Performance analysis and design optimization of LDPC coded MIMO OFDM systems

Lu, Bin; Yue, Guosen; Wang, Xin

doi:10.1109/glocom.2003.1258451

Cited by 28 publications

(54 citation statements)

References 28 publications

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“…r For large to infinite block size, certain irregularities can be introduced into LDPC codes for reducing the decoding threshold at low SNR's, e.g., [10,28,29,33,36]. Interestingly, similar efforts have also been made to design "irregular turbo codes" [6,18].…”

Section: Low Density Parity Check Codesmentioning

confidence: 98%

“…Note that in this work, for simplicity, only the fixed/deterministic (in contrast to variable/adaptive) signal constellation is considered, and its averaged power is normalized to be 1. The formulas to compute the ergodic and outage capacity for finite-constellation-size modulation (e.g., QAM) MIMO OFDM systems are further referred to [28].…”

Section: Mimo Ofdm Modulationmentioning

confidence: 99%

“…3) to approach the maximum likelihood (ML) receiver performance of joint MIMO OFDM demodulation and LDPC decoding. The extrinsic information of the LDPC coded bits is iteratively passed between a soft-input-soft-output (abbreviated as "soft") demodulator and a soft LDPC decoder; in each demodulatordecoder iteration, a number of inner iterations is performed within the soft LDPC decoder during which extrinsic information is passed along the edges in the bipartite graph, [28].…”

Section: Iterative Receiver For Ldpc Coded Mimo Ofdmmentioning

confidence: 99%

“…where R l = R represent the receive and transmit correlation matrices, which are determined by the spacing and the angle spread of MIMO antennas and whose details are referred to [28] L is the number of resolvable paths of the frequency-selective fading channels; H l [ p] is the matrix with entries being independent and identically distributed (i.i.d.) circularly symmetric complex Gaussian distributed as ∼ N c (0, β 2 l ), and is assumed to be independent for different l and different p; in addition, the power of H l [ p] is normalized as L−1 l=0 β l 2 ≡ 1.…”

Section: Mimo Ofdm Modulationmentioning

confidence: 99%

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Physical-layer air interface solutions for broadband high-speed wireless cellular systems

Wang

Madihian

2006

Wireless Netw

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We propose the physical-layer (PHY) air interface solutions for downlink and uplink transmissions in broadband high-speed wireless cellular systems. A system based on low-density parity-check (LDPC) coded multiple-input-multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) time-division multiple-accessing (TDMA) (with scheduling) is proposed for downlink transmission; and a system based on orthogonal space-time block coded (STBC) multi-carrier code-division multiple-accessing (MC-CDMA) is proposed for uplink transmission. The proposed scheme can support ∼100 Mbps peak rate over 25 MHz bandwidth downlink channels and ∼30 Mbps sum rate of multiple users over 25 MHz uplink channels. Moreover, the proposed solutions provide excellent performance and reasonable complexity for mobile station and for base station.Keywords Broadband . Wide-band . High-speed . High data rate . Physical layer . Air interface . Uplink . Downlink . MIMO . OFDM . Multi-carrier(MC) . CDMA . LDPC

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Section: Low Density Parity Check Codesmentioning

confidence: 98%

Section: Mimo Ofdm Modulationmentioning

confidence: 99%

Section: Iterative Receiver For Ldpc Coded Mimo Ofdmmentioning

confidence: 99%

Section: Mimo Ofdm Modulationmentioning

confidence: 99%

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Physical-layer air interface solutions for broadband high-speed wireless cellular systems

Wang

Madihian

2006

Wireless Netw

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“…Figure 1 shows the Turbo coded MIMO-OFDM system with the proposed iterative softKalman filter based joint channel and data detection structure. This iterative detection/decoding procedure is similar to [10,28,29]. The extrinsic decoder information, denoted by k 2 (b k,j p ), becomes increasingly accurate as long as the SNR is above a threshold.…”

Section: Approximate Error Covariance and Lower Boundmentioning

confidence: 99%

Iterative Soft-Kalman Filter-based Data Detection and Channel Estimation for Turbo Coded MIMO–OFDM Systems

Kim

Iltis

2007

Int J Wireless Inf Networks

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MIMO channels are often assumed to be constant over a block or packet. This assumption of block stationarity is valid for many fixed wireless scenarios. However, for communications in a mobile environment, the stationarity assumption will result in considerable performance degradation. In this paper, we focus on a new channel estimation technique for Turbo coded MIMO systems using OFDM. In the proposed MIMO-OFDM system, pilots are placed on selected subcarriers and used by a pair of Kalman filter (KF) channel estimators at the receiver. The KF channel estimates are then utilized by a MIMO-OFDM soft data detector based on the computationally efficient QRD-M algorithm. The soft detector output is fed back to the Kalman filters to iteratively improve the channel estimates. The extrinsic information generated by the Turbo decoder is also used as a priori information for the soft data detector. The overall receiver thus combines MIMO data detection, KF-based channel estimation, and Turbo decoding in a joint iterative structure yielding computational efficiency and improved bit-error rate (BER) performance.

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Analogue joint source‐channel coding for multiple input multiple output–orthogonal frequency division multiplexing systems

Fresnedo

Vázquez-Araújo

Castedo

et al. 2014

Trans. Emerging Tel. Tech.

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Analogue joint source-channel coding (JSCC) has been shown to approach the optimal distortion-cost trade-off when transmitting over additive white Gaussian noise channels. In this work, we consider analogue JSCC over frequencyselective channels using orthogonal frequency division multiplexing (OFDM) modulation and multiple antennas at transmission and/or reception, that is, using a multiple input multiple output(MIMO)-OFDM system. Because of its high complexity, optimal Minimum Mean Square Error (MMSE) analogue JSCC decoding is infeasible in MIMO-OFDM systems and a practical two-stage decoding approach made up of an MMSE estimator followed by a maximum likelihood decoder is proposed instead. Three different alternatives for system optimisation are considered: non-adaptive coding, adaptive coding and adaptive coding with precoding. We show that the three considered strategies approach the optimal distortion-cost trade-off, but the best performance is obtained with the adaptive coding scheme when precoding is utilised.

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Performance analysis and design optimization of LDPC coded MIMO OFDM systems

Cited by 28 publications

References 28 publications

Physical-layer air interface solutions for broadband high-speed wireless cellular systems

Physical-layer air interface solutions for broadband high-speed wireless cellular systems

Iterative Soft-Kalman Filter-based Data Detection and Channel Estimation for Turbo Coded MIMO–OFDM Systems

Analogue joint source‐channel coding for multiple input multiple output–orthogonal frequency division multiplexing systems

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