Optimal Joint Channel Estimation and Data Detection for Massive SIMO Wireless Systems: A Polynomial Complexity Solution (original) (raw)
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Efficient optimal joint channel estimation and data detection for massive MIMO systems
2016 IEEE International Symposium on Information Theory (ISIT), 2016
In this paper, we propose an efficient optimal joint channel estimation and data detection algorithm for massive MIMO wireless systems. Our algorithm is optimal in terms of the generalized likelihood ratio test (GLRT). For massive MIMO systems, we show that the expected complexity of our algorithm grows polynomially in the channel coherence time. Simulation results demonstrate significant performance gains of our algorithm compared with suboptimal non-coherent detection algorithms. To the best of our knowledge, this is the first algorithm which efficiently achieves GLRT-optimal non-coherent detections for massive MIMO systems with general constellations.
2015
Massive MIMO systems can greatly increase spectral and energy efficiency over traditional MIMO systems by exploiting large antenna arrays. However, increasing the number of antennas at the base station (BS) makes the uplink non-coherent data detection very challenging in massive MIMO systems. In this paper we consider the joint maximum likelihood (ML) channel estimation and data detection problem for massive SIMO (single input multiple output) wireless systems, which is a special case of wireless systems with large antenna arrays. We propose exact ML non-coherent data detection algorithms for both constant-modulus and nonconstant-modulus constellations, with a low expected complexity. Despite the large number of unknown channel coefficients for massive SIMO systems, we show that the expected computational complexity of these algorithms is linear in the number of receive antennas and polynomial in channel coherence time. Simulation results show the performance gains (up to 5 dB impro...
2015 IEEE Signal Processing and Signal Processing Education Workshop (SP/SPE), 2015
Massive MIMO systems have made significant progress in increasing spectral and energy efficiency over traditional MIMO systems by exploiting large antenna arrays. In this paper we consider the joint maximum likelihood (ML) channel estimation and data detection problem for massive SIMO (single input multiple output) wireless systems. Despite the large number of unknown channel coefficients for massive SIMO systems, we improve an algorithm to achieve the exact ML non-coherent data detection with a low expected complexity. We show that the expected computational complexity of this algorithm is linear in the number of receive antennas and polynomial in channel coherence time. Simulation results show the performance gain of the optimal non-coherent data detection with a low computational complexity.
IEEE Access, 2019
Exploiting massive multiple-input-multiple-output (MIMO) gains come at the expense of obtaining accurate channel estimates at the base station. However, conventional channel estimation techniques do not scale well with increasing number of antennas and incur an unacceptably large training overhead in many applications. This calls for training designs and channel estimation techniques that efficiently exploit the physical properties of the massive MIMO channel as captured by sophisticated system/channel models. In this paper, we present designs that exploit the sparsity of the angle and delay domain representation of the massive MIMO channel as well as the low-rank property of the channel covariance, while also providing the connection between the sparse angle-delay representation and low-rank covariance property. Numerous multiuser scenarios are investigated including uplink, downlink, and singleand multi-cell communications, with the designs aiming at minimizing the channel estimation error or maximizing achievable rates with reduced training overhead. Theoretical analysis and numerical performance results indicate significant reduction of training overhead over conventional techniques while achieving similar performance. The presented methods demonstrate the importance of exploiting fundamental channel properties and reveal important insights on the interplay/tradeoff between training overhead and performance that can serve as guidelines for the design of future massive MIMO communication systems. INDEX TERMS Channel sparsity, correlated fading, channel estimation, training design, compressive sensing, pilot contamination, performance bounds, massive MIMO.
Detection and Estimation Algorithms in Massive MIMO Systems
This book chapter reviews signal detection and parameter estimation techniques for multiuser multiple-antenna wireless systems with a very large number of antennas, known as massive multi-input multi-output (MIMO) systems. We consider both centralized antenna systems (CAS) and distributed antenna systems (DAS) architectures in which a large number of antenna elements are employed and focus on the uplink of a mobile cellular system. In particular, we focus on receive processing techniques that include signal detection and parameter estimation problems and discuss the specific needs of massive MIMO systems. Simulation results illustrate the performance of detection and estimation algorithms under several scenarios of interest. Key problems are discussed and future trends in massive MIMO systems are pointed out.
International Journal of Engineering Research and Technology (IJERT), 2021
https://www.ijert.org/a-comparative-study-of-different-channel-estimation-technique-for-a-massive-mimo-communication-system https://www.ijert.org/research/a-comparative-study-of-different-channel-estimation-technique-for-a-massive-mimo-communication-system-IJERTCONV6IS13139.pdf The emerging fifth generation (5G) wireless communication system raises new requirements on spectral efficiency and energy efficiency. A massive multiple-input multiple-output (MIMO) system, equipped with tens or even hundreds of antennas, is capable of providing significant improvements to spectral efficiency, energy efficiency, and robustness of the system. For the design, performance evaluation, and optimization of massive MIMO wireless communication systems, realistic channel models are indispensable. Also, we compare channel characteristics of massive MIMO channel models, such as beam forming, capacity with respect to SNR. The upcoming data traffic growth created by the evolution of smart phones, tablet computers and Machine to Machine (M2M) communication outstrips the capacity increase of wireless communications networks. As a powerful countermeasure, in the case of full-rank channel matrices, MIMO techniques are potentially capable of linearly increasing the capacity or decreasing the transmit power upon increasing the number of antennas. Hence, the recent concept of large-scale MIMO (LS-MIMO) systems has attracted substantial research attention and been regarded as a promising technique for next-generation wireless communications networks. This paper considers pilot-based channel estimation in large-scale multiple-input multiple-output (MIMO) communication systems. Motivated by the fact that computational complexity is one of the main challenges in such systems, we tried to reduce the complexity of the system using different algorithms. The coefficients of the polynomial are optimized to minimize the mean square error (MSE) of the estimate. Finally we discussed the need of LS-MIMO with some of advantages and showed the difference between the capacity range of MIMO and Massive MIMO. By analyzing the results we can observe the changes that had been obtained and can decide the perfect algorithm for channel estimation which gives rise to a good communication system. Keywords-Large Scale MIMO (LS-MIMO), Co-channel interference (CCI), large-scale/massive MIMO, MIMO detection, Bandwidth efficiency (BE), Energy efficiency (EE), Self Interference Cancellation (SIC), Zero Forcing (ZF), Matched filter (MF), Machine to Machine (M2M) communication. I. INTRODUCTION MIMO techniques can bring huge improvements in spectral efficiency to wireless systems, by increasing the spatial reuse through spatial multiplexing [2]. While 8×8 MIMO transmissions have found its way into recent communication standards, such as LTE-Advanced [3], there is an increasing interest from academy and industry to equip base stations (BSs) with much larger arrays with several hundreds of antenna elements [4]-[9]. Such large-scale MIMO, or "massive MIMO", techniques can give unprecedented spatial resolution and array gain, thus enabling a very dense spatial reuse that potentially can keep up with the rapidly increasing demand for wireless connectivity and need for high energy efficiency. Massive MIMO is an emerging technology that scales up MIMO by possibly orders of magnitude compared to current state-of-the-art. With massive MIMO, we think of systems that use antenna arrays with a few hundred antennas, simultaneously serving many tens of terminals in the same time-frequency resource. The basic premise behind massive MIMO is to reap all the benefits of conventional MIMO, but on a much greater scale. Overall, massive MIMO is an enabler for the development of future broadband (fixed and mobile) networks which will be energy-efficient, secure, and robust, and will use the spectrum efficiently. As such, it is an enabler for the future digital society infrastructure that will connect the Internet of people, Internet of things, with clouds and other network infrastructure. Many different configurations and deployment scenarios for the actual antenna arrays used by a massive MIMO system can be envisioned [6]. Massive MIMO relies on spatial multiplexing that in turn relies on the base station having good enough channel knowledge, both on the uplink and the downlink. On the uplink, this is easy to accomplish by having the terminals send pilots, based on which the base station estimates the channel responses to each of the terminals. The downlink is more difficult. In conventional MIMO systems, like the LTE standard, the base station sends out pilot waveforms based on which the terminals estimate the channel responses, quantize the so-obtained estimates and feed them back to the base station. This will not be feasible in massive MIMO systems, at least not when operating in a high-mobility environment, for two reasons. First, optimal downlink pilots should be mutually orthogonal between the antennas. This means that the amount of time frequency resources needed for downlink pilots scales as the number of antennas, so a massive MIMO system would require up to a hundred times more such
International Journal of Engineering Research and Technology (IJERT), 2019
https://www.ijert.org/A-Comparative-Study-of-Different-Channel-Estimation-Technique-for-A-Massive-MIMO-Communication-System https://www.ijert.org/research/A-Comparative-Study-of-Different-Channel-Estimation-Technique-for-A-Massive-MIMO-Communication-System-IJERTCONV7IS08085.pdf The emerging fifth generation (5G) wireless communication system raises new requirements on spectral efficiency and energy efficiency. A massive multiple-input multiple-output (MIMO) system, equipped with tens or even hundreds of antennas, is capable of providing significant improvements to spectral efficiency, energy efficiency, and robustness of the system. For the design, performance evaluation, and optimization of massive MIMO wireless communication systems, realistic channel models are indispensable. Also, we compare channel characteristics of massive MIMO channel models, such as beam forming, capacity with respect to SNR. The upcoming data traffic growth created by the evolution of smart phones, tablet computers and Machine to Machine (M2M) communication outstrips the capacity increase of wireless communications networks. As a powerful countermeasure, in the case of full-rank channel matrices, MIMO techniques are potentially capable of linearly increasing the capacity or decreasing the transmit power upon increasing the number of antennas. Hence, the recent concept of large-scale MIMO (LS-MIMO) systems has attracted substantial research attention and been regarded as a promising technique for next-generation wireless communications networks. This paper considers pilot-based channel estimation in large-scale multiple-input multiple-output (MIMO) communication systems. Motivated by the fact that computational complexity is one of the main challenges in such systems, we tried to reduce the complexity of the system using different algorithms. The coefficients of the polynomial are optimized to minimize the mean square error (MSE) of the estimate. Finally we discussed the need of LS-MIMO with some of advantages and showed the difference between the capacity range of MIMO and Massive MIMO. By analyzing the results we can observe the changes that had been obtained and can decide the perfect algorithm for channel estimation which gives rise to a good communication system.
Parametric Channel Estimation for Massive MIMO
2018 IEEE Statistical Signal Processing Workshop (SSP), 2018
Channel state information is crucial to achieving the capacity of multiantenna (MIMO) wireless communication systems. It requires estimating the channel matrix. This estimation task is studied, considering a sparse physical channel model, as well as a general measurement model taking into account hybrid architectures. The contribution is twofold. First, the Cramér-Rao bound in this context is derived. Second, interpretation of the Fisher Information Matrix structure allows to assess the role of system parameters, as well as to propose asymptotically optimal and computationally efficient estimation algorithms.
A Low-complexity near-ML performance achieving algorithm for large MIMO detection
2008
In this paper, we present a low-complexity, near maximum-likelihood (ML) performance achieving detector for large MIMO systems having tens of transmit and receive antennas. Such large MIMO systems are of interest because of the high spectral efficiencies possible in such systems. The proposed detection algorithm, termed as multistage likelihood-ascent search (M-LAS) algorithm, is rooted in Hopfield neural networks, and is shown to possess excellent performance as well as complexity attributes. In terms of performance, in a 64 × 64 V-BLAST system with 4-QAM, the proposed algorithm achieves an uncoded BER of 10 −3 at an SNR of just about 1 dB away from AWGN-only SISO performance given by Q( √ SNR). In terms of coded BER, with a rate-3/4 turbo code at a spectral efficiency of 96 bps/Hz the algorithm performs close to within about 4.5 dB from theoretical capacity, which is remarkable in terms of both high spectral efficiency as well as nearness to theoretical capacity. Our simulation results show that the above performance is achieved with a complexity of just O(NtNr) per symbol, where Nt and Nr denote the number of transmit and receive antennas.