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# Network MIMO with Partial Cooperation between Radar and Cellular Systems

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### Abstract

To meet the growing spectrum demands, future cellular systems are expected to share the spectrum of other services such as radar. In this paper, we consider a network multiple-input multiple-output (MIMO) with partial cooperation model where radar stations cooperate with cellular base stations (BS)s to deliver messages to intended mobile users. So the radar stations act as BSs in the cellular system. However, due to the high power transmitted by radar stations for detection of far targets, the cellular receivers could burnout when receiving these high radar powers. Therefore, we propose a new projection method called small singular values space projection (SSVSP) to mitigate these harmful high power and enable radar stations to collaborate with cellular base stations. In addition, we formulate the problem into a MIMO interference channel with general constraints (MIMO-IFC-GC). Finally, we provide a solution to minimize the weighted sum mean square error minimization problem (WSMMSE) with enforcing power constraints on both radar and cellular stations.

### Most cited references4

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### Target Detection Performance of Spectrum Sharing MIMO Radars

,  ,   (2014)
Future wireless communication systems are envisioned to share radio frequency (RF) spectrum, with other services such as radars, in order to meet the growing spectrum demands. In this paper, we consider co-channel spectrum sharing between cellular systems and radars. We address the problem of target detection by radars that are subject to shape its waveform in a way that it does not cause interference to cellular systems. We consider a multiple-input multiple-output (MIMO) radar and a MIMO cellular communication system with $$\mc K$$ base stations (BS). We propose a spectrum sharing algorithm which steers radar nulls, by projecting radar waveform onto the null space of interference channel, towards a `selected' BS, thus, protecting it from radar interference. This BS is selected, among $$\mc K$$ BSs, on the basis of guaranteeing minimum waveform degradation. We study target detection capabilities of this null-space projected (NSP) waveform and compare it with the orthogonal waveform. We derive the generalized likelihood ratio test (GLRT) for target detection and derive detector statistic for NSP and orthogonal waveform. The target detection performance for NSP and orthogonal waveform is studied theoretically and via Monte Carlo simulations.
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### On The Impact of Time-Varying Interference-Channel on the Spatial Approach of Spectrum Sharing between S-band Radar and Communication System

,  ,   (2014)
Spectrum sharing is a new approach to solve the congestion problem in the RF spectrum. A spatial approach for spectrum sharing between a radar and a communication system was proposed, which mitigates the radar interference to communication by projecting the radar waveform onto the null space of the interference channel, between radar and communication system [1]. In this work, we extend this approach to a maritime MIMO radar which experiences a time varying interference channel due to the oscillatory motion of a ship, because of the breaking of sea/ocean waves. We model this variation by using the matrix perturbation theory and the statistical distribution of the breaking waves. This model is then used to study the impact of perturbed interference channel on the spatial approach of spectrum sharing. We use the maximum likelihood (ML) estimate of a target's angle of arrival to study the radar's performance when its waveform is projected onto the null space of the perturbed interference channel. Through our analytical and simulation results, we study the loss in the radar's performance due to the null space projection (NSP) of its waveform on the perturbed interference channel.
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### Beampattern analysis for MIMO radar and telecommunication system coexistence

(2014)
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### Author and article information

###### Journal
2015-07-26
2015-10-18
1507.07267

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cs.IT math.IT

Numerical methods, Information systems & theory