We analyze the achievable sum rate of the Gaussian MIMO broadcast channel. We first consider Multiple-Input Single-Output (MISO) channels and derive the large system limit of the sum capacity as the number of users and transmit antennas go to infinity with a fixed ratio. We then consider Multiple-Input Multiple-Output (MIMO) broadcast channels and fix the number of users and let the number of transmit and receive antennas tend to infinity with fixed ratio. As in this case an asymptotic expression for sum capacity is hard to obtain, we evaluate the large system sum rate corresponding to successive zero-forcing beamforming with Dirty-Paper Coding. The analysis gives a lower bound on the large system sum capacity, which is numerically observed to be quite close. In addition, large system analysis is applied to estimate the relatively small performance losses with respect to sum capacity of successive zero-forcing beamforming with and without Dirty-Paper Coding in finite MISO systems.

Solving Quality of Service constrained optimization problems in the Multiple-Input Multiple-Output (MIMO) broadcast channel usually requires the iterative solution of a weighted sum rate maximization, which exhibits a considerable amount of numerical complexity itself, especially in Orthogonal Frequency Multiplexing (OFDM) systems. In this paper a low complexity framework for these problems is presented, where the allocation of data streams to users and carriers is done successively. An efficient rule for choosing the user and carrier to be allocated in each step is presented for both linear and non-linear precoding leading to small performance losses compared to the optimum.

Analytical results for the average sum rate achievable in the Multiple-Input Multiple-Output (MIMO) broadcast channel with algorithms relying on full channel state information at the transmitter are hard to obtain in practice. In the large system limit, when the number of transmit and receive antennas goes to infinity at a finite fixed ratio, however, the eigenvalues of many random matrices become deterministic and analytical expressions for the sum rate can be derived in some cases. In this paper we will present large system expressions for the sum rate for three sub-optimum algorithms, namely the Successive Encoding Successive Allocation Method (SESAM), Block Diagonalization and Block Diagonalization with Dirty Paper Coding. In case the large system limit of the sum rate does not exist, we derive lower bounds. By simulation results it is shown that the asymptotic results serve as a good approximation of the system performance with finite system parameters of reasonable size.

Achieving the capacity region in the MIMO broadcast channel requires the use of dirty paper coding (DPC). When it cannot be afforded to satisfy the requirements of all users by DPC based approaches, it is necessary to identify the users which exhibit the highest performance gain compared to simpler approaches. In this paper we present a user grouping method that aims to identify those users with highest weighted sum rate gain. First some users are excluded by a simple criterion leading to a reduced user set, from which the final user group is selected by a more sophisticated criterion.

Finding the matrix with the maximum singular value amongst a set of matrices is a common problem occurring in transmit signal processing algorithms for multiuser multiple-input multiple-output (MIMO) systems. However, computing the principal singular value of a matrix is a rather numerically complex task. Furthermore, in many practical scenarios, the number of users is large and for each user this task has to be conducted. In this paper we therefore propose a novel user preselection method which reduces the computational complexity at no performance loss. This is achieved by deselecting some users based on a simple criterion and thus avoiding explicit computations of the singular values of those users. This criterion is based on easily computable bounds for the principal singular values. Finally, a statistical analysis is provided and the application to the Successive Encoding Successive Allocation Method (SESAM) is shown.

We consider an innovative downlink multiuser MIMO scheme. Several users compete for the available resources in time, frequency and space. The proposed scheme exploits multiuser diversity and uses interference cancellation at the transmitter. We evaluate it for indoor, hot spot and multihop scenarios. Significant gains in terms of sum capacity can be achieved even under line of sight conditions. The theoretical limit can be approximately achieved in most scenarios. A nice feature of the proposed scheme is that it inherently provides some fairness regarding rate distribution among users even though fairness is not explicitly taken into account by the scheduler.

The exploitation of multiuser diversity and the application of multiple antennas at transmitter and receiver are considered to be key technologies for future highly bandwidth-efficient wireless systems. We combine both ideas in a downlink multicarrier transmission scheme where multiple users compete for the available resources in time, frequency and space. The instantaneous channel impulse responses for all users are assumed to be perfectly known at the transmitter. Our proposed algorithm allocates each spatial dimension on a subcarrier to the user which has the highest channel tap gain on the respective spatial dimension. The scheduling strategy is optimized for sum capacity maximization. In this paper, we restrict ourselves to a more illustrative description of the idea rather then providing mathematical details. We demonstrate the potential of the proposed scheme by capacity results for measured real world channels in a large office environment. Finally, video streaming is used as a potential application with high data rate and low latency demands. It is shown that the proposed method has the potential to exploit multiuser diversity while still providing stable video streams even though QoS constraints are not explicitly taken into account by the scheduler.

We consider a multiuser MIMO-OFDMA scheme which exploits multiuser diversity in all dimensions: time, frequency and space. The main contribution of this paper is the evaluation and explanation of multiuser MIMO in a real world scenario, i.e. a large office room, based on measured channels. We report interesting results of a measurement campaign which suggest that significant MIMO gains are possible in an indoor environment even under strong line-of-sight condition as long as the distance of the users from the base station is larger than a reverberation distance which only depends on room surface and material. We show results on the achievable multiuser MIMO data rates for the given scenario compare to theoretical limits and discuss the results in the light of the insights gained from the measurement campaign. We also introduce restrictions on the rate distribution between users, i.e. QoS constraints. It is shown that the theoretical limits can be approximately achieved provided that the users which compete for the spatial resources are carefully chosen.

In bottom-up cross-layer optimization, each layer offers a set of feasible operation points to the layer above, enabling the application to decide which parameter setup is optimum. In this paper, the capabilities of the physical layer in a multi-user MISO system are described in terms of the users' outage probabilities. Optimum descriptions are given by sets of efficient outage probability tuples. We first derive a closed-form expression for a user's outage probability in the presence of multi-user interference. Next, the set of efficient outage probability tuples under a simplified SDMA transmit strategy is derived. We show how a good approximate description of the efficient set is found by equidistant sampling. Finally, the performance of the proposed transmit strategy is compared with an orthogonal multiple access scheme, demonstrating that optimum mode selection at the physical layer can lead to significant performance gains at the application layer