Multi-user MIMO

About: Multi-user MIMO is a research topic. Over the lifetime, 10265 publications have been published within this topic receiving 227206 citations. The topic is also known as: multi user mimo & MU-MIMO.

What makes a good MIMO channel model

Abstract: Using different meaningful measures of quality, the paper investigates the accuracy of analytical MIMO channel models. Different metrics should be applied if the underlying MIMO channel supports predominantly beamforming, spatial multiplexing or diversity. The number of envisaged antennas plays an important role. By comparing the results of an extensive indoor measurement campaign at 5.2 GHz, we find the following main conclusions: (i) the recently developed Weichselberger model predicts capacity for any antenna number and represents diversity best of all three models, but still not satisfactorily; (ii) except for 2/spl times/2 MIMO systems, the Kronecker model fails to predict capacity, joint angular power spectrum, and diversity; (iii) the virtual channel representation should only be used for modeling the joint angular power spectrum for very large numbers of antennas. The answer to the question posed in the title: the appropriate model has to be chosen according to the considered application.

Signal Identification for Multiple-Antenna Wireless Systems: Achievements and Challenges

Abstract: Signal identification is an umbrella term for signal processing techniques designed for the identification of the transmission parameters of unknown or partially known communication signals. Initially, a key technology for military applications such as signal interception, radio surveillance and electronic warfare, signal identification techniques recently found applications in commercial wireless communications as an enabling technology for cognitive receivers. With the advance and rapid adoption of multiple-input multiple-output (MIMO) communication systems in the last decade, extension of signal identification methods to include this transmission paradigm has become a priority and focus of intensive research efforts. The aim of this work is to provide a comprehensive state-of-the-art survey on algorithms proposed for the new and challenging signal identification problems specific to MIMO systems, including space-time block code (STBC) identification, MIMO modulation identification, and detection of the number of transmit antennas. Finally, concluding remarks on MIMO signal identification are provided along with an outline of the open problems and future research directions.

Fundamentals and challenges of optical multiple-input multiple-output multimode fiber links [Topics in Optical Communications]

Abstract: In this article we discuss the application of MIMO processing to multimode fiber links. MIMO processing is shown to increase the information capacity of communication links linearly as the minimum number of transmitters/receivers increases. The fundamentals of optical MIMO fiber links are presented, and the promises and challenges of such systems are elaborated

Capacity and downlink transmission algorithms for a multi-user MIMO channel

Abstract: Downlink beamforming in a multi-user MIMO channel can provide significant gain in system throughput by allowing space division multiple access (SDMA). The exact solution for the sum capacity of such channels does not exist in closed form, but requires an expensive iterative algorithm. By imposing certain constraints on the capacity equation, a sub-optimal closed-form solution can be obtained. The paper presents two such solutions. The first, referred to as "block-diagonalization" arises from forcing all inter-user interference to zero. The second - "successive optimization" - is an alternative method of minimizing transmit power for an arbitrary information rate per user, and allows solutions which, under certain circumstances, are superior to the block-diagonalization approach. Both algorithms have sub-optimal performance, but they lead to simpler transmitter and receiver structures, and allow a tradeoff between performance and complexity.

Improving Energy Efficiency in a Wireless Sensor Network by Combining Cooperative MIMO With Data Aggregation

Abstract: In wireless sensor networks where nodes are powered by batteries, it is critical to prolong the network lifetime by minimizing the energy consumption of each node. In this paper, the cooperative multiple-input-multiple-output (MIMO) and data-aggregation techniques are jointly adopted to reduce the energy consumption per bit in wireless sensor networks by reducing the amount of data for transmission and better using network resources through cooperative communication. For this purpose, we derive a new energy model that considers the correlation between data generated by nodes and the distance between them for a cluster-based sensor network by employing the combined techniques. Using this model, the effect of the cluster size on the average energy consumption per node can be analyzed. It is shown that the energy efficiency of the network can significantly be enhanced in cooperative MIMO systems with data aggregation, compared with either cooperative MIMO systems without data aggregation or data-aggregation systems without cooperative MIMO, if sensor nodes are properly clusterized. Both centralized and distributed data-aggregation schemes for the cooperating nodes to exchange and compress their data are also proposed and appraised, which lead to diverse impacts of data correlation on the energy performance of the integrated cooperative MIMO and data-aggregation systems.