VLSI Implementation of a Fixed-Complexity Soft-Output MIMO Detector for High-Speed Wireless

This paper presents a low-complexity MIMO symbol detector with close-Maximum a posteriori performance for the emerging multiantenna enhanced high-speed wireless communications. The VLSI implementation is based on a novel MIMO detection algorithm called Modified Fixed-Complexity Soft-Output (MFCSO) detection, which achieves a good trade-off between performance and implementation cost compared to the referenced prior art. By including a microcode-controlled channel preprocessing unit and a pipelined detection unit, it is flexible enough to cover several different standards and transmission schemes. The flexibility allows adaptive detection to minimize power consumption without degradation in throughput. The VLSI implementation of the detector is presented to show that real-time MIMO symbol detection of 20 MHz bandwidth 3GPP LTE and 10 MHz WiMAX downlink physical channel is achievable at reasonable silicon cost

Computationally-Efficient Design of a Dynamic System-Level LTE Simulator

The Long-Term Evolution (LTE) is the next generation of current mobile telecommunication networks. LTE has a new flat radio-network architecture and a significant increase in spectrum efficiency. In this paper, a computationally-efficient tool for dynamic system-level LTE simulations is proposed. A physical layer abstraction is performed to predict link-layer performance with a low computational cost. At link layer, there are two important functions designed to increase the network capacity: Link Adaptation and Dynamic Scheduling. Other Radio Resource Management functionalities such as Admission Control and Mobility Management are performed at network layer. The simulator is conceived for large simulated network time to allow evaluation of optimization algorithms for the main network-level functionalities

Appropriate Algorithms for Estimating Frequency-Selective Rician Fading MIMO Channels and Channel Rice Factor: Substantial Benefits of Rician Model and Estimator Tradeoffs

<p/> <p>The training-based channel estimation (TBCE) scheme in multiple-input multiple-output (MIMO) frequency-selective Rician fading channels is investigated. We propose the new technique of shifted scaled least squares (SSLS) and the minimum mean square error (MMSE) estimator that are suitable to estimate the above-mentioned channel model. Analytical results show that the proposed estimators achieve much better minimum possible Bayesian Cram&#233;r-Rao lower bounds (CRLBs) in the frequency-selective Rician MIMO channels compared with those of Rayleigh one. It is seen that the SSLS channel estimator requires less knowledge about the channel and/or has better performance than the conventional least squares (LS) and MMSE estimators. Simulation results confirm the superiority of the proposed channel estimators. Finally, to estimate the channel Rice factor, an algorithm is proposed, and its efficiency is verified using the result in the SSLS and MMSE channel estimators.</p