In this paper, we propose a complete synchronization algorithm for continuous
phase modulation (CPM) signals in burst-mode transmission over additive white
Gaussian noise (AWGN) channels. The timing and carrier recovery are performed
through a data-aided (DA) maximum likelihood algorithm, which jointly estimates
symbol timing, carrier phase, and frequency offsets based on an optimized
synchronization preamble. Our algorithm estimates the frequency offset via a
one dimensional grid search, after which symbol timing and carrier phase are
computed via simple closed-form expressions. The mean-square error (MSE) of the
algorithm's estimates reveals that it performs very close to the theoretical
Cram\'er-Rao bound (CRB) for various CPMs at signal-to-noise ratios (SNRs) as
low as 0 dB. Furthermore, we present a frame synchronization algorithm that
detects the arrival of bursts and estimates the start-of-signal. We simulate
the performance of the frame synchronization algorithm along with the timing
and carrier recovery algorithm. The bit error rate results demonstrate near
ideal synchronization performance for low SNRs and short preambles.Comment: Copyright 2013 IEE
Abstract-Low probability of intercept (LPI) communication generally relies on the presence of noise to obfuscate a covert signal through the use of spectral spreading or hopping. In contrast, this paper addresses the use of ambient interference from other man-made emissions as a means to mask the presence of covert communication. Specifically, the high power, wide bandwidth, and repeating structure of pulsed radar systems provide an advantageous framework within which to embed a communication signal. The operating paradigm considered here is that of an RF tag/transponder that is illuminated by the radar and intends to covertly communicate with the radar or some other desired receiver while being masked by the ambient radar backscatter to avoid detection by an intercept receiver. Communication takes place on an intra-pulse (or individual pulse) basis to maximize the data rate. The impact of multipath, and its exploitation using time reversal to achieve spatio-temporal focusing, is considered. The processing gain for the destination receiver and intercept receiver are derived analytically and subsequently used to optimize the parameterization of communication symbol design.
Communication at millimeter wave frequencies will be one of the essential new technologies in 5G. Acquiring an accurate channel estimate is the key to facilitate advanced millimeter wave hybrid multiple-input multiple-output (MIMO) precoding techniques. Millimeter wave MIMO channel estimation, however, suffers from a considerably increased channel use overhead. This happens due to the limited number of radio frequency (RF) chains that prevent the digital baseband from directly accessing the signal at each antenna. To address this issue, recent research has focused on adaptive closed-loop and two-way channel estimation techniques. In this paper, unlike the prior approaches, we study a non-adaptive, hence rather simple, open-loop millimeter wave MIMO channel estimation technique. We present a simple random design of channel subspace sampling signals and show that they obey the restricted isometry property (RIP) with high probability. We then formulate the channel estimation as a low-rank subspace decomposition problem and, based on the RIP, show that the proposed framework reveals resilience to a low signal-to-noise ratio. It is revealed that the required number of channel uses ensuring a bounded estimation error is linearly proportional to the degrees of freedom of the channel, whereas it converges to a constant value if the number of RF chains can grow proportionally to the channel dimension while keeping the channel rank fixed. In particular, we show that the tighter the RIP characterization the lower the channel estimation error is. We also devise an iterative technique that effectively finds a suboptimal but stationary solution to the formulated problem. The proposed technique is shown to have improved channel estimation accuracy with a low channel use overhead as compared to that of previous closed-loop and two-way adaptation techniques.
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