Downlink Performance of Superimposed Pilots in Massive MIMO Systems (original) (raw)

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