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We propose a reactive route discovery protocol for energy efficient transmission in a wireless multihop network with multiple input multiple output (MIMO) channels. We focus on networks consisting of MIMO nodes that use time division scheduling where the source needs to transmit a certain amount of data to the destination in a fixed amount of time. We show how the intermediate nodes find the route distributively. This protocol takes into account the quality of the MIMO channels and allocates power to them so that the end-to-end transmission energy is minimized. The resulting protocol is easy to implement and provides the most energy efficient route and the power allocation for the data transmission. We perform simulations on Rayleigh-fading channels to show the significant energy benefits of the proposed route discovery protocol against a dynamic source routing (DSR)-based routing protocol with optimal power allocation. [ABSTRACT FROM PUBLISHER]

The problem of weighted sum-rate maximization (WSRMax) in multicell downlink multiple-input single-output (MISO) systems is considered. The problem is known to be NP-hard. We propose a method, based on branch and bound technique, which solves globally the nonconvex WSRMax problem with an optimality certificate. Specifically, the algorithm computes a sequence of asymptotically tight upper and lower bounds and it terminates when the difference between them falls below a pre-specified tolerance. Novel bounding techniques via conic optimization are introduced and their efficiency is demonstrated by numerical simulations. The proposed method can be used to provide performance benchmarks by back-substituting it into many existing network design problems which relies on WSRMax problem. The method proposed here can be easily extended to maximize any system performance metric that can be expressed as a Lipschitz continuous and increasing function of signal-to-interference-plus-noise ratio. [ABSTRACT FROM PUBLISHER]

Transmission power allocation in single-carrier multiple-input multiple-output (MIMO) systems with iterative frequency-domain (FD) soft cancellation (SC) minimum mean-squared error (MMSE) equalization is considered. A novel framework for transmission power minimization subject to equalizer convergence constraints, referred as convergence constrained power allocation (CCPA) method, is proposed based on extrinsic information transfer (EXIT) chart analysis. The proposed method decouples the spatial interference between the streams using singular value decomposition (SVD), and minimizes the transmission power while achieving the target mutual information for each stream after iterations at the receiver. We show that the transmission power optimization can be formulated as a convex optimization problem. Three CCPA methods, one approximately optimal, and other two heuristic methods inspired by the Lagrange duality are derived. The numerical results demonstrate that the proposed scheme outperforms the existing linear precoding schemes. Moreover, the proposed heuristic schemes can achieve performance close with that of the approximately optimal method in terms of the equalizer convergence properties as well as transmission power. [ABSTRACT FROM PUBLISHER]

Joint design of linear multiuser multiple-input-multiple-output (MIMO) transceiver subject to different quality of service (QoS) constraints per user and with per-antenna or antenna group power constraints is considered. Solutions for two linear transceiver optimization problems are proposed, i.e., balancing the weighted signal-to-interference-plus-noise ratio (SINR) per data stream and balancing the weighted rate for each scheduled user with minimum rate requirements per user. The proposed joint transceiver optimization algorithms are compared to corresponding optimal nonlinear transmission methods as well as to generalized zero-forcing transmission solutions. Unlike the optimal nonlinear schemes, the optimization problems employed in the linear multiuser MIMO transceiver design are not convex in general. However, the proposed algorithms are shown to provide very efficient solutions despite of the fact that the global optimum cannot be guaranteed due to nonconvexity of the problems. [ABSTRACT FROM PUBLISHER]

We consider a single-cell multiple-input multiple-output (MIMO) downlink channel where linear transmission and reception strategy is employed. The base station (BS) transmitter is equipped with a scheduler using a simple opportunistic beamforming strategy, which associates an intended user for each of the transmitted data streams. For the case when the channel of the scheduled users is available at the BS, we propose a general method for joint design of the transmit and the receive beamformers according to different optimization criteria, including weighted sum rate maximization, weighted sum mean square error minimization, minimum signal-to-interference-plus-noise ratio (SINR) maximization and sum power minimization under a minimum SINR constraint. The proposed method can handle multiple antennas at the BS and at the mobile user with single and/or multiple data streams per scheduled user. The optimization problems encountered in the beamformer design (e.g., covariance rank constraint) are not convex in general. Therefore, the problem of finding the global optimum is intrinsically nontractable. However, by exploiting the uplink-downlink SINR duality, we decompose the original optimization problem as a series of simpler optimization problems which can be efficiently solved by using standard convex optimization tools. Even though each subproblem is optimally solved, there is no guarantee that the global optimum has been found due to the nonconvexity of the problem. However, the simulations show that the algorithms converge fast to a solution, which can be a local optimum, but is still efficient. [ABSTRACT FROM AUTHOR]

A novel low-complexity and provably convergent algorithm is proposed to find the sum capacity for vector Gaussian broadcast channels. Unlike the recently proposed sum-power constraint iterative waterfilling (SPC-IWF) algorithms, it has lower complexity and requires no additional precautions to ensure the convergence. We have proved analytically the convergence with probability one, and the computer simulations show that the proposed algorithm converges faster than the earlier variants of SPC-IWF algorithms. We formulate the problem in the context of a multiple-input multiple-output orthogonal frequency-division multiplexing system and discuss the simplifications provided by the block-diagonal channel structure. [ABSTRACT FROM AUTHOR]