Approximating the constellation constrained capacity of the MIMO channel with discrete input

Yankov, Metodi P.; Forchhammer, Søren; Larsen, Knud J.; Christensen, L. Peter

doi:10.1109/icc.2015.7248952

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…A strategy to optimize the MIMO precoder and the input distribution jointly is presented in [36]. Also, MIMO precoder optimization with discrete inputs in the absence of channel state information at the transmitter is considered in [37]. A more detailed summary of results pertaining to the capacity limits of MIMO channels with discrete input constellations can be found in [38].…”

Section: Introductionmentioning

confidence: 99%

Capacity Performance of Tensor Multi-Domain Communication Systems With Discrete Signalling Constellations

Pandey

Leib

2023

IEEE Open J. Commun. Soc.

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Modern communication systems employ multi-domain modulation and coding techniques for effectively exploiting all available resources. Hence in such systems the transmit and receive signals have an inherent multi-domain structure which can be represented using tensors. This work considers the capacity of higher order tensor channels associated with such multi-domain communication systems when the elements of the input tensor are constrained to be drawn from discrete signalling constellations. We establish a relationship between the tensor gradient of the mutual information and the error covariance tensor associated with the minimum mean squared error estimator at the receiver. This relation is used to iteratively find a multi-linear precoder at the input which achieves capacity of the tensor channel under the signalling constellation constraints. Through numerical examples, we show the convergence behavior of the proposed precoder, and compare the capacity achieved under different constellations with the capacity when the input is Gaussian. Further, we exploit the tensor formulation of the problem to find the channel capacity under a variety of different power constraints spanning across several domains. At high SNR, the constellation constraints saturates the capacity while at low SNRs, the constellation constraints are not too relevant, and the power constraints dominate and limit the performance. The capacity saturation level depends on the input order and distribution.

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Section: Introductionmentioning

confidence: 99%

Capacity Performance of Tensor Multi-Domain Communication Systems With Discrete Signalling Constellations

Pandey

Leib

2023

IEEE Open J. Commun. Soc.

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“…The channel capcity has became an majorly considerable paramerter in todays and upcoming wireless systems for the communication [1]. Generally, the channel capacuty of an fading channel can be referred as the sum of all the narrow band flat fading channel capacities at every discrete frequency points [2]. Hence, various radio techniques have been evolved in recent past where wideband technique has come up with better data communication rate for the short range wireless system [3].…”

Section: Introductionmentioning

confidence: 99%

PSO-CCO_MIMO-SA: A particle swarm optimization based channel capacity optimzation for MIMO system incorporated with smart antenna

Shivapanchakshari

Aravinda

2020

IJECE

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With the radio channels physical limits, achieving higher data rate in the multi-channel systems is been a biggest concern. Hence, various spatial domain techniques have been introduced by incorporating array of antenna elements (i.e., smart antenna) in recent past for the channel limit expansion in mobile communication antennas. These smart antennas help to yield the improved array gain or bearm forming gain and hence by power efficiency enhanmaent in the channel and antenna range expansion. The use of smart antenna leads to spatial diversity and minimizes the fading effect and improves link reliability. However, in the process of antenna design, the proper channel modelling is is biggest concern which affect the wireless system performance. The recent works of MIMO design systems have discussed the issues in number of antenna selection which suggests that optimization of MIMO channel capacity is required. Hence, a Particle Swarm Optimization based channel capacity optimzation for MIMO system incorporated with smart antenna is introduced in this paper. From the outcomes it is been found that the proposed PSO based MIMO system achieves better convergenece speed which results in better channel capacity.

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A Survey on MIMO Transmission With Finite Input Signals: Technical Challenges, Advances, and Future Trends

Xiao

Ding

et al. 2018

Proc. IEEE

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Approximating the constellation constrained capacity of the MIMO channel with discrete input

Cited by 4 publications

References 17 publications

Capacity Performance of Tensor Multi-Domain Communication Systems With Discrete Signalling Constellations

Capacity Performance of Tensor Multi-Domain Communication Systems With Discrete Signalling Constellations

PSO-CCO_MIMO-SA: A particle swarm optimization based channel capacity optimzation for MIMO system incorporated with smart antenna

A Survey on MIMO Transmission With Finite Input Signals: Technical Challenges, Advances, and Future Trends

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