Orthogonal frequency-division multiplexing (OFDM) effectively mitigates intersymbol interference (ISI) caused by the delay spread of wireless channels. Therefore, it has been used in many wireless systems and adopted by various standards. In this paper, we present a comprehensive survey on OFDM for wireless communications. We address basic OFDM and related modulations, as well as techniques to improve the performance of OFDM for wireless communications, including channel estimation and signal detection, time- and frequency-offset estimation and correction, peak-to-average power ratio reduction, and multiple-input-multiple-output (MIMO) techniques. We also describe the applications of OFDM in current systems and standards.

OFDM and Its Wireless Applications: A Survey

This paper presents analysis of performance measurements from a MIMO-OFDM IEEE 802.11n hardware implementation at 5.2 GHz using four transmitters and four receivers. Two spatial multiplexing systems are compared; one which uses a zero-forcing (ZF) detector and the other a list sphere detector (LSD). We show that the measured results do not align with standard prediction based on simulation assuming uncorrelated receiver noise. We show that the discrepancy can be explained by the inclusion of transmitter noise into the channel model. This effect is not included in existing MIMO-OFDM channel models. The measured results from our hardware implementation show successful packet transmission at 600 Mb/s with 15 bits/s/Hz spectral efficiency at 73% coverage for ZF and 84% coverage for LSD with an average receiver signal to noise ratio (SNR) of 26 dB.

Transmitter Noise Effect on the Performance of a MIMO-OFDM Hardware Implementation Achieving Improved Coverage

Lattice reduction-aided decoding (LRAD) features reduced decoding complexity and near-optimum performance in multiinput multioutput communications. In this paper, a quantitative error-rate analysis of LRAD is presented. To this aim, the proximity factors are defined to measure the worst-case losses in decoding distances associated with the decision region relative to infinite lattice decoding (ILD), namely, closest point search in an infinite lattice. Upper bounds on the proximity factors are derived, which are functions of the dimension n of the lattice alone. The study is then extended to the dual-basis reduction. It is found that the bounds for dual basis reduction may be smaller. Reasonably good bounds are derived in many cases. Proximity factors not only imply the same diversity order in fading channels, but also relate the error probabilities of ILD and LRAD.

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On the Proximity Factors of Lattice Reduction-Aided Decoding

We introduce a new family of detectors for multiple-input multiple-output (MIMO) channels called Chase detectors because of their resemblance to the well-known Chase algorithm for soft decoding of error-control codes. A Chase detector is fully specified by only three simple parameters; nevertheless, it reduces to a wide range of previously reported MIMO detectors as special cases, including the maximum-likelihood and decision-feedback detectors. Based on the Chase framework, we propose two new detectors, the B-Chase and L-Chase detectors, both of which perform well on fading channels. In fact, the L-Chase detector is shown to outperform the BLAST-ordered decision-feedback detector by 9.8 dB, while simultaneously requiring 17% fewer computations, on a 4-input 4-output Rayleigh-fading channel with uncoded 4-QAM inputs. Under the same conditions, the B-Chase detector falls only 0.2 dB short of the minimum-mean-squared-error sphere detector, while requiring 50% fewer computations.

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The Chase family of detection algorithms for multiple-input multiple-output channels

We introduce a new family of detectors for multiple-input multiple-output (MIMO) channels. These detectors are called Chase detectors because they can be interpreted as a translation of the Chase error-control decoding algorithm from time to space. The Chase detector is parameterized by only four parameters; nevertheless, it reduces to a wide range of previously reported MIMO detectors as special cases, including the maximum-likelihood and decision-feedback detectors. The Chase detector defines a simple framework for not only comparing existing MIMO detection algorithms but also proposing new ones. For example, based on the Chase framework, we propose a new detector called B-Chase that performs well on fading channels. Specifically, on a four-input four-output Rayleigh-fading channel with uncoded 16-QAM inputs, one instance of the B-Chase detector falls only 0.4 dB short of the performance of the maximum-likelihood sphere detector while reducing complexity by 68%. Another instance of the B-Chase detector outperforms the BLAST-ordered decision-feedback detector by 4.4 dB while increasing complexity by only 17%.

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The Chase Family of Detection Algorithms for Multiple-Input Multiple-Output Channels

The decision-feedback (DF) detector is a nonlinear detection strategy for multiple-input multiple-output (MIMO) channels that can significantly outperform a linear detector, especially when the order in which the inputs are detected is optimized according to the so-called Bell Labs Layered Space-Time (BLAST) ordering. The DF detector may be implemented as the cascade of a linear detector, which mitigates interference at the expense of correlating the noise, followed by a noise predictor, which exploits the correlation in the noise to reduce its variance. With this architecture, existing linear detectors can be easily upgraded to DF detectors. We propose a low-complexity algorithm for determining the BLAST ordering that is facilitated by the noise-predictive architecture. The resulting ordered noise-predictive DF detector requires fewer computations than previously reported ordered-DF algorithms. We also propose and derive the ordered noise-predictive minimum-mean-squared-error DF detector and show how to determine its BLAST ordering with low complexity.

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Noise-predictive decision-feedback detection for multiple-input multiple-output channels

A three-transmitter three-receiver orthogonal frequency-division multiplexing Bell Laboratories layered space-time testbed is set up, which achieves a peak data rate of 281.25 Mb/s and a spectral efficiency of 14.4 b/Hz/s. The transmitter of the testbed consists of three signal generators transmitting three independent OFDM signals at 25 Msamples/s synchronously. Three synchronized receiving links are used, each of which includes an RF receiver, an analog-to-digital converter, a digital downconverter, and a PowerPC processor for baseband processing. The performance and complexity of three typical BLAST detection techniques (linear detection, ordered decision feedback detection, and partial decision detection) are evaluated and compared using the data from the experiments conducted in both line-of-sight and non-line-of-sight indoor environments.

Implementation and experimental results of a three-transmitter three-receiver OFDM/BLAST testbed

The performance of a decision-feedback detector on a fading multiple-input multiple-output channel is limited by the low diversity of the first symbol detected. We propose a new family of detection techniques which overcomes this bottleneck by using a list detector for the first symbol; the list detector is then combined with a parallel bank of decision-feedback detectors, one for each element of the list. The detector family is parameterized by the length of the list, which can be adjusted to achieve a wide range of attractive trade-offs between performance and complexity. For example, on a 4-input 4-output Rayleigh-fading channel with uncoded 16-QAM inputs, one version of the proposed detector outperforms the popular minimum-mean-squared-error BLAST-ordered decision-feedback detector by 1.5 dB, while simultaneously requiring 6% fewer computations.

The sorted-QR Chase detector for multiple-input multiple-output channels

Two powerful techniques for improving the performance of a detector in a multiple-input multiple-output communications channel are decision feedback and lattice reduction. We propose a new detector, the DOLLAR detector, that combines decision feedback and lattice reduction in a novel way. The DOLLAR detector is based on a simple lattice reduction algorithm that is designed to exploit the power of decision feedback. The combination of the new lattice reduction algorithm and decision feedback leads to near-optimal performance with complexity comparable to the BLAST-ordered decision-feedback (BODF) detector. For example, over a 4-input 4-output Rayleigh-fading channel with 16-QAM inputs, the DOLLAR detector outperforms the BODF detector by 6 dB while increasing complexity by 16%.

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D.W. Waters

Papers

Noise-predictive decision-feedback detection for multiple-input multiple-output channels

Implementation and experimental results of a three-transmitter three-receiver OFDM/BLAST testbed

The sorted-QR Chase detector for multiple-input multiple-output channels

A reduced-complexity lattice-aided decision-feedback detector