Abstract-With the technological growth of broadband wireless technology like CDMA, OFDM and MIMO, a lots of development efforts towards wireless communication system and imaging radar system are well justified. It has been recently shown that multipleinput multiple-output (MIMO) antenna systems have the potential to dramatically improve the performance of communication systems over single antenna systems. Efforts are also being imparted towards a Convergence Technology. The convergence between a communication and radar technology which will result in ITS (Intelligent Transport System) and other applications. This motivates development of the present article. This is an effort in the direction to utilize or converge the communication technologies towards radar and to achieve the interference and clutter free quality remote images of targets.
Intelligent Transport system (ITS) deals with the remote sensing as well as access to the internet and multimedia on the move. There is a need for a system that supports both remote sensing and communication aspects with simple and reconfigurable hardware.In the present work MATLAB/SIMULINK based modeling of such a system is done based on DSSS. Hardware realization of this model is achieved through the SignalWAVE software defined radio (SDR). From the simulation results two empirical formulae are developed for target detection.
Summary
Massive multiple‐input multiple‐output (MIMO) systems improve spectral efficiency and link reliability. Linear minimum mean‐squared error (MMSE) detectors can achieve optimal performance in massive MIMO detection but require large dimension matrix inversion, which is computationally intensive. Therefore, low complexity iterative detection schemes are proposed in the literature as an alternative to the exact MMSE method. However, the performance of these schemes is greatly influenced by the choice of the initial solution. Therefore, to improve the detection performance in this paper, we proposed three hybrid detection schemes, which are Newton–Schultz–Richardson (NS‐RI), Newton–Schultz–Chebyshev (NS‐Cheby), and Newton–Schultz–Gauss–Seidel (NS‐GS). The proposed hybrid schemes show significant performance improvement and a higher convergence rate compared to their original counterpart. The performance of the proposed detectors is further improved by the likelihood ascent search (LAS) stage, which corrects the detected symbols obtained from iterative MMSE methods through a neighborhood search. However, the complexity of the LAS algorithm primarily depends on the initialization step. In this work, we introduce an efficient Gram matrix computation in the real domain. Additionally, we have applied a band approximation of the Gram matrix for the LAS initialization, which reduces the order of computational complexity of the Gram matrix from
Ofalse(NT2NRfalse) to O(ωNTNR) where ω < <2NT.
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