A simplified parametric channel estimation approach was proposed for orthogonal frequency division multiplexing (OFDM) systems. Based on parametric channel model, this algorithm is composed of two parts: the estimation of channel parameters and channel interpolation. The exponentially embedded family (EEF) criterion is exploited to determine the number of channel paths as well as the multipath time delays. Consequently, the channel frequency responses is acquired via the estimated parameters. Additionally, the authors’ scheme is computationally efficient owing to the needless of the eigenvalue decomposition or the estimation of signal parameters by the rotational invariance technique (ESPRIT). Simulations are provided to validate the performance of this algorithm from perspectives of the probability of correct estimation and the mean square error (MSE). It is demonstrated that this approach exhibits a superior performance over the existing algorithms.
An amplify-and-forward (AF) based multi-relay network is studied. In order to minimize the system outage probability with a required transmission rate, a joint power allocation (PA) and multi-relay selection scheme is proposed under both total and individual power constraints (TIPC). In the proposed scheme, the idea of ordering is adopted to avoid exhaustive search without losing much system performance. Besides the channel quantity, the ordering algorithm proposed in this article also takes relays’ maximal output ability into consideration, which is usually ignored in traditional relay ordering algorithms. In addition, simple power reallocation method is provided to avoid repetitive PA operation during the process of searching all possible relay subsets. By Adopting the idea of ordering and using the proposed power reallocation method lead to remarkable decrease of the computation complexity, making the scheme easier and more feasible to implement in practical communication scenarios. Simulations show that the proposed multi-relay selection scheme provides similar performance compared to the optimal scheme with optimal PA and exhaustive search (OPAES) but with much lower complexity
In order to take advantage of the asynchronous sensing information, alleviate the sensing overhead of secondary users (SUs) and improve the detection performance, a sensor node-assisted asynchronous cooperative spectrum sensing (SN-ACSS) scheme for cognitive radio (CR) network (CRN) was proposed. In SN-ACSS, each SU is surrounded by sensor nodes (SNs), which asynchronously make hard decisions and soft decisions based on the Bayesian fusion rule instead of the SU. The SU combines these soft decisions and makes the local soft decision. Finally, the fusion center (FC) fuses the local soft decisions transmitted from SUs with different weight coefficients to attain the final soft decision. Besides, the impact of the statistics of licensed band occupancy on detection performance and the fact that different SNs have different sensing contributions are also considered in SN-ACSS scheme. Numerical results show that compared with the conventional synchronous cooperative spectrum sensing (SCSS) and the existing ACSS schemes, SN-ACSS algorithm achieves a better detection performance and lower cost with the same number of SNs.
Particle filtering (PF) algorithm has the powerful potential for coping with difficult non-linear and non-Gaussian problems. Aiming at non-linear, non-Gaussian and time-varying characteristics of power line channel, a time-varying channel estimation scheme combined PF algorithm with decision feedback method is proposed. In the proposed scheme, firstly the indoor power line channel is measured using the pseudo-noise (PN) correlation method, and a first-order dynamic autoregressive (AR) model is set up to describe the measured channel, then, the channel states are estimated dynamically from the received signals by exploiting the proposed scheme. Meanwhile, due to the complex noise distribution of power line channel, the performance of channel estimation based on the proposed scheme under the Middleton class A impulsive noise environment is analyzed. Comparisons are made with the channel estimation scheme respectively based on least square (LS), Kalman filtering (KF) and the proposed algorithm. Simulation indicates that PF algorithm dealing with this power line channel estimation difficult non-linear and non-Gaussian problems performance is superior to those of LS and KF respectively, so the proposed scheme achieves higher estimation accuracy. Therefore, it is confirmed that PF algorithm has its own unique advantage for power line channel estimation.
The article investigates how to send perfect space-time codes with low feedback amount and symbol-by-symbol decoding for X channel using precoders. It is assumed that two users are introduced with two antennas and two receivers. Each user employs a rate-2 space-time block code and follows certain rule when sending codewords. The multi-user interference is eliminated by pre-coding at the transmitter and linear processing at the receiver. Compared with the existing scheme for the same scene, the proposed scheme greatly reduces feedback amount and improves the transmission efficiency, while keeping the same decoding complexity. Simulations demonstrate the validity of the proposed scheme.
Orthogonal schemes are usually adopted for cell-edge users in a cellular network, where the spectrum is poorly utilized. A new interference alignment (IA) based space-division hybrid (IA-SDH) scheme is proposed by joint design transmit precoding matrices and beamforming matrices for a three-cell constant cellular network, where users at cell-center work with a conventional scheme while users at cell-edge utilize an IA scheme. Therefore, the cell-edge users suffer no interference and cell-center users are only affected by negligible inter-cell interference. Analysis simulation shows that more users can be served simultaneously than that of a conventional scheme in certain user distributions.
The fixed level and dynamic denoising method was studied based on indoor-to-outdoor measured channel impulse responses (IRs) at 5.25 GHz with radio frequency (RF) 100 MHz bandwidth. It is found that the dynamic ranges, peak powers and noise floors of the IRs are with close correlations. The comparisons with different denoising methods are given by deriving the power delay profiles (PDPs), root mean square (RMS) delay spread (RMS DS), number of paths (NOPs) and Ricean K-factors. It is concluded that the traditional fixed level noise cut is under estimate of DS and NOPs. The Ricean K-factors are of little sensitive to noise cut irrespective of what kind of methods applied. The PDPs are not very sensitive to the fixed level noise cut, however, obvious changes can be found by dynamic noise cut. The dynamic noise cut is preferred when clear noise floors is observed and decided from the measured IRs, it’s of importance in data post processing for wideband radio channel measurements as well as the relevant modeling work.
Cloud radio access networks (C-RANs) were proposed as promising solutions to increase both spectrum and energy efficiency performance in next generation wireless communication systems. Much works discussed the concrete implementation technology to justify the huge development potential of C-RAN. However, only a few litterateurs focused on characterizing the physical layer security in the downlink. The authors studied the physical layer security in downlink heterogeneous C-RAN systems in the article. To characterize the random deployment of remote radio units (RRUs) with single antenna configuration, the stochastic geometry is based to evaluate the proposals’ secrecy transmission capacity performances, where the closed-form expressions are derived. Furthermore, two security strategies based on eavesdropper neutralization region to protect the target RRU user against eavesdropping were presented and analyzed. Simulation illustrates the secrecy transmission capacity performance limits on different system parameters. The presented security strategies show a significant enhancement on the secrecy performance.
The multi-radio multi-channel wireless mesh network (MRMC-WMN) draws general attention because of its excellent throughput performance, robustness and relative low cost. The closed interactions among power control (PC), channel assignment (CA) and routing is contributed to the performance of multi-radio multi-channel wireless mesh networks (MRMC-WMNs). However, the joint PC, CA and routing (JPCR) design, desired to achieve a global optimization, was poor addressed. The authors present a routing algorithm joint with PC and CA (JPCRA) to seek the routing, power and channel scheme for each flow, which can improve the fairness performance. Firstly, considering available channels and power levels, the routing metric, called minimum flow rate, is designed based on the physical interference and Shannon channel models. The JPCRA is presented based on the genetic algorithm (GA) with simulated annealing to maximize the minimum flow rate, an non-deterministic polynomial-time hard (NP-Hard) problem. Simulations show the JPCRA obtains better fairness among different flows and higher network throughput.
This article addresses the problem of route selection in wireless mesh networks (WMNs). The traditional routing metrics adopt packet delivery ratio (PDR) as a representative metric of wireless link quality assessment. However, PDR measured by the broadcast-based probe method is affected by the size, number and transmission rate of probe packets, which influences the metric accuracy. In this paper, improved expected transmission count (iETX), a new routing metric of interference-aware link quality, is proposed for WMNs. Dispensing with traditional broadcast-based probing method, the iETX uses regional physical interference model to obtain PDR. Regional physical interference model is built upon the relationship between signal to interference plus noise ratio (SINR) and PDR, which contributes to the improvement of metric accuracy. The iETX comprehensively considers the effects of interference and link quality and minimizes the expected number of packet transmissions required for successful delivery, which helps find a path with minimum interference and high throughput. Simulation shows that the proposed metric can significantly improve the network performance.
A voice conversion (VC) system was designed based on Gaussian mixture model (GMM) and radial basis function (RBF) neural network. As a voice conversion model, RBF network needs quantities of training data to improve its performance. For one speech, the networks trained by different segments of data have different transformation effects. Since trying segment by segment to obtain the best conversion effect is complex, a conversion method was proposed, that uses GMM for statistics before training RBF network to aim at the problem. The speech transformation and representation using adaptive interpolation of weighted spectrum (STRAIGHT) model is used for accurate extraction of vocal tract spectrum. Then GMM is used to classify the numerous spectral parameters. The obtained mean parameters were trained in RBF network. Experiment reveals that, the soft classification ability of GMM can promptly realize the reduction and classification of training data under the premise of ensuring the training effect. The selection complexity is decreased thereafter. Compared to the conventional RBF network training methods, this method can make the transformation of spectral parameters more effective and improve the quality of converted speech.
The technology of 60 GHz radio is considered promising for providing fast connectivity and gigabit data rate. One of the main challenges to its secure indoor transmission is how to generate secret keys between communication devices. To investigate this issue, The authors develop an efficient mechanism of secret key generation exploiting multipath relative delay based on 60 GHz standard channel models. The comparison of key-mismatch probability between line-of-sight (LOS) and non-line-of-sight (NLOS) environments is considered. Verification of the proposed scheme is conducted. Simulation shows that the number of extracted multipath components proportionally did affect key generation rate and key-mismatch probability. It also indicates that communicating transceivers have a slightly lower key-mismatch probability in NLOS condition than in LOS condition. Moreover, in comparison to the existing approach of using received signal amplitude as a common random source, the mechanism can achieve better performance in key agreement.
Sequences with nice pseudo-randomness play an important role in not only communication system but also cryptography system. Based on the Legendre-Sidelnikov sequence, a modified Legendre-Sidelnikov sequence was introduced. The exact value of the autocorrelation function was derived by strict computation. According to the values of the autocorrelation functions of the two Legendre-Sidelnikov sequences, it is proven that both of them have perfect pseudo-randomness. Furthermore, a detailed comparison between autocorrelation functions of the two Legendre-Sidelnikov sequences was deduced. It indicates that no matter which parameters are chosen, the modified sequence has pseudo-randomness as good as the primitive sequence, which is of great significance for applications.
Equivalence between two classes of quaternary sequences with odd period and best known autocorrelation are proved. A lower bound on the linear complexity of these sequences is presented. It is shown that the quaternary sequences have large linear complexity to resist Reeds and Sloane algorithm attack effectively.
Option is a promising method to discover the hierarchical structure in reinforcement learning (RL) for learning acceleration. The key to option discovery is about how an agent can find useful subgoals autonomically among the passing trails. By analyzing the agent’s actions in the trails, useful heuristics can be found. Not only does the agent pass subgoals more frequently, but also its effective actions are restricted in subgoals. As a consequence, the subgoals can be deemed as the most matching action-restricted states in the paths. In the grid-world environment, the concept of the unique-direction value reflecting the action-restricted property was introduced to find the most matching action-restricted states. The unique-direction-value (UDV) approach is chosen to form options offline and online autonomically. Experiments show that the approach can find subgoals correctly. Thus the Q-learning with options found on both offline and online process can accelerate learning significantly.