Pilot-assisted estimation of mimo fading channel response and achievable data rates

Samardžija, Dragan; Mandayam, Narayan B.

doi:10.1109/tsp.2003.818158

Cited by 97 publications

(10 citation statements)

References 29 publications

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“…The capacity loss by the channel estimation error is calculated in [22,23]. Even though the channel estimation error occurs, the proposed MCP-IA always achieves the DoF and exhibits the small channel capacity loss which is within the errors derived in [22,23]. Thus, the proposed MCP-IA is robust to the channel estimation errors.…”

Section: Mobile Information Systemsmentioning

confidence: 72%

Macrocell Protection Interference Alignment in Two-Tier Downlink Heterogeneous Networks

Seo

Kim

Ahn

et al. 2017

Mobile Information Systems

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Conventional interference alignment (IA) has been developed to mitigate interference problems for the coexistence of picocells and macrocells. This paper proposes a macrocell protection interference alignment (MCP-IA) in two-tier MIMO downlink heterogeneous networks. The proposed method aligns the interference of the macro user equipment (UE) and mitigates the interference of the pico-UEs with a minimum mean squared error interference rejection combining (MMSE-IRC) receiver. Compared to the conventional IA, the proposed MCP-IA provides an additional array gain obtained by the precoder design of the macro BS and a diversity gain achieved by signal space selections. The degrees of freedom (DoF) of the proposed MCP-IA are equal to or greater than that of the conventional IA and are derived theoretically. Link level simulations show the link capacity and the DoF of the macro UE, and also exhibit the proposed MCP-IA attaining additional array gain and diversity gain. The system level simulation illustrates that the proposed method prevents the interference of the macro UE completely and preserves the throughput of the pico-UE irrespective of the number of picocells. For 4 × 2 antenna configuration, the system level simulation demonstrates that the proposed MCP-IA throughput of the macro UE is not affected by the number of picocells and that the proposed MCP-IA throughput of the picocells approaches that of single-user MIMO (SU-MIMO) with a 3% loss.

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Section: Mobile Information Systemsmentioning

confidence: 72%

Macrocell Protection Interference Alignment in Two-Tier Downlink Heterogeneous Networks

Seo

Kim

Ahn

et al. 2017

Mobile Information Systems

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“…The use of orthogonal sequences for symbol timing and channel learning has been used in many MIMO systems [41]. Although it is reasonable to assume to have different number of pilot symbols per antenna [42], here we consider that the pilot symbols are of the same length. In this section, we assume that the pilot sequences x i [l]'s for l = 1, · · · , L s (or L p ) assigned to the i-th antenna for i = 1, · · · , n are known at the receiver.…”

Section: Tx System Classification With Exact Knowledge Of Pilot mentioning

confidence: 99%

Detection of Single Versus Multiple Antenna Transmission Systems Using Pilot Data

Wimalajeewa

Varshney

Su³

2017

IEEE Trans. on Signal and Inf. Process. over Networks

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In this paper, we consider the problem of classifying the transmission system when it is not known a priori whether the transmission is via a single antenna or multiple antennas. The receiver is assumed to be employed with a known number of antennas. In a data frame transmitted by most multiple input multiple output (MIMO) systems, some pilot or training data is inserted for symbol timing synchronization and estimation of the channel. Our goal is to perform MIMO transmit antenna classification using this pilot data. More specifically, the problem of determining the transmission system is cast as a multiple hypothesis testing problem where the number of hypotheses is equal to the maximum number of transmit antennas. Under the assumption of receiver having the exact knowledge of pilot data used for timing synchronization and channel estimation, we consider maximum likelihood (ML) and correlation test statistics to classify the MIMO transmit system. When only probabilistic knowledge of pilot data is available at the receiver, a hybridmaximum likelihood (HML) based test statistic is constructed using the expectation-maximization (EM) algorithm. The performance of the proposed algorithms is illustrated via simulations and comparative merits of different techniques in terms of the computational complexity and performance are discussed.

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“…For the pilot-assisted MIMO system with ML channel estimation, the closed-loop ergodic capacity with water-filling can be obtained with the help of [20], [21]…”

Section: Ergodic Capacity Of Pilot-assisted Mimo Systemsmentioning

confidence: 99%

“…In this subsection, we give the simulation results of the relationship between the ergodic capacity and the feedback intervals. For simplicity, we assume that the block size is L = 100 with the duration of 1 ms, and the power of pilot is 10% of the total transmit power, which is a reasonable value in practice [21]. We select a relatively smaller value of SNR, which is 0 dB, and the Doppler frequency is 9.26 Hz (Moving speed is 5 km/h, and the Carrier Frequency is 2 GHz).…”

Section: B Ergodic Capacity and Feedback Intervalmentioning

confidence: 99%

On the Minimum Differential Feedback for Time-Correlated MIMO Rayleigh Block-Fading Channels

Zhang

Song

et al. 2012

IEEE Trans. Commun.

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In this paper, we consider a general multiple input multiple output (MIMO) system with channel state information (CSI) feedback over time-correlated Rayleigh block-fading channels. Specifically, we first derive the closed-form expression of the minimum differential feedback rate to achieve the maximum erdodic capacity in the presence of channel estimation errors and quantization distortion at the receiver. With the feedback-channel transmission rate constraint, in the periodic feedback system, we further investigate the relationship of the ergodic capacity and the differential feedback interval, and we find by theoretical analysis that there exists an optimal differential feedback interval to maximize ergodic capacity. Finally, analytical results are verified through simulations in a practical periodic differential feedback system using Lloyd's quantization algorithm.

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Pilot-assisted estimation of mimo fading channel response and achievable data rates

Cited by 97 publications

References 29 publications

Macrocell Protection Interference Alignment in Two-Tier Downlink Heterogeneous Networks

Macrocell Protection Interference Alignment in Two-Tier Downlink Heterogeneous Networks

Detection of Single Versus Multiple Antenna Transmission Systems Using Pilot Data

On the Minimum Differential Feedback for Time-Correlated MIMO Rayleigh Block-Fading Channels

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