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Recently, there has been an evolution of mobile networks towards the fourth generation radio wireless communications (4G) as LTE (Long Term Evolution). In this paper, the feasibility of using LTE-based passive radar is investigated to take advantage of using LTE signal as illuminator of opportunity for moving object monitoring. An analysis of ambiguity function is done on a typical LTE waveform to assess the Doppler and range characteristics. The initial results and analysis show that LTE signal range and Doppler resolutions of 7.5m and 0.11m/s can be achieved, respectively.

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Channel quality indicator (CQI) feedback in long-term evolution (LTE) system is an essential technique in describing the instantaneous channel state information. The CQI calculations highly depend on the accuracy of the channel estimation process in order to assign appropriate modulation and coding scheme. However, one of the critical issues affecting the LTE system performance is obtaining the CQI for each transmission period which will inevitably cost many resources. Therefore, an appropriate method for reducing CQI feedback overhead along with accurate channel estimation technique is required to manage the allocated resources and obtains significant improvements in system performance. In this paper, an adaptive threshold feedback compression scheme based on CQI scheme is proposed to obtain better system performance in terms of system throughput and error rate in LTE system. This proposed adaptive scheme dynamically adapts its threshold level to the signal to noise ratio variations, thus increasing the throughput and reducing the CQI feedback overhead. Results show that the proposed CQI based adaptive threshold feedback compression scheme enhances the tradeoff between system throughput and block error rate.

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The fast fading channel produced by the fast user mobility requires a powerful channel estimation to report the most accurate channel status. Such estimation techniques are usualy suffer from large number of computational processes, and thus, their complexity needs to be minimized. However, the calculation reliability of channel coefficients depends mainly on the accuracy of the channel estimation model. Therefore, obtaining a joint optimized solution for channel estimation error, feedback overhead, and complexity is very crucial. In this paper, two different channel estimation schemes;

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