1. Multiple Criteria Optimisation for Base Station Antenna Arrays in Mobile Communication Systems By Ioannis Chasiotis PhD Student Institute for Communications and Signal Processing Department of Electronic and Electrical Engineering Supervisor: Prof. T. S. Durrani

2. Presentation Outline Aims and Objectives Why Antenna Arrays? Multiple Criteria Optimisation Algorithm Example Results Conclusions & Future Work

3. Aims & Objectives Develop an optimisation algorithm to provide system designers with optimal design solutions for a modern base station antenna array  Antenna Array Size  Capital Cost Different Mobile System Architectures  GSM  CDMA Develop a graphical user interface (GUI) for use as a separate decision making software package

4. Why Antenna Arrays? Antenna arrays introduce significant improvement in system performance  Transmit and Receive Gain  Interference  Capacity/Spectral Efficiency  Area of Coverage Improvement in performance criteria is greatly influenced by array size However…This improvement is accompanied by escalating costs Antenna Array

5. Trade-Off Capital Investment Performance

6. To obtain the optimal array size given the trade – off between the performance criteria and the increase in cost All objective functions are combined into one scalar function to be maximised Two simple approaches to achieve this  Additive Aggregation  Multiplicative Aggregation Multiple Criteria Optimisation Individual objective functions Weighting coefficients Number of objective functions

7. Multiple Criteria Optimisation II Uplink  Criteria under consideration Spectral Efficiency (η s ) Overall Antenna Gain (G) Area of Coverage (A) Capital Costs (C uplink ) Optimisation Function  Additive Aggregation  Multiplicative Aggregation Downlink  Criteria under consideration Spectral Efficiency (η s ) Overall Antenna Gain (G) Area of Coverage (A) Transmission Efficiency (η Tr ) Capital Costs (C downlink ) Optimisation Function  Additive Aggregation  Multiplicative Aggregation

8. Weighting Factors Computation Weights  Scale influence of criteria in the overall optimisation function J(f(x))  Reflect the relative importance of the considered criteria “Swing” weight method Computing efficient – few computations per cycle  Rank weights-criteria based on their contribution to J(f(x))  Measured in terms of the swing from the worst to the best value of each criterion  Assign weight values according to their rank  Normalise weights Example: Weight computation for the uplink mode of operation. Weights are computed for an increasing array size and adapt to the different effect that each criterion will have at each value of M (number of sensors – array size)

9. Optmisation Algorithm (Additive Aggregation)

10. Example: Simulation Parameters SIMULATION PARAMETERS Bandwidth (B) 5MHz Mobile Terminal Antenna Height (H m ) 2m Reference Noise Temperature (Tp) 290 o K Carrier Frequency (F c ) 2GHz Receiver Noise Figure (F) 3 Path Loss Exponent (gama) 4 Base Station Antenna Height (H b ) 30m Environment Set-Up urban

11. Results I (Uplink) Additive AggregationMultiplicative Aggregation Maximum at 11 sensors in both cases

12. Results II (Downlink) Additive AggregationMultiplicative Aggregation Maximum at 13 sensors in both cases

13. Complete Communications Link (Uplink & Downlink) Additive AggregationMultiplicative Aggregation Maximum at 12 sensors in both cases

14. Conclusions & Future Work Increasing antenna array size of base station does not yield the best results There is an optimum number of sensors that balances the cost-performance trade-off in the best possible way Results show that the aggregation method used to formulate the optimisation functions does not affect the findings of the algorithm The algorithm is currently under further development to be able to provide a potential system designer with optimum solution for cases of MIMO (Multiple Input – Multiple Output) systems, where arrays are used at both end of the communications link.