the resources or by interference filtering. © 2009 by Taylor & Francis Group, LLC Long Term Evolution: 3GPP LTE Radio and Cellular Technology Figure 7.6 Combining signals are transmitted over multiple nodes simultane- ously. This provides additional diversity and SNR improvement. complexity can be a viable technique (e.g., cooperation between sectors; see, for example, Zirwas et al. [18]). The first advantage of cooperation is the improvement of the SNR, especially in areas between the transmitters (the "cell edge"). This can be viewed as a form of coherent macrodiversity. Another advantage is the possibil- ity of effectively reducing interference. In this context, this strategy is sometimes referred to as "network MIMO" because data streams can be spatially multiplexed analogously to a classical point-to-point MIMO system. 7.2.2.1 Axiomatic Approach to Interference Modeling Future wireless networks are expected to provide high-rate services for densely populated user environments. The application content is moving from audio to high-speed data transfer. The number of communication links is increasing. Therefore, interference is becoming an increasingly limiting factor. Interference puts a limit on how many users per cell can be served at a certain data rate. It there- fore makes sense to ask for theoretical performance bounds, the region of achiev- able performances, and optimal transmission strategies. These results can serve as guidelines for the development of algorithms and implementations. A general framework for interference modeling was introduced by Yates in Yeung et al. [55], with extensions in references 46, 49, and 51. It was proposed to model interference as a function of the transmit powers. Consider K users, K = {1,2,..., K}, whose transmit powers P1, PK are collected in a vector p=[p, (7.1) The resulting interference at user k is given by Ik(p). The set of all possible power vectors is denoted by P. The behavior of the system depends on how the interference functions I(p) Ik(p) depend on the avail