Empirical-Stochastic LMS-MIMO Channel Model Implementation and Validation

King, Peter R.; Brown, Tim; Kyrgiazos, Argyrios; Evans, B.G.

doi:10.1109/tap.2011.2173448

Cited by 29 publications

(27 citation statements)

References 9 publications

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“…The channel model that is considered here is an empirical stochastic model for LMS-MIMO [26]. This model has been validated and compared with the other existing models [27].…”

Section: Channel Modelmentioning

confidence: 99%

“…This model has been validated and compared with the other existing models [27]. The stochastic properties of this model are derived from an S-band tree-lined road measurement campaign (suburban area) using dual circular polarizations at low elevations [26]. A four-state Markov model presented in Figure 5 later in this section is used to determine the present channel state using the probability transition matrix P presented in (2); the corresponding parameters of the current channel state are then used to compute large-scale fading (shadowing) matrix H large and small-scale fading (multipath) matrix H small that are summed together (in dB) to produce the channel matrix, H, in the following:…”

Section: Channel Modelmentioning

confidence: 99%

“…States are numbered and characterized as follows: State 1 is CP High XP High, State 2 is CP Low XP High, State 3 is CP High XP Low and State 4 is CP Low XP Low. The 4 × 4 transition matrix, P, is used to predict the next possible state [26]. Let P ij denote the transition probability from state i to state j, where i, j ∈ {1, 2, 3, 4}.…”

Section: Channel Modelmentioning

confidence: 99%

See 2 more Smart Citations

Packet scheduling in MIMO satellite long term evolution networks

Aiyetoro

Giambene

Takawira

2015

Int. J. Satell. Commun. Network.

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This paper deals with the performance evaluation of different packet scheduling schemes for Long Term Evolution\ud mobile satellite systems based on the adoption of a multi-user MIMO technique. The major breakthrough of\ud MIMO technology in terrestrial networks has motivated the interest here for the adoption of MIMO in mobile\ud satellite systems as well. In particular, a land mobile dual-polarized GEO satellite system has been considered\ud in this work. The aim of this paper is to propose new cross-layer packet scheduling schemes that achieve a good\ud trade-off among throughput, QoS and fairness and to conduct performance comparisons with other scheduling\ud schemes in the literature. This is the reason why this paper also proposes a new performance index that can be\ud used to evaluate the overall performance of each scheduler. The work shows that the new cross-layer scheduler,\ud called channel-based queue sensitive scheduler, attains the best performance in terms of the new comprehensive\ud performance index, thus representing an interesting solution for future mobile satellite systems

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“…The channel model that is considered here is an empirical stochastic model for LMS-MIMO [26]. This model has been validated and compared with the other existing models [27].…”

Section: Channel Modelmentioning

confidence: 99%

Section: Channel Modelmentioning

confidence: 99%

See 1 more Smart Citation

Packet scheduling in MIMO satellite long term evolution networks

Aiyetoro

Giambene

Takawira

2015

Int. J. Satell. Commun. Network.

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“…Stochastic processes arise naturally in many applications [29][30][31][32][33][34]. In this work, we are concerned with multivariable stochastic processes that are measureable and accumulated at defined intervals over a period.…”

Section: Concept Of Concatenated Processmentioning

confidence: 99%

Characterization and parameterization of dynamic wireless channels over long duration using evolutionary channel parameters

Chude-Okonkwo¹,

Chude-Olisah²,

Nunoo³

et al. 2015

Int J Communication

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Summary The characterization and parameterization of processes that arise in many fields of science and technology are very crucial. Of particular importance are dynamic processes whose statistics are time‐varying and are often modeled as stochastic processes. A typical example of such process is the wireless communication channel. Existing methods that are used to characterize and parameterize the dynamic stochastic wireless channel often consider short‐term duration over which the channel statistics are invariant. Conversely, this paper presents the characterization of the dynamic wireless communication channel over a long‐term duration where time/frequency channel realizations are obtained at sample intervals. To structure such channel realizations over a long duration, the idea of concatenating the ‘instantaneous’ channel realizations is presented. The resultant concatenated multivariable process is characterized using the concepts of process non‐summability and piecewise separability. Based on these concepts, the second‐order statistical parameterization of the concatenated stochastic process in both time and frequency domain is presented. The parameterization approach is based on fitting appropriate set of unit step functions that approximate the raw concatenated data using sets of evolutionary stationarity parameters. To illustrate the concepts developed in this paper, measurement‐based experiments and analysis are presented and adaptively applied to improve wideband multicarrier system performance. Copyright © 2015 John Wiley & Sons, Ltd.

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“…Among the various schemes (space, polarization or frequency diversity) polarization diversity offers good results within limited space and bandwidth. Although it has been applied mainly to linear polarization, recently it was demonstrated that circular polarization (CP) diversity also offers significantly greater channel capacity, especially for line-of-sight propagation [1]. This creates a need for simple and compact dual CP antennas, such as reported in this communication.…”

Section: Introductionmentioning

confidence: 99%