Adaptive dynamic channel borrowing in road-covering mobile networks

This paper introduces adaptive dynamic channel borrowing strategies for wireless networks covering a road. In a GSM-based model, road traffic prediction models are used to characterise the movement of hot spots, such as occurring in rush hour due to traffic jams moving along a highway, and subsequently to predict the teletraffic load offered to the wireless network. From these results a dynamic upper bound on the capacity required to achieve a specified Quality of Service level in the cells is computed. Restricting borrowing to neighbouring cells to avoid excessive reallocation of capacity, optimal channel borrowing strategies based on traffic movement and traffic density are given. These strategies can be characterised by a straightforward rule of thumb: borrow capacity from the cell on the steeper side of the traffic peak, that makes our strategy easily implementable. Results of a dynamic simulation under realistic load indicate a significant reduction of call blocking probabilities under our optimal channel borrowing strategy.