized model for field signal strength attenuation prediction. The hybridized model combines a conventional Log-distance model and an adaptive neural network model. The adaptive neural model employs a multilayer Levenberg Marquardt back propagation algorithm to reimburse for the prediction errors obtained by means of using only the conventional model in urban microcellular environment. After applying a number of first order statistical indicators such standard deviation and root mean square error for a comprehensive performance evaluation, the hybrid — based algorithm provides more accurate prediction results with measured values compared to the conventional approach. The computationally effective prediction technique of the hybrid based neural network model can be used for standardization and enhancement of the conventional field strength propagation loss prediction methods.

Environment-Adaptation Based Hybrid Neural Network Predictor for Signal Propagation Loss Prediction in Cluttered and Open Urban Microcells

In the design and placement of radio base station transmitters, the accurate field signal power prediction and modelling is of critical importance. In this work, an adaptive neural network predictor which combines multilayer perception (MLP) and adaptive linear element (Adaline) is proposed for enhanced signal propagation loss prediction in microcellular urban environments. The prediction accuracy of the proposed Hybrid adaptive neural network predictor has been tested and evaluated using experimental field strength data acquired from LTE radio network environment with mixed residential, commercial and cluttered building structures. By means of first order statistical performance evaluation metrics, namely, regression coefficient (R), root mean squared error, standard deviation and mean absolute error, the proposed adaptive hybrid approach provide a better prediction accuracy compared to the standard MLP ANN prediction approach. The superior performance of the hybrid neural predictor can be attributed to its capability to learn, adaptively respond and predict the fluctuating patterns of the reference propagation loss data during training.

Downlink Massive MIMO Systems: Achievable Sum Rates and Energy Efficiency Perspective for Future 5G Systems

To meet up with the ever increasing subscribers’ demand for higher data rates and mobile data traffic growth in the telecommunication industry, the fifth generation (5G) systems is being considered for the next future cellular communication standards. The two principal design requirements being aimed at in 5G are robust data transmission rates in Gigabits and low power consumption systems. Massive multiple input multiple output (M-MIMO) technology is an evolving smart antenna technology which has some key promising potentials to boost 5G networks in meeting the aforementioned requirements. However, there is an emergent concern that increased number of antenna arrays in M-MIMO system could induce high power consumption and poor energy efficiency when deployed at the base stations (BSs). Also, inter-cellular interference which occurs as a result of pilot contamination, fast fading and uncorrelated noise effects in the radio channels are other open issues in M-MIMO system. This work investigates and compare the achievable sum rates and energy efficiency of a downlink single cell M-MIMO systems utilizing linear and nonlinear precoding schemes. First, we have shown how the increasing signal-to-noise ratio and M-antennas impact the achievable sum rates. Furthermore, the energy saving potentials of M-MIMO systems in macro, micro and pico cellular environments when linear and nonlinear precoding schemes are utilized at the BS have been demonstrated. Particularly, by means of power fairness index, the tradeoff among the energy efficiency, sum rate and the system users have also been presented and discussed. Results show that substantial energy efficiency improvements can be achieved in micro and pico cellular environments of downlink M-MIMO systems when non-linear successive interference cancellation precoding is applied compared to linear precoding schemes.

ENERGY-EFFICIENT COMMUNICATION IN LARGE SCALE ANTENNA SYSTEMS: IMPACT OF VARIABLE USER CAPACITY AND NUMBER OF TRANSMISSION ANTENNAS

Energy-efficient transmission is fast becoming a critical factor in designing future mobile broadband cellular communication systems. This research work examines the constraints with regard to the achievable throughput and energy efficiency that can be attained on the use of precoding-based massive MIMO systems, bearing in mind the effect of some key performance impacting parameters. We first introduced an absolute energy efficiency-based model to evaluate the deep-down relationship among the packet length, the Bit error rate (BER) and throughput. Then, by means of simulation with cyclic coordinated search algorithm, optimal achievable throughput and energy efficiency performance have been shown and demonstrated for variable capacity of users and number of transmission antennas. This work is expected to be of enormous importance to practical system design on the use of massive MIMO antenna technology for data throughput and energy efficiency maximization in future 5G systems.