جستجوی مقالات مرتبط با کلیدواژه "channel estimation" در نشریات گروه "برق"

To overcome the problem of channel estimation in massive multiple-input multiple-output (M-MIMO) systems, in this paper we propose a downlink link channel estimation scheme in frequency-division duplex (FDD) based on structured compressive sensing to reduce the pilot required by which Intrinsic spatial sparsity of M-MIMO delay channels are amplified. For this purpose, first, after discussing the different methods of channel estimation and examining the existing challenges, we define our roadmap and propose our algorithm, in which we estimate the channel based on the greedy orthogonal matching pursuit (OMP) algorithm. In this algorithm, spatial correlation between the channel impulse response of different transmitter antennas is used for accurate channel estimation. This algorithm obtains the channel sparsity in an adaptive way, which negates the ideal assumption of the previous works that the channel sparsity is in hand. In this case, this algorithm estimates the channel with good accuracy in cases when the exact amount of channel sparsity is not known. Finally, we present simulations that demonstrate the ability of this method to reduce the required pilot. The simulations show that the proposed channel estimation reliably obtains the channel sparsity level and the support set compared to similar methods.

In this paper, uplink of single-cell massive multiple-input multiple-output (mMIMO) system with receiver hardware impairment is considered. Users have limited energy budget in each coherence interval which should be allocated optimally for uplink pilot and data symbol transmission. Also, we assume that for both channel estimation and data transmission phases receiver suffers from hardware impairments. Hence, using the received pilots the estimates of the channels are obtained at the receiver then by use of these estimates and maximum ratio combining (MRC) technique achievable rate of the users are derived for different values of the transmitted pilot and data powers. Next, a fairness optimization problem is proposed so as to maximize the minimum rate of the users by considering energy budget constraint in a coherence interval. The original optimization problem is a nonconvex one. However, we reformulate it such that it can be solved efficiently using geometric programming. Finally, numerical results are provided which signify the considerable improvements by jointly allocating pilot and data powers in comparison with simple equal power allocation or only optimizing the data power.

Massive MIMO system is one of the advanced and attractive technologies for fifth generation of wireless networks. In such systems, base stations employ a very large number of antennas (Hundreds or even thousands of antennas). As a result, in these systems, capacity, spectral efficiency and energy efficiency increases in comparison with conventional MIMO systems. In the channel estimation, interference, which is produced by reuse of the same pilot sequences by different users of neighboring cells, is the most important factor in the performance limits. This phenomenon is referred to as pilot contamination. In this paper, pilot contamination is described and then the research works presented by the researchers on this topic has summarized. We categorize the different proposed mitigation techniques for pilot contamination using the following taxonomy: protocol-based methods, precoding methods, AOA-based methods, blind methods and smart pilot assignment methods. Finally, we propose a method based on smart pilot allocation, to reduce pilot contamination. Simulation results show that both uplink rate of user’s increases based on the proposed method and the accuracy of channel estimation improved greatly than exciting methods. Then we propose a method based on pilot precoding and protocol based allocation to reducing pilot contamination effects. In addition, we study a range of challenges and future research topics.