Nonlinear Channel Estimation Error Effect on Capacity of MIMO System

This paper presents the effect of nonlinearity and the effect of estimation error on the channel capacity in MIMO system. We consider a nonlinear MIMO channel, and compare the capacity of Rayleigh MIMO channel model with estimation error with the nonlinear model, at different estimation errors. We consider the estimation error as Gaussian distribution. The simulation results show that the channel capacity of linear and nonlinear MIMO channels are sensitive to the channel estimation error, and due to the nonlinearity, the capacity is less than linear channel. Keywords: MIMO, MIMO Modeling, channel capacity, channel estimation error, nonlinear MIMO channel.

Characteristics and Channel Capacity Studies of a Novel 6G Non-Stationary Massive MIMO Channel Model Considering Mutual Coupling

In the sixth generation (6G) wireless communicationnetworks, ultra-massive multiple-input multiple-output (MIMO)communication is one of the most promising technologies. Inultra-massive MIMO channels, the mutual coupling (MC) effectis more obvious when antenna elements are more closely spaced.In this paper, a novel 6G space-time-frequency (STF) nonstationarymassive MIMO channel model is proposed, whichjointly considers MC, antenna efficiency, and near-field steeringvectors of different antenna topologies. As the Shannon capacitytheorem is based on the wide-sense stationary (WSS) channelassumption and cannot be applied to non-stationary channels,we propose a novel non-stationary channel capacity calculationmethod that divides the non-stationary channel into WSS subchannels. Important statistical properties and channel capacities of the proposed channel model are derived and verified by ultra-massive MIMO channel measurements and data postprocessing. The results show that the simulated spatial crosscorrelationfunction (CCF) and channel capacity considering MC and antenna efficiency are closer to measured results. It also shows that antenna topologies have an impact on channel capacities. Furthermore, channel capacities using the proposednovel calculation method match the measured channel capacities in non-stationary channels

Modified Spatial Channel Model for MIMO Wireless Systems

?The third generation partnership Project's (3GPP) spatial channel model (SCM) is a stochastic channel model for MIMO systems. Due to fixed subpath power levels and angular directions, the SCM model does not show the degree of variation which is encountered in real channels. In this paper, we propose a modified SCM model which has random subpath powers and directions and still produces Laplace shape angular power spectrum. Simulation results on outage MIMO capacity with basic and modified SCM models show that the modified SCM model gives constantly smaller capacity values. Accordingly, it seems that the basic SCM gives too small correlation between MIMO antennas. Moreover, the variance in capacity values is larger using the proposed SCM model. Simulation results were supported by the outage capacity results from a measurement campaign conducted in the city centre of Oulu, Finland

Distributed MIMO Systems with Oblivious Antennas

A scenario in which a single source communicates with a single destination via a distributed MIMO transceiver is considered. The source operates each of the transmit antennas via finite-capacity links, and likewise the destination is connected to the receiving antennas through capacity-constrained channels. Targeting a nomadic communication scenario, in which the distributed MIMO transceiver is designed to serve different standards or services, transmitters and receivers are assumed to be oblivious to the encoding functions shared by source and destination. Adopting a Gaussian symmetric interference network as the channel model (as for regularly placed transmitters and receivers), achievable rates are investigated and compared with an upper bound. It is concluded that in certain asymptotic and non-asymptotic regimes obliviousness of transmitters and receivers does not cause any loss of optimality.Comment: In Proc. of the 2008 IEEE International Symposium on Information Theory (ISIT 2008), Toronto, Ontario, Canad

Performance Enhancement Using NOMA-MIMO for 5G Networks

The integration of MIMO and NOMA technologies addresses key challenges in 5G and beyond, such as connectivity, latency, and dependability. However, resolving these issues, especially in MIMO-enabled 5G networks, required additional research. This involved optimizing parameters like bit error rate, downlink spectrum efficiency, average capacity rate, and uplink transmission outage probability. The model employed Quadrature Phase Shift Keying modulation on selected frequency channels, accommodating diverse user characteristics. Evaluation showed that MIMO-NOMA significantly improved bit error rate and transmitting power for the best user in download transmission. For uplink transmission, there was an increase in the average capacity rate and a decrease in outage probability for the best user. Closed-form formulas for various parameters in both downlink and uplink NOMA, with and without MIMO, were derived. Overall, adopting MIMO-NOMA led to a remarkable performance improvement for all users, even in challenging conditions like interference or fading channels

System level analysis of noise and interference analysis for a MIMO system

Journal ArticleMultiple input multiple output antenna communication system are gaining importance in the field of communication and ad-hoc networks due to increase demand for wireless throughput in band-limited channels. A system analysis is not complete without accounting for the system level noise and interference. This abstract provides a simple system level model including noise and interference to enable detailed analysis of MIMO system. Multiple-input-multiple-output systems have been studied for more than a decade, since Froschini's landmark conclusion that the theoretical information capacity increases linearly with the number of antennas for rich multipath channels [1]. Experimental characterization and model development have shown that the ideal linear capacity increase is not achievable in practice due to a number of factors including correlation of the communication channel, close antenna spacing and subsequent mutual coupling [2-4]. The capacity also depends on the type of channel state information (CSI) available. A popular expression for MIMO capacity is: [3