A Gaussian MIMO broadcast channel (GMBC) models the MIMO transmission of Gaussian signals from a transmitter to one or more receivers. Its capacity region and different precoding schemes for it have been well investigated, especially for the case wherein there are only transmit power constraints. In this paper, a special case of GMBC is investigated, wherein receive power constraints are also included. By imposing receive power constraints, the model, called protected GMBC (PGMBC), can be applied to certain scenarios in spatial spectrum sharing, secretive communications, mesh networks and base station cooperation. The sum capacity, capacity region, and application examples for the PGMBC are discussed in this paper. Sub-optimum precoding algorithms are also proposed for the PGMBC, where standard user precoding techniques are performed over a BC with a modified channel, which we refer to as the "protection-implied BC." In the protection-implied BC, the receiver protection constraints have been implied in the channel, which means that by satisfying the transmit power constraints on the protection implied channel, receiver protection constraints are guaranteed to be met. Any standard single-user or multi-user MIMO precoding scheme may then be performed on the protection-implied channel. When SINR-matching duality-based precoding is applied on the protection-implied channel, sum-capacity under full protection constraints (zero receive power), and near-sum-capacity under partial protection constraints (limited non-zero receive power) are achieved, and were verified by simulations.

Traditional cellular networks suffer the so-called “cell-edge problem” in which the user throughput is deteriorated because of pathloss and inter-cell (co-channel) interference. Recently, Base Station Cooperation (BSC) was proposed as a solution to the cell-edge problem by alleviating the interference and improving diversity and multiplexing gains at the cell-edge. However, it has minimal impact on cell-inner users and increases the complexity of the network. Moreover, static clustering, which fixes the cooperating cells, suffers from inter-cluster interference at the cluster-edge. In this paper, dynamic fractional cooperation is proposed to realize dynamic clustering in a shared RRU network. In the proposed algorithm, base station cooperation is performed dynamically at cell edges for throughput improvement of users located in these areas. To realize such base station cooperation in large scale cellular networks, coordinated scheduling and distributed dynamic cooperation are introduced. The introduction of coordinated scheduling in BSC multi-user MIMO not only maximizes the performance of BSC for cell-edge users but also reduces computational complexity by performing simple single-cell MIMO for cell-inner users. Furthermore, the proposed dynamic clustering employing shared RRU network realizes efficient transmission at all cell edges by forming cooperative cells dynamically with minimal network complexity. Owing to the combinations of the proposed algorithms, dynamic fractional cooperation achieves high network performance at all areas in the cellular network. Simulation results show that the cell-average and the 5% cell-edge user throughput can be significantly increased in practical cellular network scenarios.