Channel capacity analysis of non-orthogonal multiple access and massive multiple-input multiple-output wireless communication networks considering perfect and imperfect channel state information

Abstract

In this paper, we investigate the non-orthogonal multiple access (NOMA) and massive multiple-input multiple-output (M-MIMO) techniques and through simulation, and a comparison is given between the NOMA and orthogonal multiple access techniques. Integrating NOMA with M-MIMO is a very challenging task. In this paper, for a single-cell system, NOMA is integrated with a M-MIMO system for better spectral and energy efficiency. Investigation of the multiple user gain is the focus of this work because the multiple user gain supports simultaneous transmission of multiple users in the case of the M-MIMO system. In this way, the M-MIMO will provide a 100 times channel capacity increase, which results in very high data transmission rate. In the modern communication system, achieving multiple user gain is a very difficult task when channel estimation error is present. The performance of the orthogonal multiple access as well as NOMA system significantly reduced in the presence of channel estimation error. However, most of the current schemes do not work well with imperfect perfect channel state information conditions. Simulation results closely agree with the theoretical outcomes.

Performance evaluation of unmanned aerial vehicle communication by increasing antennas of cellular base stations

“…Therefore, exact channel capacity may not be computed. Instead, capacity lower bound can be computed [22], [23]. Capacity lower bound is: , 𝜌 is the power and |𝐸{𝑔|𝛺}| 2 denotes the absolute value square of channel.…”

Investigation of the fifth generation non-orthogonal multiple access technique for defense applications using deep learning

“…However, in a practical scenario, the cancellation is not perfect and the conventional SIC scheme is not the preferred method for practical conditions, especially with severe fading channel conditions. [24][25][26][27] Also, in the scenario when the signal detection of the signal is perfect through the SIC scheme, due to various fading channel imperfectness, the decoded signal is not perfectly matched with the original signal. 27 It is given in Xia et al 28 that the imperfect decoding of the signal using the SIC yields degradation of the system performance.…”

Examination of the multiple-input multiple-output space-time block-code selective decode and forward relaying protocol over non-homogeneous fading channel conditions

“…The diversity order (DO) for a dual-phase and multi-phase cooperative network is derived, and simulation results demonstrate that the STBC-based S-DF protocol outperforms the AF-based network. However, the studies [4][5][6][7][8][9][10][11] only consider the Rayleigh and Nakagami-m fading channel conditions. However, in realistic scenarios, the fading channel is generally non-homogeneous, and previous research on the S-DF protocol has been restricted to traditional frequency flat fading channels.…”

“…Cooperative communication methods are considered as a practical strategy for expanding network coverage while maintaining a high data rate in 5G and beyond 5G wireless communication networks. 6 In a relaying network, the relay node receives the signal from the source node, manipulates it by performing simple or sophisticated and advanced signal processing operations depending on the type of the relay node, and then relays or passes this signal to the destination node. Amplify-and-forward (AF), decode-and-forward (DF), and compress-and-forward (CAF) are the three most prevalent types of cooperative communication networks.…”