Capacity and coding for quantized MIMO systems

Nossek, Josef A.; Ivrlač, Michel T.

doi:10.1145/1143549.1143827

Cited by 69 publications

(60 citation statements)

References 5 publications

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“…At 10 dB SNR, for example, the corresponding ratio for 2-bit quantization is still a very high 85%, while at 20 dB, 3-bit quantization is enough to achieve 85% of the infinite-precision capacity. Similar encouraging results have been reported earlier in [12], [13] also. However, the input alphabet in these works was taken as binary to begin with, in which case the good performance with low-precision output quantization is perhaps less surprising.…”

Section: Comparison With Unquantized Observationssupporting

confidence: 92%

Capacity of the discrete-time AWGN channel under output quantization

Singh

Dabeer

Madhow

2008

2008 IEEE International Symposium on Information Theory

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Abstract-We investigate the limits of communication over the discrete-time Additive White Gaussian Noise (AWGN) channel, when the channel output is quantized using a small number of bits. We first provide a proof of our recent conjecture on the optimality of a discrete input distribution in this scenario. Specifically, we show that for any given output quantizer choice with K quantization bins (i.e., a precision of log 2 K bits), the input distribution, under an average power constraint, need not have any more than K + 1 mass points to achieve the channel capacity. The cutting-plane algorithm is employed to compute this capacity and to generate optimum input distributions. Numerical optimization over the choice of the quantizer is then performed (for 2-bit and 3-bit symmetric quantization), and the results we obtain show that the loss due to low-precision output quantization, which is small at low signal-to-noise ratio (SNR) as expected, can be quite acceptable even for moderate to high SNR values. For example, at SNRs up to 20 dB, 2-3 bit quantization achieves 80-90% of the capacity achievable using infinite-precision quantization.

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Section: Comparison With Unquantized Observationssupporting

confidence: 92%

Capacity of the discrete-time AWGN channel under output quantization

Singh

Dabeer

Madhow

2008

2008 IEEE International Symposium on Information Theory

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“…Prior work on the effect of reduced ADC precision on channel capacity with fixed input distribution includes [7], [8], [9]. However, other than our own results reported earlier in [10], [11], [12], we are not aware of an information-theoretic investigation with low-precision ADC that includes optimization of the input distribution.…”

Section: Introductionmentioning

confidence: 77%

“…This result is further tightened by the following theorem that shows the optimality of binary antipodal signaling for all SNRs. Theorem 2: For the 1-bit symmetric quantized channel model (9), the capacity is achieved by binary antipodal signaling and is given by…”

Section: A 1-bit Symmetric Quantization : Binary Antipodal Signalingmentioning

confidence: 99%

On the limits of communication with low-precision analog-to-digital conversion at the receiver

Singh

Dabeer

Madhow

2009

IEEE Trans. Commun.

332

183

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Abstract-As communication systems scale up in speed and bandwidth, the cost and power consumption of high-precision (e.g., 8-12 bits) analog-to-digital conversion (ADC) becomes the limiting factor in modern transceiver architectures based on digital signal processing. In this work, we explore the impact of lowering the precision of the ADC on the performance of the communication link. Specifically, we evaluate the communication limits imposed by low-precision ADC (e.g., 1-3 bits) for transmission over the real discrete-time Additive White Gaussian Noise (AWGN) channel, under an average power constraint on the input. For an ADC with K quantization bins (i.e., a precision of log 2 K bits), we show that the input distribution need not have any more than K+1 mass points to achieve the channel capacity. For 2-bin (1-bit) symmetric quantization, this result is tightened to show that binary antipodal signaling is optimum for any signal-tonoise ratio (SNR). For multi-bit quantization, a dual formulation of the channel capacity problem is used to obtain tight upper bounds on the capacity. The cutting-plane algorithm is employed to compute the capacity numerically, and the results obtained are used to make the following encouraging observations : (a) up to a moderately high SNR of 20 dB, 2-3 bit quantization results in only 10-20% reduction of spectral efficiency compared to unquantized observations, (b) standard equiprobable pulse amplitude modulated input with quantizer thresholds set to implement maximum likelihood hard decisions is asymptotically optimum at high SNR, and works well at low to moderate SNRs as well.

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“…The first one is whether lowprecision ADC can be applied in large-scale MIMO. [13] showed that the MIMO channel capacity loss due to coarse quantization was surprisingly small. This fact indicates that, at least from information theoretical point, low-precision ADC can be considered in large-scale MIMO.…”

Section: Introductionmentioning

confidence: 99%

Convex optimization based multiuser detection for uplink large-scale MIMO under low-resolution quantization

Wang

2014

2014 IEEE International Conference on Communications (ICC)

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In large-scale Multiple-Input-Multiple-Output, the Base Station (BS) is equipped with a large-size antenna array containing tens even hundreds Radio Frequency channels. If so many RF front-ends adopt the classical receivers, their cost and power consumption during the receiving mode would increase quickly. Therefore, we exploit low-resolution Analog-to-DigitalConvertor to design low-power and low-cost software defined radio receivers for the BS. However, the new problem is how the BS fulfills channel estimation and Multiuser Detection (MUD) under low-resolution quantization. In this paper, first, least square method is used for channel estimation, and a robust Maximum Likelihood (ML) MUD problem is constructed to take into account the channel estimation errors. Second, an iterative multiuser detector is constructed by relaxing the ML MUD problem as a convex optimization problem and then solving the convex problem through the nonmonotone spectral projected gradient method. Compared with the Minimum Mean Square Error (MMSE) detector, the proposed detector has lower computational complexity, and is more suitable for hardware implementation. Simulation results show that it also outperforms MMSE.

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Capacity and coding for quantized MIMO systems

Cited by 69 publications

References 5 publications

Capacity of the discrete-time AWGN channel under output quantization

Capacity of the discrete-time AWGN channel under output quantization

On the limits of communication with low-precision analog-to-digital conversion at the receiver

Convex optimization based multiuser detection for uplink large-scale MIMO under low-resolution quantization

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