Analysis and design of multi-element antenna systems in mobile fading channels require a model for the space-time cross-correlation among the links of the underlying multipleinput multiple-output (MIMO) Mobile-to-Mobile (M-to-M) communication channel. In this paper, we propose a MIMO channel reference model for M-to-M communication systems that we call it as the modified geometrical two-ring model. This model is based on the extension of single-bounce two-ring scattering model for flat fading channel under the assumption that the transmitter and the receiver are moving. Assuming an isotropic and singlebounce scattering model, a closed-form expression for the spacetime cross-correlation function (CCF) between any two sub channels is derived. The proposed model provides an important framework in M-to-M system design, where includes many existing correlation models as special cases. Some numerical results are presented as special cases of the derived CCF.
Analysis and design of multielement antenna systems in mobile fading channels require a model for the space-time cross-correlation among the links of the underlying multipleinput multiple-output (MIMO) Mobile-to-Mobile (M-to-M) communication channels. In this paper, we propose the modified geometrical two-ring model, a MIMO channel reference model for M-to-M communication systems. This model is based on the extension of single-bounce two-ring scattering model for flat fading channel under the assumption that the transmitter and the receiver are moving. Assuming single-bounce scattering model in both isotropic and nonisotropic environment, a closed-form expression for the space-time cross-correlation function (CCF) between any two subchannels is derived. The proposed model provides an important framework in M-to-M system design, where includes many existing correlation models as special cases. Also, two realizable statistical simulation models are proposed for simulating both isotropic and nonisotropic reference model. The realizable simulation models are based onSum-of-Sinusoids (SoS)simulation model. Finally, the correctness of the proposed simulation models is shown via different simulation scenarios.
A set-membership (SM) normalized least-meansquare (NLMS) (SMNLMS) algorithm is developed using SM theory in the class of optimal bounding ellipsoid (OBE) algorithms. This signed version of NLMS algorithm requires a priori knowledge of a bound for the error magnitude, which is unknown in most applications. A very simple algorithm is proposed for the case in which the unknown magnitude of the measurement noise is slowly time-varying. The proposed algorithm is able to extract the noise magnitude information and exploit this magnitude to enhance or accelerate the learning process without risk of overbounding or performance loss due to underbounding. The performance of the proposed algorithm is compared with that of SMNLMS using some simulation examples.
In this study, the authors examine resource allocation in an orthogonal frequency-division multiple-access-based cognitive radio (CR) network which dynamically senses primary users (PUs) spectrum and opportunistically uses available channels. The aim is resource allocation such that the CR network throughput is maximised under the PUs maximum interference constraint and cognitive users (CUs) transmission power budget. This problem is formulated as a mixedinteger non-linear programming problem which is NP-hard in general and infeasible to solve in real-time. To reduce the computational complexity, the authors decouple the problem into two separate steps. After initial power allocation, in the first step, an adaptive algorithm is employed to assign subcarriers to the CUs toward throughput maximisation by using these initial powers. In the second step, power is allocated optimally to the assigned subcarriers. Simulation results show that the proposed method nearly achieves the optimal solution in a small number of iterations meaning significant reduction in the computational complexity.
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