an-out network, we can look for good solutions locally at each base station to get a pretty good global solution. We can start our backhaul heuristic with a feasible solution of having each base sta- tion send all its packets directly to the MSC with no daisy chains. Some service providers may stop right here and use the star graph solution described in Sec. 26.1 as their backhaul network. Now let us consider each base station and see if there is a cheaper way to get the packets home to the MSC. We only need to consider all the transport links on the list from that base station. For each link on the list, we add the cost of using that link and the incremental cost of carrying those packets on the downstream links, and we sub- tract the cost of the transport we were using before. Let us put this in more mathematical notation. For every base station node b, we are going to consider changing its next node toward the MSC in the fan-out network. Sup- pose that the current next node for b is node C and we are considering changing that to base station node d. Then the cost added by the move from C to d is the cost of all the b packets (including any packets upstream of b) being sent to d plus the extra cost of adding all the b packets to whatever links d is using to get to the MSC. The cost saved by the move from C to d is the cost of all the b packets being sent to C and the cost saved by removing all the b packets from whatever links C is using to get to the MSC. We do this for all the transport links on our list from b to anywhere and then move on to the next base station to look for improvement. This iteration continues for all the base stations and all the links on our list until no improvement is found. Is this interactive, heuristic solution as good as the full-system- view optimal solution of the network flow optimization in Sec. 26.1.3? Probably not, but it is close to optimal, and it is a lot less work. Furthermore, an incremental solution technique, such as this iterative heuristic, is well suited to the cont