Abstract-This paper will design non-linear frequency modulation (NLFM) signal for Chebyshev, Kaiser, Taylor, and raised-cosine power spectral densities (PSDs). Then, the variation of peak sidelobe level with regard to mainlobe width for these four different window functions are analyzed. It has been demonstrated that reduction of sidelobe level in NLFM signal can lead to increase in mainlobe width of autocorrelation function. Furthermore, the results of power spectral density obtained from the simulation and the desired PSD are compared. Finally, error percentage between simulated PSD and desired PSD for different peak sidelobe level are illustrated. The stationary phase concept is the possible source for this error.
A compact multiple-input-multiple-output (MIMO) antenna with very high isolation is proposed for ultrawideband (UWB) applications. The antenna with a compact size of 30.1 × 20.5 mm 2 (0.31λ 0 × 0.21λ 0) consists of two planar-monopole antenna elements. It is found that isolation of more than 25 dB can be achieved between two parallel monopole antenna elements. For the lowfrequency isolation, an efficient technique of bending the feed-line and applying a new protruded ground is introduced. To increase isolation, a design based on suppressing surface wave, near-field, and far-field coupling is applied. The simulation and measurement results of the proposed antenna with the good agreement are presented and show a bandwidth with S 11 ≤ −10 dB, S 12 ≤ −25 dB ranged from 3.1 to 10.6 GHz making the proposed antenna a good candidate for UWB MIMO systems. 1. INTRODUCTION MIMO technology has aroused interest because of its application in 4G, RFID, Digital Home, and WLAN. Demand for high data rate and, as a result, huge bandwidth is increasing. In 2002 US-FCC approved unlicensed use of 3.1-10.6 GHz frequency band at low energy level [1]. Therefore in order to improve the capacity of the system, UWB MIMO antenna has been developed for commercial systems. UWB MIMO antenna with high isolation has application in short-range high-data-rate, transmission automotive communications, and radar imaging systems [2, 3]. When several antennas are in close proximity, they suffer from severe mutual coupling, which results in lower antenna efficiency and loss of bandwidth, and further degrades the performance of either diversity gain or spatial multiplexing schemes [3]. So the question then arises as to how to put together antenna elements with low coupling and occupying the least possible space. Because these two properties contradict each other, the problem is very challenging. The mutual coupling is also attributed to three phenomena: near-field coupling, far-field coupling, and surface wave coupling [4]. Many techniques and MIMO structures have been proposed for compact MIMO systems. In [5-7], the size of the proposed antenna was not small enough for the present portable devices. In [8, 9], the proposed antenna was not able to cover the entire UWB bandwidth allocated by the FCC [1]. In [3, 5] and [10-13] unlike our purposed antenna, the antenna elements were perpendicular to each other. None of the above could attain a very high isolation with S 12 < −30 dB in such a small size while covering the whole UWB bandwidth. Certain techniques are also reported to improve isolation. Methods include using simple and fractalbased DGS [14], EBG [15], soft surface structures [16], and Metamaterial-Inspired Isolatorin between the antenna elements [17], etc. Among the aforementioned designs, none of them could achieve very high isolation in such a small size at low-frequency levels because in small size structures reducing mutual coupling at these frequencies due to long wavelength is very challenging.
In this paper, a phase improvement algorithm has been developed to design the nonlinear frequency modulated (NLFM) signal for the four windows of Raised-Cosine, Taylor, Chebyshev, and Kaiser. We have already designed NLFM signal by stationary phase method. The simulation results for the peak sidelobe level of the autocorrelation function in the phase improvement algorithm reveal a significant average decrement of about 5 dB with respect to stationary phase method. Moreover, to evaluate the efficiency of the phase improvement algorithm, minimum error value for each iteration is calculated.Introduction: Goal of pulse compression is to increase bandwidth and improve range resolution [1]. There are several methods for pulse compression. For example, coding methods such as Barker, Huffman, Zadoff-Chu, etc. are utilized in pulse compression [2], but due to the phase discontinuity and the signal amplitude variability (such as the Huffman codes), they result in loss increment in the receiver (due to mismatching) [3]. The linear frequency modulation (LFM) method has received much attention since its phase continuity and the constant amplitude of the signal, but it suffers from relatively high sidelobes in autocorrelation function (ACF) [3].The NLFM method has been proposed to reduce the sidelobes level in ACF. In NLFM method, the signal amplitude is constant and the frequency variations with respect to time is nonlinear. Stationary phase concept (SPC) is commonly used in NLFM method. SPC explains that power spectral density (PSD) in a frequency is relatively high if the related frequency variation is low with regard to time [3]. Using this method leads to noticeable sidelobes level decrement in ACF. Additionally, it causes the main lobe width to increases slightly but negligible.The phase improvement algorithm (PIA) is proposed here to be used after the stationary phase method. This method is designed based on the phase matching techniques. To start the algorithm, an appropriate value for the phase is used which comes from stationary phase method. The algorithm is repeated several times in order to get closer to the optimal phase value where sidelobes level are significantly reduced compared to the stationary phase method.The remainder of the letter is organized as follows: Second section outlines the proposed phase improvement algorithm. In the third section, the simulation results of the proposed algorithm are discussed and a comparison between SPC and the proposed method is made. Finally, the fourth section concludes the paper.
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