 which is a trade-off between two competing goals: the desire to get maximum throughput now and the desire to get maximum throughput in the future. This approach is one step toward combining the QoS and opportunistic scheduled approaches. Another parameter to consider when maximizing total throughput is radiated power. As broadcast power is a limited resource, a sensible approach is to investigate the effects of varying allocated broadcast power depending on various factors. Some schemes rely on the minimizing of overall transmit power to reduce interference to other users, allowing greater reuse of available spectrum, for example. This is often termed the "water-filling" method. Such approach is outside the scope of this project but remains an interesting one, nonethe- less. Further information can be found in Ref. 19 (a computationally inexpensive water-filling algorithm) and Ref. 20 (an uplink transmission algorithm minimizing total power, following a first-come first-served basis). Grossglauser and Tse present an interesting and novel approach also taking advantage of multiuser diversity, by which they show that mobility (counter to intuition) increases the capacity of wireless networks.2 21 Their algorithm assumes a mesh network topology, where users relay data to one another instead of relying on a central base station. The authors Chapter 7 Wireless scheduling and Quality of Service optimization techniques 245 demonstrate how user mobility allows taking advantage of what can be termed spatial multi- user diversity. However, the authors do concede that their approach is probably not well suited to real-time applications such as voice communications. It is now clear that significant performance gains can be achieved using an opportunistic approach. However, despite it revealing many interesting possibilities, such an approach alone is not suited for a 4G/5G scheduler, due to its inability to meet QoS guarantees. The Quality of Service scheduler Data scheduling from a QoS-centric approach concentrates on maxi