1,129 research outputs found
Task-Based Quantization for Massive MIMO Channel Estimation
Massive multiple-input multiple-output (MIMO) systems are
the focus of increasing research attention. In such setups, there is an
urgent need to utilize simple low-resolution quantizers, due to power
and memory constraints. In this work we study massive MIMO channel
estimation with quantized measurements, when the quantization system
is designed to minimize the channel estimation error, as opposed to
the quantization distortion. We first consider vector quantization, and
characterize the minimal error achievable. Next, we focus on practical
systems utilizing scalar uniform quantizers, and design the analog
and digital processing as well as the quantization dynamic range to
optimize the channel estimation accuracy. Our results demonstrate that
the resulting massive MIMO system which utilizes low-resolution scalar
quantizers can approach the minimal estimation error dictated by ratedistortion theory, achievable using vector quantizers
Asymptotic Task-Based Quantization with Application to Massive MIMO
Quantizers take part in nearly every digital signal processing system which
operates on physical signals. They are commonly designed to accurately
represent the underlying signal, regardless of the specific task to be
performed on the quantized data. In systems working with high-dimensional
signals, such as massive multiple-input multiple-output (MIMO) systems, it is
beneficial to utilize low-resolution quantizers, due to cost, power, and memory
constraints. In this work we study quantization of high-dimensional inputs,
aiming at improving performance under resolution constraints by accounting for
the system task in the quantizers design. We focus on the task of recovering a
desired signal statistically related to the high-dimensional input, and analyze
two quantization approaches: We first consider vector quantization, which is
typically computationally infeasible, and characterize the optimal performance
achievable with this approach. Next, we focus on practical systems which
utilize hardware-limited scalar uniform analog-to-digital converters (ADCs),
and design a task-based quantizer under this model. The resulting system
accounts for the task by linearly combining the observed signal into a lower
dimension prior to quantization. We then apply our proposed technique to
channel estimation in massive MIMO networks. Our results demonstrate that a
system utilizing low-resolution scalar ADCs can approach the optimal channel
estimation performance by properly accounting for the task in the system
design
Channel Estimation and Symbol Detection In Massive MIMO Systems Using Expectation Propagation
The advantages envisioned from using large antenna arrays have made massive multiple- input multiple-output systems (also known as massive MIMO) a promising technology for future wireless standards. Despite the advantages that massive MIMO systems provide, increasing the number of antennas introduces new technical challenges that need to be resolved. In particular, symbol detection is one of the key challenges in massive MIMO. Obtaining accurate channel state information (CSI) for the extremely large number of chan- nels involved is a difficult task and consumes significant resources. Therefore for Massive MIMO systems coherent detectors must be able to cope with highly imperfect CSI. More importantly, non-coherent schemes which do not rely on CSI for symbol detection become very attractive. Expectation propagation (EP) has been recently proposed as a low complexity algo- rithm for symbol detection in massive MIMO systems , where its performance is evaluated on the premise that perfect channel state information (CSI) is available at the receiver. However, in practical systems, exact CSI is not available due to a variety of reasons in- cluding channel estimation errors, quantization errors and aging. In this work we study the performance of EP in the presence of imperfect CSI due to channel estimation er- rors and show that in this case the EP detector experiences significant performance loss. Moreover, the EP detector shows a higher sensitivity to channel estimation errors in the high signal-to-noise ratio (SNR) regions where the rate of its performance improvement decreases. We investigate this behavior of the EP detector and propose a Modified EP detector for colored noise which utilizes the correlation matrix of the channel estimation error. Simulation results verify that the modified algorithm is robust against imperfect CSI and its performance is significantly improved over the EP algorithm, particularly in the higher SNR regions, and that for the modified detector, the slope of the symbol error rate (SER) vs. SNR plots are similar to the case of perfect CSI. Next, an algorithm based on expectation propagation is proposed for noncoherent symbol detection in large-scale SIMO systems. It is verified through simulation that in terms of SER, the proposed detector outperforms the pilotbased coherent MMSE detector for blocks as small as two symbols. This makes the proposed detector suitable for fast fading channels with very short coherence times. In addition, the SER performance of this detec- tor converges to that of the optimum ML receiver when the size of the blocks increases. Finally it is shown that for Rician fading channels, knowledge of the fading parameters is not required for achieving the SER gains. A channel estimation method was recently proposed for multi-cell massive MIMO sys- tems based on the eigenvalue decomposition of the correlation matrix of the received vectors (EVD-based). This algorithm, however, is sensitive to the size of the antenna array as well as the number of samples used in the evaluation of the correlation matrix. As the final work in this dissertation, we present a noncoherent channel estimation and symbol de- tection scheme for multi-cell massive MIMO systems based on expectation propagation. The proposed algorithm is initialized with the channel estimation result from the EVD- based method. Simulation results show that after a few iterations, the EP-based algorithm significantly outperforms the EVD-based method in both channel estimation and symbol error rate. Moreover, the EP-based algorithm is not sensitive to antenna array size or the inaccuracies of sample correlation matrix
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