Modeling of Wide-Band MIMO Radio Channels Based on NLoS Indoor Measurements

Abstract-In this letter, we investigated the optimal minimummean-squared-error (MMSE) based successive interference cancellation (SIC) strategy designed for lattice-reduction aided multiple-input multiple-output (MIMO) detectors. For the sake of generating the MMSE-based MIMO symbol estimate at each SIC detection stage, we model the so-called effective symbols generated with the aid of lattice-reduction as joint Gaussian distributed random variables. However, after lattice-reduction, the effective symbols become correlated and exhibit a non-zero mean. Hence, we derive the optimal MMSE SIC detector, which updates the mean and variance of the effective symbols at each SIC detection stage. As a result, the proposed detector achieves a better performance compared to its counterpart dispensing with updating the mean and variance, and performs close to the maximum likelihood detector.

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“…We used the Kronecker-structure based channel model, in which the channel matrix is generated as [17] …”

Section: Simulation Resultsmentioning

confidence: 99%

Optimal Lattice-Reduction Aided Successive Interference Cancellation for MIMO Systems

Lee

Chun

Hanzo

2007

IEEE Trans. Wireless Commun.

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“…This model is called Rician fading or Rayleigh fading (ifh k = 0), since the magnitude of each channel element is Rice or Rayleigh distributed, respectively. Although simple, this model makes sense in rich multipath scenarios (e.g., based on the Lindeberg Central limit theorem [309]) and has been validated by measurements [54,132,288,294,306]. The spatial directivity is specified by the off-diagonal elements in R k and the exponential correlation model in [162] provides a simple parametrization.…”

Section: Resource Allocationmentioning

confidence: 99%

Optimal Resource Allocation in Coordinated Multi-Cell Systems

Björnson

2013

FNT in Communications and Information Theory

287

317

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The use of multiple antennas at base stations is a key component in the design of cellular communication systems that can meet high-capacity demands in the downlink. Under ideal conditions, the gain of employing multiple antennas is well-recognized: the data throughput increases linearly with the number of transmit antennas if the spatial dimension is utilized to serve many users in parallel. The practical performance of multi-cell systems is, however, limited by a variety of nonidealities, such as insufficient channel knowledge, high computational complexity, heterogeneous user conditions, limited backhaul capacity, transceiver impairments, and the constrained level of coordination between base stations. This tutorial presents a general framework for modeling different multi-cell scenarios, including clustered joint transmission, coordinated beamforming, interference channels, cognitive radio, and spectrum sharing between operators. The framework enables joint analysis and insights that are both scenario independent and dependent.The performance of multi-cell systems depends on the resource allocation; that is, how the time, power, frequency, and spatial resources are divided among users. A comprehensive characterization of resource allocation problem categories is provided, along with the signal processing algorithms that solve them. The inherent difficulties are revealed: (a) the overwhelming spatial degrees-of-freedom created by the multitude of transmit antennas; and (b) the fundamental tradeoff between maximizing aggregate system throughput and maintaining user fairness. The tutorial provides a pragmatic foundation for resource allocation where the system utility metric can be selected to achieve practical feasibility. The structure of optimal resource allocation is also derived, in terms of beamforming parameterizations and optimal operating points.This tutorial provides a solid ground and understanding for optimization of practical multi-cell systems, including the impact of the nonidealities mentioned above. The Matlab code is available online for some of the examples and algorithms in this tutorial. IntroductionThis section describes a general framework for modeling different types of multi-cell systems and measuring their performance -both in terms of system utility and individual user performance. The framework is based on the concept of dynamic cooperation clusters, which enables unified analysis of everything from interference channels and cognitive radio to cellular networks with global joint transmission. The concept of resource allocation is defined as allocating transmit power among users and spatial directions, while satisfying a set of power constraints that have physical, regulatory, and economic implications. A major complication in resource allocation is the inter-user interference that arises and limits the performance when multiple users are served in parallel. Resource allocation is particularly complex when multiple antennas are employed at each base station. However, the throu...

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“…The MIMO system has a 0 antennas at the source, a i antennas at the i-th relay, and a N antennas at the destination. Then, the channel gain matrix at the i-th hop is represented by the Kronecker model [11,12,[31][32][33][34][35] as:…”

Section: Spatially Correlated Wireless Multi-hop Mimo Channelmentioning

confidence: 99%

CMI analysis and precoding designs for correlated multi-hop MIMO channels

Tran

Nguyen

2015

J Wireless Com Network

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Conditional mutual information (CMI) analysis and precoding design for generally correlated wireless multi-hop multi-input multi-output (MIMO) channels are presented in this paper. Although some particular scenarios have been examined in existing publications, this paper investigates a generally correlated transmission system having spatially correlated channel, mutually correlated source symbols, and additive colored Gaussian noise (ACGN). First, without precoding techniques, we derive the optimized source symbol covariances upon mutual information maximization. Secondly, we apply a precoding technique and then design the precoder in two cases: maximizing the mutual information and minimizing the detection error. Since the optimal design for the end-to-end system cannot be analytically obtained in closed form due to the non-monotonic nature, we relax the optimization problem and attain sub-optimal designs in closed form. Simulation results show that without precoding, the average mutual information obtained by the asymptotic design is very close to the one obtained by the optimal design, while saving a huge computational complexity. When having the proposed precoding matrices, the end-to-end mutual information significantly increases while it does not require resources of the system such as transmission power or bandwidth.

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Modeling of Wide-Band MIMO Radio Channels Based on NLoS Indoor Measurements

Cited by 242 publications

References 44 publications

Optimal Lattice-Reduction Aided Successive Interference Cancellation for MIMO Systems

Optimal Lattice-Reduction Aided Successive Interference Cancellation for MIMO Systems

Optimal Resource Allocation in Coordinated Multi-Cell Systems

CMI analysis and precoding designs for correlated multi-hop MIMO channels

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