MATH

The relative motion between radar and targets in large time-bandwidth product synthetic aperture radar (SAR) induces serious dilation in the received signal. To process the received signal with serious dilation, a new technique called dilation dependent matched filtering (DDMF) is proposed to combine with the two-dimensional space frequency interpolation wavefront reconstruction (SFIWR) method. The DDMF-SFIWR method can effectively eliminate the impact of dilation when the illuminated area is relatively small, as verified by simulations and acoustic experiments.

A method is proposed to factor the type-II discrete cosine transform (DCT-II) into lifting steps and additions. After approximating the lifting matrices, we get a new type-II integer discrete cosine transform (IntDCT-II) that is float-point multiplication free. Based on the relationships among the various types of DCTs, we can generally factor any DCTs into lifting steps and additions and then get four types of integer DCTs, which need no floatpoint multiplications. By combining the polynomial transform and the one-dimensional (1-D) integer cosine transform, a two-dimensional (2-D) integer discrete cosine transform is proposed. The proposed transform needs only integer operations and shifts. Furthermore, it is nonseparable and requires a far fewer number of operations than that used by the corresponding row-column 2-D integer discrete cosine transform.

Cyclic prefixed single carrier (CP-SC) has emerged as a promising technique for wideband wireless communication. The cyclic prefix (CP) in CP-SC costs some bandwidth, but it cannot be used for channel estimation in fast-variant channel environment. In this letter, a new scheme is proposed for CP-SC which not only adds a CP but also a suffix. With the aid of the CP and suffix, channel responses can be resolved by the fast Fourier transform. The additional known symbols also ensure input symbols recoverable regardless of the channel null locations. Better synchronization is also possible.

In this letter, a Rake-based multiuser detection technique, consisting of multiuser single-path signal separation, time-delay estimation, and multipath combining, is proposed for quasi-synchronous spatial-division multiple-access (SDMA) systems. Time diversity is achieved for performance improvement. In addition, only the upper bounds of the channel length and the time delays are required. Simulation results verify the effectiveness of the proposed technique, as well as its robustness against overestimation of the maximum channel length and the maximum time delay.

A semiblind method is proposed for simultaneously estimating the carrier frequency offsets (CFOs) and channels of an uplink multiuser multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) system. By incorporating the CFOs into the transmitted symbols and channels, the MIMO-OFDM with CFO is remodeled into an MIMO-OFDM without CFO. The known blind method for channel estimation (Zeng and Ng in 2004) (Y. H. Zeng and T. S. Ng, "A semi-blind channel estimation method for multiuser multi-antenna OFDM systems," IEEE Trans. Signal Process., vol. 52, no. 5, pp. 1419-1429, May 2004.) is then directly used for the remodeled system to obtain the shaped channels with an ambiguity matrix. A pilot OFDM block for each user is then exploited to resolve the CFOs and the ambiguity matrix. Two dedicated pilot designs, periodical and consecutive pilots, are discussed. Based on each pilot design and the estimated shaped channels, two methods are proposed to estimate the CFOs. As a result, based on the second-order statistics (SOS) of the received signal and one pilot OFDM block, the CFOs and channels are found simultaneously. Finally, a fast equalization method is given to recover the signals corrupted by the CFOs.

Space time coding has by now been well documented as an attractive means of achieving high data rate transmissions with diversity and coding gains, provided that the underlying propagation channels can be accounted for. In this paper, we rely on redundant linear precoding to develop a (semi-)blind channel estimation algorithm for space time (ST) orthogonal frequency division multiplexing (OFDM) transmissions with Alamouti's block code applied on each subcarrier. We establish that multichannel identifiability is guaranteed up to one or two scalar ambiguities, regardless of the channel zero locations and the underlying signal constellations, when distinct or identical precoders are employed for even and odd indexed symbol blocks. With known pilots inserted either before or after precoding, we resolve the residual scalar ambiguities and show that distinct precoders require half the number of pilots than identical precoders to achieve the same channel estimation accuracy. Simulation results confirm our theoretical analysis and illustrate that the proposed semi-blind algorithm is capable of tracking slow channel variations and improving the overall system performance relative to competing differential ST alternatives.

This paper considers a scenario in which a secondary user (SU) opportunistically accesses a channel allocated to some primary network (PN) that switches between idle and active states in a time-slotted manner. At the beginning of each time slot, SU can choose to stay idle or to carry out spectrum sensing to detect the state of PN. If PN is detected to be idle, SU can carry out data transmission. Spectrum sensing consumes time and energy and introduces false alarms and mis-detections. The objective is to dynamically decide, for each time slot, whether SU should stay idle or carry out sensing, and if so, for how long, to maximize the expected reward. We formulate this as a partially observable Markov decision process and prove important properties of the optimal control policies. Heuristic control policies with low complexity and good performance are also proposed. Numerical results show the significant performance gain of our dynamic control approach for opportunistic spectrum access.

Efficient estimations of the carrier frequency offset (CFO) and channel are vital to orthogonal frequency division multiplexing (OFDM). In this paper, a new fast algorithm is proposed for estimating the CFO based on the pilot structure in [M. Morelli, U. Mengali, An improved frequency offset estimator for OFDM applications, IEEE Commun. Lett. 3(3) (1999) 75-77; H.K. Song, Y.H. You, J.H. Paik, Y.S. Cho, Frequency-offset synchronization and channel estimation for OFDM-based transmission, IEEE Commun. Lett. 4(3) (2000) 95-97]. The new method first identifies a coarse estimation of the CFO and then refines it by a least square (LS) method. It always gives a more accurate estimation than the multistage method in [H.K. Song, Y.H. You, J.H. Paik, Y.S. Cho, Frequency-offset synchronization and channel estimation for OFDM-based transmission, IEEE Commun. Lett. 4(3) (2000) 95-97] and is better than the method in [M. Morelli, U. Mengali, An improved frequency offset estimator for OFDM applications, IEEE Commun. Lett. 3(3) (1999) 75-77] at low SNR. It only slightly worse than the maximum likelihood estimator at high SNR. Furthermore, the proposed method can be used for CFO tracking. Also, in this paper, channel estimation by using the same pilot block is considered. The best values for the non-null subcarriers are found to optimize the channel estimation and peak to average power ratio (PAPR) simultaneously. Simulations demonstrate that the proposed methods are effective.

Measurements performed at several locations clearly show that frequency spectrum is under-utilized. Cognitive radio (CR) is a strong candidate to ensure better spectrum utilization by providing access in an opportunistic manner, i.e., when the spectrum is temporary or spatially unutilized (a.k.a. white-space). The key enabling technology for realizing better frequency spectrum utilization in CR system is spectrum sensing. In particular, spectrum sensing in television (TV) bands is of great interest due to the potential availability of spectrum as a result of the planned migration of analog TV broadcasting to digital TV broadcasting. In this paper, we present the TV white-space prototype that we developed to detect vacant spectrum in TV bands. The prototype is implemented on a real-time platform and tested in practical environments. Results show that our prototype is able to detect vacant channels up to sensitivities from −116 to −120 dBm under realistic conditions. This confirmation will give the much needed confidence in the capability of CR systems to detect the operation of primary users and protect their use of spectrum.

In this paper, we propose pilot-aided subspace tracking channel estimators for OFDM systems over power line multipath channels (PLC) with unknown colored noise exploiting real signal transmission. In the proposed estimators, the signal subspace and the noise subspace are identified through decomposition of pseudo covariance matrix of received time domain samples, unlike the conventional subspace tracking which is based on the decomposition of covariance matrix. The least square (LS) technique together with the identified signal subspace are then used to derive the channel estimate in time domain using pilot OFDM symbols. The proposed estimators do not assume the knowledge of statistics of colored noise. The simulations show that, the proposed pilot-aided subspace tracking channel estimators outperform conventional LS based channel estimators, however, the performance gain is degraded with the increase of number of multipath. Further, the proposed estimators require the number of delay path known as a prior.

A subspace based blind method is proposed for estimating the channel responses of a multiuser and multi-antenna OFDM uplink system. It gives estimations to all channel responses,subject to a scalar matrix ambiguity, and does not need precise channel order information (only an upper bound for all orders is required). Furthermore, the scalar ambiguity matrix can be easily resolved by using only one pilot OFDM block given that the number of users is smaller than the number of symbols in the pilot symbol block. Simulations show that the method is effective and robust.

A subspace-based blind method is proposed for estimating the channel responses of a multiuser and multiantenna orthogonal frequency division multiplexing (OFDM) uplink system. It gives estimations to all channel responses subject to a scalar matrix ambiguity and does not need precise channel order information (only an upper bound for the orders is required). Furthermore, the scalar ambiguity matrix can be easily resolved by using only one pilot OFDM block, given that the number of users is smaller than the number of symbols in the pilot symbol block. Equalization methods are discussed based on the estimated channels. By using partial knowledge of the channels, a multipath subspace method is proposed that reduces the computational complexity. Simulations show that the methods are effective and robust.

This article presents a method for realtime mapping of algal blooms in turbid coastal waters using the remote sensing reflectance of red band (Channel 1, 580-680 nm) and near-infrared band (Channel 2, 720-1100 nm) of the AVHRR sensor on the NOAA series satellites. A turbidity-free function for near-infrared and red signals, α 0 = (b b (1) /b b (2))(a (2) / a (1)) based on the first order b b /(a+b b) model deducing equation R rs (2)−1 = α 0 R rs (1) −1 + g −1 (1−α 0), were selected as a chlorophyll-a related index for detecting algal blooms, and the algal blooms with chlorophyll-a concentration of 64-256 mg/L could be defined by window of 1.6 < α 0 < 5.2 and 0.01< R rs (2) /g < 0.2. Such turbidity-free two-band method is supported by both sea-truth data and remote sensing experiment for an algal blooms event on the near-shore water off the Minjiang estuary of southeastern China during early June of 2003. Comparisons of this algorithm with other published algorithms, one-band method (i.e. method of bright water) or two-band methods (i.e. method of ratio, method of NDVI, and method of subtracting) have suggested that the turbidity-free function method could be regarded as a standard algorithm in capabilities of AVHRR imagery or other high resolution but wide near-infrared and red band imagery for detecting algal blooms events in coastal waters.

Cognitive radio is widely expected to be the next Big Bang in wireless communications. Spectrum sensing, that is, detecting the presence of the primary users in a licensed spectrum, is a fundamental problem for cognitive radio. As a result, spectrum sensing has reborn as a very active research area in recent years despite its long history. In this paper, spectrum sensing techniques from the optimal likelihood ratio test to energy detection, matched filtering detection, cyclostationary detection, eigenvalue-based sensing, joint space-time sensing, and robust sensing methods are reviewed. Cooperative spectrum sensing with multiple receivers is also discussed. Special attention is paid to sensing methods that need little prior information on the source signal and the propagation channel. Practical challenges such as noise power uncertainty are discussed and possible solutions are provided. Theoretical analysis on the test statistic distribution and threshold setting is also investigated.

Orthogonal frequency division multiplex (OFDM) has been widely used in various wireless communications systems. Thus the detection of OFDM signals is of significant importance in cognitive radio and other spectrum sharing systems. A common feature of OFDM in many popular standards is that some pilot subcarriers repeat periodically after certain OFDM blocks. In this paper, sensing methods for OFDM signals are proposed by using such repetition structure of the pilots. Firstly, special properties for the auto-correlation (AC) of the received signals are identified, from which the optimal likelihood ratio test (LRT) is derived. However, this method requires the knowledge of channel information, carrier frequency offset (CFO) and noise power. To make the LRT method practical, we then propose an approximated LRT (ALRT) method that does not rely on the channel information and noise power, thus the CFO is the only remaining obstacle to the ALRT. To handle the problem, we propose a method to estimate the composite CFO and compensate its effect in the AC using multiple taps of ACs of the received signals. Computer simulations have shown that the proposed sensing methods are robust to frequency offset, noise power uncertainty, time delay uncertainty, and frequency selectiveness of the channel.

A novel subspace (SS) based blind channel estimation method for multi-input, multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems is proposed in this work. With an appropriate re-modulation on the received signal blocks, the SS method can be effectively applied to the cyclic prefix (CP) based MIMO-OFDM system when the number of the receive antennas is no less than the number of transmit antennas. These features show great compatibility with the coming fourth generation (4G) wireless communication standards as well as most existing single-input single-output (SISO) OFDM standards, thus allow the proposed algorithm to be conveniently integrated into practical applications. Compared with the traditional SS method, the proposed algorithm exhibits many advantages such as robustness to channel order overestimation, capability of guaranteeing the channel identifiability etc. Analytical expressions for the mean-square error (MSE) and the approximated Cramér-Rao bound (ACRB) of the proposed algorithm are derived in closed forms. Various numerical examples are conducted to corroborate the proposed studies.

In this letter, a method is proposed to optimally combine the received signal samples in space and time based on the principle of maximizing the signal-to-noise ratio (SNR). After the combining, energy detection (ED) is used. However, optimal combining needs information of the source signal and channel, which is usually unknown. To overcome this difficulty, a method is proposed to blindly combine the signal samples. Similar to energy detection, blindly combined energy detection (BCED) does not need any information of the source signal and the channel a priori. BCED can be much better than ED for highly correlated signals, and most importantly, it does not need noise power estimation and overcomes ED's susceptibility to noise uncertainty. Also, perfect synchronization is not required. Simulations based on wireless microphone signals and randomly generated signals are presented to verify the methods.