This letter considers a cognitive radio (CR) network where multiple secondary downlinks coexist with a primary network. The primary user (PU) is assumed to be protected by the interference outage constraint with only channel distribution information (CDI) being available at the secondary users (SUs). The power allocation problem to maximize the sum outage capacity of the SUs under the interference outage constraint and the transmit power constraint is investigated. Due to the difficulty in obtaining the optimal solution, we propose a heuristic power allocation algorithm based on the bisection search method that can guarantee to satisfy both the interference outage and the transmit power constraints. It is shown that the proposed algorithm converges fast and outperforms other reference algorithms.

Write once memory (WOM) codes allow reuse of a write-once medium. This paper focuses on applying WOM codes to the binary symmetric asymmetric multiple access channel (BS-AMAC). At one specific rate pair, WOM codes can achieve the BS-AMAC maximum sum-rate. Further, any achievable rate pair for a two-write WOM code is also an achievable rate pair for the BS-AMAC. Compared to the uniform input distribution of linear codes, the non-uniform WOM input distribution is helpful for a BS-AMAC. In addition, WOM codes enable “symbol-wise estimation”, resulting in the decomposition to two distinct channels. This scheme does not achieve the BS-AMAC maximum sum-rate if the channel has errors, however leads to reduced-complexity decoding by enabling independent decoding of two codewords. Achievable rates for this decomposed system are also given. The AMAC has practical application to the relay channel and we briefly discuss the relay channel with block Markov encoding using WOM codes. This scheme may be effective for cooperative wireless communications despite the fact that WOM codes are designed for data storage.

In this study, we develop a new algorithm for decoding binary linear codes for symbol-pair read channels. The symbol-pair read channel was recently introduced by Cassuto and Blaum to model channels with higher write resolutions than read resolutions. The proposed decoding algorithm is based on linear programming (LP). For LDPC codes, the proposed algorithm runs in time polynomial in the codeword length. It is proved that the proposed LP decoder has the maximum-likelihood (ML) certificate property, i.e., the output of the decoder is guaranteed to be the ML codeword when it is integral. We also introduce the fractional pair distance dfp of the code, which is a lower bound on the minimum pair distance. It is proved that the proposed LP decoder corrects up to ⌈dfp/2⌉-1 errors.

In this paper, we discuss an algebraic decoding of BCH codes over symbol-pair read channels. The channels output overlapping pairs of symbols in storage applications. The pair distance and pair error are used in the channels. We define a polynomial that represents the positions of the pair errors as the error-locator polynomials and a polynomial that represents the positions of the pairs of a received pair vector in conflict as conflict-locator polynomial. In this paper, we propose algebraic methods for correcting two-pair and three-pair errors for BCH codes. First, we show the relation between the error-locator polynomials and the conflict-locator polynomial. Second, we show the relation among these polynomials and the syndromes. Finally, we provide how to correct the pair errors by solving equations including the relational expression by algebraic methods.

Reliable wireless communication often requires accurate knowledge of the underlying multipath channels. Numerous measurement campaigns have shown that physical multipath channels tend to exhibit a sparse structure. Conventional blind channel identification (BCI) strategies such as the least squares, which are known to be optimal under the assumption of rich multipath channels, are ill-suited to exploiting the inherent sparse nature of multipath channels. Recently, l1-norm regularized least-squares-type approaches have been proposed to address this problem with a single parameter governing all coefficients, which is equivalent to maximum a posteriori probability estimation with a Laplacian prior for the channel coefficients. Since Laplace prior is not conjugate to the Gaussian likelihood, no closed form of Bayesian inference is possible. Following a different approach, this paper deals with blind channel identification of a single-input multiple-output (SIMO) system based on sparse Bayesian learning (SBL). The inherent sparse nature of wireless multipath channels is exploited by incorporating a transformative cross relation formulation into a general Bayesian framework, in which the filter coefficients are governed by independent scalar parameters. A fast iterative Bayesian inference method is then applied to the proposed model for obtaining sparse solutions, which completely eliminates the need for computationally costly parameter fine tuning, which is necessary in the l1-norm regularization method. Simulation results are provided to demonstrate the superior effectiveness of the proposed channel estimation algorithm over the conventional least squares (LS) scheme as well as the l1-norm regularization method. It is shown that the proposed algorithm exhibits superior estimation performance compared to both LS and l1-norm regularization methods.

This paper presents a Multi-channel MAC protocol with channel grouping for multi-channel ad-hoc networks. The proposed protocol has both concepts of the multiple rendezvous and the single control channel protocols, which were proposed as a MAC protocol for multi-channel ad-hoc network without centralized stations. In the proposed protocol, all the channels are divided into some groups and each group has a control channel. Network nodes circulate among the groups and channel negotiations are carried out on a control channel of the group. By applying the channel grouping, it is possible to enhance network throughput without reducing the channel-usage probability. Because there is an optimum group number for obtaining the highest throughput, this paper gives analytical expressions of maximum network throughput for the proposed protocol as a function of system parameters. The effectiveness of the proposed protocol is shown from simulation results. In addition, the validity of the analytical expressions is confirmed from quantitative agreements between analytical predictions and simulation results.

In this paper, we propose and analyze the opportunistic amplify-and-forward (AF) relaying scheme using antenna selection in conjunction with different adaptive transmission techniques over Rayleigh fading channels. In this scheme, the best antenna of a source and the best relay are selected for communication between the source and destination. Closed-form expressions for the outage probability and average symbol error rate (SER) are derived to confirm that increasing the number of antennas is the best option as compared with increasing the number of relays. We also obtain closed-form expressions for the average channel capacity under three different adaptive transmission techniques: 1) optimal power and rate adaptation; 2) constant power with optimal rate adaptation; and 3) channel inversion with a fixed rate. The channel capacity performance of the considered adaptive transmission techniques is evaluated and compared with a different number of relays and various antennas configurations for each adaptive technique. Our derived analytical results are verified through extensive Monte Carlo simulations.

Wireless channel emulators are used for the performance evaluation of wireless systems when actual wireless environment test is infeasible. The main contribution of this paper is the design of a MU-MIMO channel emulator capable of sending channel feedback automatically to the access point from the generated channel coefficients after the programmable time duration. This function is used for MU beamforming features of IEEE 802.11ac. The second contribution is the low complexity design of MIMO channel emulator with a single path implementation for all MIMO channel taps. A single path design allows all elements of the MIMO channel matrix to use only one Gaussian noise generator, Doppler filter, spatial correlation channel and Rician fading emulator to minimize the hardware complexity. In addition, single path implementation allows the addition of the feedback channel output with only a few additional non-sequential elements which would otherwise double in a parallel implementation. To demonstrate the functionality of our MU-MIMO channel emulator, we present actual hardware emulator results of MU-BF receive signal constellation on oscilloscope.

Although many approaches about ideal channels have been proposed in previous researches, few authors considered the situation of nonideal communication links. In this paper, we study the problem of distributed decision fusion over nonideal channels by using the scan statistics. In order to obtain the fusion rule under nonideal channels, we set up the nonideal channels model with the modulation error, noise and signal attenuation. Under this model, we update the fusion rule by using the scan statstics. We firstly consider the fusion rule when sensors are distributed in grid, then derive the expressions of the detection probability and false alarm probability when sensors follow an uniform distribution. Extensive simulations are conducted in order to investigate the performance of our fusion rule and the influence of signal-noise ratio (SNR) on the detection and false alarm probability. These simulations show that the theoretical values of the global detection probability and the global false alarm probability are close to the experimental results, and the fusion rule also has high performance at the high SNR region. But there are some further researches need to do for solving the large computational complexity.

The dark channel prior (DCP)-based image dehazing method has been widely used for enhancing visibility of outdoor images. However, since the DCP-based method assumes that the minimum values within local patches of natural outdoor haze-free images are zero, underestimation of the transmission is inevitable when the assumption does not hold. In this letter, a novel iterative image dehazing algorithm is proposed to compensate for the underestimated transmission. Experimental results show that the proposed method can improve the dehazing performance by increasing the transmission estimation accuracy.

In this paper, an iterative robust minimum-mean square error (MMSE) receiver for space-time block coding (STBC) is proposed to mitigate the performance degradations caused by channel state information (CSI) errors. The proposed scheme estimates an instantaneous covariance matrix of the effective noise, which includes additive white Gaussian noise and the effect of CSI errors. For this estimation, multiple solution candidate vectors are selected based on the distances between the MMSE estimate of the solution and the constellation points, and their a-posteriori probabilities are utilized to execute the estimation of the covariance matrix. To improve the estimation accuracy, the estimated covariance matrix is updated iteratively. Simulation results show that proposed robust receiver achieves substantial performance gains in terms of bit error rates as compared to conventional receiver schemes under CSI errors.

The 3-D channel routing is a fundamental problem on the physical design of 3-D integrated circuits. The 3-D channel is a 3-D grid G and the terminals are vertices of G located in the top and bottom layers. A net is a set of terminals to be connected. The objective of the 3-D channel routing problem is to connect the terminals in each net with a Steiner tree (wire) in G using as few layers as possible and as short wires as possible in such a way that wires for distinct nets are disjoint. This paper shows that the problem is intractable. We also show that a sparse set of ν 2-terminal nets can be routed in a 3-D channel with O(√ν) layers using wires of length O(√ν).

In this paper, we present the channel estimation (CE) problem in the orthogonal frequency division multiplexing system with offset quadrature amplitude modulation (OFDM/OQAM). Most CE methods rely on the assumption of a low frequency selective channel to tackle the problem in a way similar to OFDM. However, these methods would result in a severe performance degradation of the channel estimation when the assumption is not quite inaccurate. Instead, we focus on estimating the channel impulse response (CIR) itself which makes no assumption on the degree of frequency selectivity of the channels. After describing the main idea of this technique, we present an iterative CE method that does not require zero-value guard symbols in the preamble and consequently improves the spectral efficiency. This is done by the iterative estimation of the unknown transmitted data adjacent to the preamble. Analysis and simulation results validate the efficacy of the proposed method in multipath fading channels.

This paper presents a wideband digital predistortion (DPD) architecture suitable for wideband wireless systems, such as IEEE 802.11ad/WiGig, where low oversampling ratio of the digital-to-analog converter (DAC) is a bottleneck for available linearization bandwidth. In order to overcome the bandwidth limitation in the conventional DPD, the proposed DPD introduces a complex coefficient filter in the DPD signal processing, which enables it to achieve asymmetric linearization. This approach effectively suppresses one side of adjacent channel leakages with twice the bandwidth as compared to the conventional DPD. The concept is verified through system simulation and measurements. Using a scaled model of a 2 GHz RF carrier frequency, the measurement shows a 4.2 dB advantage over the conventional DPD in terms of adjacent channel leakage.

In this letter, a novel channel impulse response (CIR)-based fingerprinting positioning method using kernel principal component analysis (KPCA) has been proposed. During the offline phase of the proposed method, a survey is performed to collect all CIRs from access points, and a fingerprint database is constructed, which has vectors including CIR and physical location. During the online phase, KPCA is first employed to solve the nonlinearity and complexity in the CIR-position dependencies and extract the principal nonlinear features in CIRs, and support vector regression is then used to adaptively learn the regress function between the KPCA components and physical locations. In addition, the iterative narrowing-scope step is further used to refine the estimation. The performance comparison shows that the proposed method outperforms the traditional received signal strength based positioning methods.

In this letter, we present a spectrally efficient multicast method which enables a transmitter to simultaneously transmit multiple multicast streams without any interference among multicast groups. By using unique combiners at receivers with multiple antennas within each multicast group, the proposed method simplifies multiple channels between the transmitter and the receivers to an equivalent channel. In addition, we establish the sufficient condition for the system configuration which should be satisfied for the channel simplification and provide a combiner design technique for the receivers. To remove interference among multicast groups, the precoder for the transmitter is designed by utilizing the equivalent channels. By exploiting time resources efficiently, the channel simplification (CS) based method achieves a higher sum rate than the time division multiplexing (TDM) based method, which the existing multicast techniques fundamentally employ, at high signal-to-noise ratio (SNR) regime. Furthermore, we present a multicast method combining the CS based method with the TDM based method to utilize the benefits of both methods. Simulation results successfully demonstrate that the combined multicast method obtains a better sum rate performance at overall SNR regime.

Global navigation satellite system (GNSS) receivers equipped with the frequency domain interference suppression (FDIS) filter can operate in environments with harsh interference. The FDIS will not cause tracking error bias for an ideal analog receiver channel as its magnitude response and phase response are constant. However, the analog receiver channel distortion is induced by RF cables, amplifiers, and mixers. The distortion of the channel caused asymmetry correlation function. The correlation function is further deformed by the FDIS filter. More seriously, since the FDIS filter is adaptive, the bias will vary with the jamming pattern, especially when the frequency of interference is varying. For precision navigation applications, this bias must be mitigated. Fortunately, to prevent power loss, the analog receiver channel filter is a real function or the imaginary part is negligible. Therefore, the magnitude response and the phase response are even functions. Based on these channel features, a new FDIS filter based on mirror frequency amplitude compensation (MFAC) method is proposed in this paper. The amplitude of the symmetry position of the notch frequency is doubled in the MFAC method in order to mitigate the tracking bias. Simulation results show that the MFAC-based FDIS method is capable of reducing the bias error to less than 0.1ns, which is significant smaller than that achieved by the traditional FDIS method.

We reported a secure scan design approach using shift register equivalents (SR-equivalents, for short) that are functionally equivalent but not structurally equivalent to shift registers [10 and also introduced generalized shift registers (GSRs, for short) to apply them to secure scan design [11]-[13]. In this paper, we combine both concepts of SR-equivalents and GSRs and consider the synthesis problem of SR-equivalent GSRs, i.e., how to modify a given GSR to an SR-equivalent GSR. We also consider the enumeration problem of SR-equivalent GFSRs, i.e., the cardinality of the class of SR-equivalent GSRs to clarify the security level of the secure scan architecture.

In this paper, a new scheme is presented for ground moving target indication for multichannel high-resolution wide-swath (HRWS) SAR systems with modified reconstruction filters. The conventional steering vector is generalized for moving targets through taking into account the additional Doppler centroid shift caused by the across-track velocity. Two modified steering vectors with symmetric velocity information are utilized to produce two images for the same scene. Due to the unmatched steering vectors, the stationary backgrounds are defocused but they still hold the same intensities in both images but moving targets are blurred to different extents. The ambiguous components of the moving targets can also be suppressed due to the beamforming in the reconstruction procedure. Therefore, ground moving target indication can be carried out via intensity comparison between the two images. The effectiveness of the proposed method is verified by both simulated and real airborne SAR data.

This study investigates a real-time joint channel and hyperparameter estimation method for orthogonal frequency division multiplexing mobile communications. The channel frequency response of the pilot subcarrier and its fixed hyperparameters (such as channel statistics) are estimated using a Liu and West filter (LWF), which is based on the state-space model and sequential Monte Carlo method. For the first time, to our knowledge, we demonstrate that the conventional LWF biases the hyperparameter due to a poor estimate of the likelihood caused by overfitting in noisy environments. Moreover, this problem cannot be solved by conventional smoothing techniques. For this, we modify the conventional LWF and regularize the likelihood using a Kalman smoother. The effectiveness of the proposed method is confirmed via numerical analysis. When both of the Doppler frequency and delay spread hyperparameters are unknown, the conventional LWF significantly degrades the performance, sometimes below that of least squares estimation. By avoiding the hyperparameter estimation failure, our method outperforms the conventional approach and achieves good performance near the lower bound. The coding gain in our proposed method is at most 10 dB higher than that in the conventional LWF. Thus, the proposed method improves the channel and hyperparameter estimation accuracy. Derived from mathematical principles, our proposal is applicable not only to wireless technology but also to a broad range of related areas such as machine learning and econometrics.