Antenna subset diversity with non-ideal channel estimation

Gifford, Wesley M.; Win, Moe Z.; Chiani, Marco

doi:10.1109/twc.2008.05744

Cited by 60 publications

(28 citation statements)

References 22 publications

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“…This is done by transforming the CDFs of the estimated parameters, characterized previously in (14), (15), (17) and (18). To begin with, we consider the CDF of the P d in order to respect the constraint on the detection probability defined in (19).…”

Section: Characterization Of Performance Parametersmentioning

confidence: 99%

Performance analysis of hybrid cognitive radio systems with imperfect channel knowledge

Kaushik

Sharma

Chatzinotas

et al. 2016

2016 IEEE International Conference on Communications (ICC)

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Abstract-In this paper, we study the performance of hybrid cognitive radio systems that combine the benefits of interweave and underlay systems by employing a spectrum sensing and a power control mechanism at the Secondary Transmitter (ST). Existing baseline models considered for performance analysis assume perfect knowledge of the involved channels at the ST, however, such situations hardly exist in practical deployments. Motivated by this fact, we propose a novel approach that incorporates channel estimation at the ST, and consequently characterizes the performance of Hybrid Systems (HSs) under realistic scenarios. To capture the impact of imperfect channel knowledge, we propose outage constraints on the detection probability at the ST and on the interference power received at the primary receiver. Our analysis reveals that the baseline model overestimates the performance of the HS in terms of achievable secondary user throughput. Finally, based on the proposed estimation-sensing-throughput tradeoff, we determine suitable estimation and sensing durations that effectively capture the effect of imperfect channel knowledge and subsequently enhance the achievable secondary user throughput.

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Section: Characterization Of Performance Parametersmentioning

confidence: 99%

Performance analysis of hybrid cognitive radio systems with imperfect channel knowledge

Kaushik

Sharma

Chatzinotas

et al. 2016

2016 IEEE International Conference on Communications (ICC)

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“…5 This expression implicitly assumes that the variance of D is the same for all MQAM symbols. However, with imperfect estimation, this is no longer the case, as can be seen from (8). However, this approximation is quite good and is commonly used [8].…”

Section: B Proof Of Theoremmentioning

confidence: 99%

Optimal Weighted Antenna Selection for Imperfect Channel Knowledge from Training

Kristem

Mehta

Molisch

2009

2009 IEEE International Conference on Communications

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Receive antenna selection (AS) reduces the hardware complexity of multi-antenna receivers by dynamically connecting an instantaneously best antenna element to the available radio frequency (RF) chain. Due to the hardware constraints, the channels at various antenna elements have to be sounded sequentially to obtain estimates that are required for selecting the "best" antenna and for coherently demodulating data. Consequently, the channel state information at different antennas is outdated by different amounts. We show that, for this reason, simply selecting the antenna with the highest estimated channel gain is not optimum. Rather, the channel estimates of different antennas should be weighted differently, depending on the training scheme. We derive closed-form expressions for the symbol error probability (SEP) of AS for MPSK and MQAM in time-varying Rayleigh fading channels for arbitrary selection weights, and validate them with simulations. We then derive an explicit formula for the optimal selection weights that minimize the SEP. We find that when selection weights are not used, the SEP need not improve as the number of antenna elements increases, which is in contrast to the ideal channel estimation case. However, the optimal selection weights remedy this situation and significantly improve performance.

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“…Estimation errors and feedback delay render the CSIT imperfect. The effect of estimation errors on MIMO antenna selection is studied in [7] and the effect of feedback delay for a specific case of a 2 × 1 978-1-4244-5489-1/10/$26.00 c 2010 IEEE system is studied in [8]. Outage performance and DiversityMultiplexing gain Tradeoff (DMT) of the MIMO antenna selection are presented in [9] for the general imperfect CSIT model that encompasses feedback delay, estimation errors and prediction.…”

Section: Introductionmentioning

confidence: 99%

“…Outage performance and DiversityMultiplexing gain Tradeoff (DMT) of the MIMO antenna selection are presented in [9] for the general imperfect CSIT model that encompasses feedback delay, estimation errors and prediction. However, rate adaptation has not been considered in [7], [8], [9]. In practice, systems with CSIT are likely to adapt the transmission rate while maintaining a target outage probability.…”

Section: Introductionmentioning

confidence: 99%

Rate adaptation in MIMO antenna selection system with imperfect CSIT

Ramya

Bhashyam

2010

2010 Second International Conference on COMmunication Systems and NETworks (COMSNETS 2010)

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In this paper, we analyze the performance of an adaptive Multiple Input Multiple Output (MIMO) system employing transmit antenna selection and rate adaptation based on channel state information (CSI). Imperfections in CSI due to estimation error and feedback delay are considered. Since CSI is imperfect, the rate of transmission is chosen in order to meet a target outage probability. The average throughput is evaluated for various cases of imperfect CSI using Monte Carlo simulations. Simulation results show that rate adaptation provides significant gains even with imperfect CSI. Furthermore, prediction can be used to effectively combat the effect of feedback delay.

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Antenna subset diversity with non-ideal channel estimation

Cited by 60 publications

References 22 publications

Performance analysis of hybrid cognitive radio systems with imperfect channel knowledge

Performance analysis of hybrid cognitive radio systems with imperfect channel knowledge

Optimal Weighted Antenna Selection for Imperfect Channel Knowledge from Training

Rate adaptation in MIMO antenna selection system with imperfect CSIT

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