Biblio

Blind identification of channel codes is crucial in intelligent communication and non-cooperative signal processing, and it plays a significant role in wireless physical layer security, information interception, and information confrontation. Previous researches show a high computation complexity by manual feature extractions, in addition, problems of indisposed accuracy and poor robustness are to be resolved in a low signal-to-noise ratio (SNR). For solving these difficulties, based on deep residual shrinkage network (DRSN), this paper proposes a novel recognizer by deep learning technologies to blindly distinguish the type and the parameter of channel codes without any prior knowledge or channel state, furthermore, feature extractions by the neural network from codewords can avoid intricate calculations. We evaluated the performance of this recognizer in AWGN, single-path fading, and multi-path fading channels, the results of the experiments showed that the method we proposed worked well. It could achieve over 85 % of recognition accuracy for channel codes in AWGN channels when SNR is not lower than 4dB, and provide an improvement of more than 5% over the previous research in recognition accuracy, which proves the validation of the proposed method.

Unmanned Aerial Vehicles (UAVs) are drawing enormous attention in both commercial and military applications to facilitate dynamic wireless communications and deliver seamless connectivity due to their flexible deployment, inherent line-of-sight (LOS) air-to-ground (A2G) channels, and high mobility. These advantages, however, render UAV-enabled wireless communication systems susceptible to eavesdropping attempts. Hence, there is a strong need to protect the wireless channel through which most of the UAV-enabled applications share data with each other. There exist various error correction techniques such as Low Density Parity Check (LDPC), polar codes that provide safe and reliable data transmission by exploiting the physical layer but require high transmission power. Also, the security gap achieved by these error-correction techniques must be reduced to improve the security level. In this paper, we present deep learning (DL) enabled punctured LDPC codes to provide secure and reliable transmission of data for UAVs through the Additive White Gaussian Noise (AWGN) channel irrespective of the computational power and channel state information (CSI) of the Eavesdropper. Numerical result analysis shows that the proposed scheme reduces the Bit Error Rate (BER) at Bob effectively as compared to Eve and the Signal to Noise Ratio (SNR) per bit value of 3.5 dB is achieved at the maximum threshold value of BER. Also, the security gap is reduced by 47.22 % as compared to conventional LDPC codes.

Covert communications enable a transmitter to send information reliably in the presence of an adversary, who looks to detect whether the transmission took place or not. We consider covert communications over quasi-static block fading channels, where users suffer from channel uncertainty. We investigate the adversary Willie's optimal detection performance in two extreme cases, i.e., the case of perfect channel state information (CSI) and the case of channel distribution information (CDI) only. It is shown that in the large detection error regime, Willie's detection performances of these two cases are essentially indistinguishable, which implies that the quality of CSI does not help Willie in improving his detection performance. This result enables us to study the covert transmission design without the need to factor in the exact amount of channel uncertainty at Willie. We then obtain the optimal and suboptimal closed-form solution to the covert transmission design. Our result reveals fundamental difference in the design between the case of quasi-static fading channel and the previously studied case of non-fading AWGN channel.

The design of codes for communicating reliably over a statistically well defined channel is an important endeavor involving deep mathematical research and wide-ranging practical applications. In this work, we present the first family of codes obtained via deep learning, which significantly outperforms state-of-the-art codes designed over several decades of research. The communication channel under consideration is the Gaussian noise channel with feedback, whose study was initiated by Shannon; feedback is known theoretically to improve reliability of communication, but no practical codes that do so have ever been successfully constructed. We break this logjam by integrating information theoretic insights harmoniously with recurrent-neural-network based encoders and decoders to create novel codes that outperform known codes by 3 orders of magnitude in reliability and achieve a 3dB gain in terms of SNR. We also demonstrate several desirable properties of the codes: (a) generalization to larger block lengths, (b) composability with known codes, and (c) adaptation to practical constraints. This result also has broader ramifications for coding theory: even when the channel has a clear mathematical model, deep learning methodologies, when combined with channel-specific information-theoretic insights, can potentially beat state-of-the-art codes constructed over decades of mathematical research.

Cognitive radio is development of wireless communication and mobile computing. Spectrum is a limited source. The licensed spectrum is proposed to be used only by the spectrum owners. Cognitive radio is a new view of the recycle licensed spectrum in an unlicensed manner. The main condition of the cognitive radio network is sensing the spectrum hole. Cognitive radio can be detect unused spectrum. It shares this with no interference to the licensed spectrum. It can be a sense signals. It makes viable communication in the middle of multiple users through co-operation in a self-organized manner. The energy detector method is unseen signal detector because it reject the data of the signal.In this paper, has implemented Simulink Energy Detection of spectrum sensing cognitive radio in a MATLAB Simulink to Exploit spectrum holes and avoid damaging interference to licensed spectrum and unlicensed spectrum. The hidden primary user problem will happened because fading or shadowing. Ithappens when cognitive radio could not be detected by primer users because of its location. Cooperative sensing spectrum sensing is the best-proposed method to solve the hidden problem.

Fifth-generation (5G) network demands of higher data rate, massive user connectivity and large spectrum can be achieve using Sparse Code Multiple Access (SCMA) scheme. The integration of cognitive feature spectrum sensing with SCMA can enhance the spectrum efficiency in a heavily dense 5G wireless network. In this paper, we have investigated the primary user detection performance using SCMA in Centralized Cooperative Spectrum Sensing (CCSS). The developed model can support massive user connectivity, lower latency and higher spectrum utilization for future 5G networks. The simulation study is performed for AWGN and Rayleigh fading channel. Log-MPA iterative receiver based Log-Likelihood Ratio (LLR) soft test statistic is passed to Fusion Center (FC). The Wald-hypothesis test is used at FC to finalize the PU decision.

The design of reliable and secure codes with finite block length is an important requirement for industrial machine type communications. In this work, we develop an autoencoder for the multi-mode fiber wiretap channel taking into account the error performance at the legitimate receiver and the information leakage at potential eavesdroppers. The estimate of the mutual information leakage includes AWGN and fading channels. The code design is tailored to the specific channel setup where the eavesdropper experiences a mode dependent loss. Numerical simulations illustrate the performance and show a Pareto improvement of the proposed scheme compared to the state-of-the-art polar wiretap codes.

This paper designs a re-transmission strategy to intensify the security of communication over the additive white Gaussian noise (AWGN) wire-tap channel. In this scheme, irregular low-density parity-check (LDPC) codes work with reliability-based hybrid automatic repeat-request (RB-HARQ). For irregular LDPC codes, the variable nodes have different degrees, which means miscellaneous protection for the nodes. In RB-HARQ protocol, the legitimate receiver calls for re-transmissions including the most unreliable bits at decoder's outputting. The bits' reliability can be evaluated by the average magnitude of a posteriori probability log-likelihood ratios (APP LLRs). Specifically, this scheme utilizes the bit-error rate (BER) to assess the secrecy performance. Besides, the paper gives close analyses of BER through theoretical arguments and simulations. Results of numerical example demonstrate that RB-HARQ protocol with irregular LDPC codes can hugely reinforce the security performance of the communication system.

Covert communication, i.e., communication with a low probability of detection (LPD), has attracted a huge body of work. Recent studies have concluded that the maximal covert coding rate of the discrete memoryless channels and the additive white Gaussian noise (AWGN) channels is diminishing with the blocklength: the maximum information nats that can be transmitted covertly and reliably over such channels is only on the order of the square root of the blocklength. In this paper, we study covert communication over multiple-input multiple-output (MIMO) AWGN channels. We derive the order-optimal scaling law of the number of covert nats when the maximal covert coding rate of MIMO AWGN channels is diminishing with the blocklength. Furthermore, we provide a comparative discussion for the case in which secrecy and energy undetectability constraints are combined.

Spreading code assumes an indispensable work in WCDMA system. Every individual client in a cell is isolated by an exceptional spread code. PN grouping are commonly utilized in WCDMA framework. For example, Walsh codes or gold codes as spread code. Data received from WCDMA are transmitted using chaotic signal and that signal is generated by using logistic map. It is unsuitable to be utilized as spreading sequence. Using a threshold function the chaos signal is changed in the form of binary sequence. Consequently, QPSK modulation techniques is analyzed in W-CDMA downlink over Additive white Gaussian noise channel (AWGN) and Rayleigh multipath fading channel. The activity was assessed with the assistance of BER contrary to SNR utilizing parameters indicating the BER in low to high in SNR.

In practical wireless communication systems, the channel conditions of legitimate users can not always be better than those of eavesdroppers. This realistic fact brings the challenge for the design of secure transmission over wiretap channels which requires that the eavesdropping channel conditions should be worse than legitimate channels. In this paper, we present a robust chaos-based information masking polar coding scheme for enhancing reliability and security performances under realistic channel conditions for practical systems. In our design, we mask the original information, wherein the masking matrix is determined by chaotic sequences. Then the masked information is encoded by the secure polar coding scheme. After the channel polarization achieved by the polar coding, we could identify the bit-channels providing good transmission conditions for legitimate users and the bit-channels with bad conditions for eavesdroppers. Simulations are performed over the additive white Gaussian noise (AWGN) and slow flat-fading Rayleigh channels. The results demonstrate that compared with existing schemes, the proposed scheme can achieve better reliability and security even when the eavesdroppers have better channel conditions than legitimate users, hence the practicability is greatly enhanced.

We propose a coding scheme for covert communication over additive white Gaussian noise channels, which extends a previous construction for discrete memoryless channels. We first show how sparse signaling with On-Off keying fails to achieve the covert capacity but that a modification allowing the use of binary phase-shift keying for "on" symbols recovers the loss. We then construct a modified pulse-position modulation scheme that, combined with multilevel coding, can achieve the covert capacity with low-complexity error-control codes. The main contribution of this work is to reconcile the tension between diffuse and sparse signaling suggested by earlier information-theoretic results.

In this paper, we address the stability problem of a noiseless linear time invariant control system over parallel Gaussian channels with feedback. It is shown that the eigenvalues-rate condition which has been proved as a necessary condition, is also sufficient for stability over parallel Gaussian channels. In fact, it is proved that for stabilizing a control system over the parallel Gaussian channels, it suffices that the Shannon channel capacity obtained by the water filling technique is greater than the sum of the logarithm of the unstable eigenvalues magnitude. In order to prove this sufficient condition, we propose a new nonlinear joint source channel coding for parallel Gaussian channels by which the initial state is transmitted through communication steps. This coding scheme with a linear control policy results in the stability of the system under the eigenvalues-rate condition. Hence, the proposed encoder, decoder and controller are efficient for this problem.

-In wireless networks, Cooperative communication can be used to increase the strength of the communication by means of spatial diversity. Basic idea that exists behind Cooperative communication is, if the transmission from source to destination is not successful, a helping node called relay can be used to send the same information to the destination through independent paths. In order to improve the performance of such communication, channel coding techniques can be used which reduces the Bit Error Rate. Previous works on cooperative communication only concentrated on improving channel capacity through cooperation. Hence this paper presents different Channel coding methods such as Turbo coding, Convolutional coding, and low-density parity-check coding over Rayleigh fading channels in the presence of Additive white Gaussian noise. Performance of these Channel coding techniques are measured in terms of noise power spectral density (NO ) vs. Bit error rate.

The existing radial topology makes the power system less reliable since any part in the system failure will disrupt electrical power delivery in the network. The increasing security concerns, electrical energy theft, and present advancement in Information and Communication Technologies are some factors that led to modernization of power system. In a smart grid, a network of smart sensors offers numerous opportunities that may include monitoring of power, consumer-side energy management, synchronization of dispersed power storage, and integrating sources of renewable energy. Smart sensor networks are low cost and are ease to deploy hence they are favorable contestants for deployment smart power grids at a larger scale. These networks will result in a colossal volume of dissimilar range of data that require an efficient processing and analyzing process in order to realize an efficient smart grid. The existing technology can be used to collect data but dealing with the collected information proficiently as well as mining valuable material out of it remains challenging. The paper investigates communication technologies that maybe deployed in a smart grid. In this paper simulations results for the Additive White Gaussian Noise (AWGN) channel are illustrated. We propose a model and a communication network domain riding on the power system domain. The model was interrogated by simulation in MATLAB.

We consider the problem of covert communication over a state-dependent channel, where the transmitter has non-causal knowledge of the channel states. Here, “covert” means that the probability that a warden on the channel can detect the communication must be small. In contrast with traditional models without noncausal channel-state information at the transmitter, we show that covert communication can be possible with positive rate. We derive closed-form formulas for the maximum achievable covert communication rate (“covert capacity”) in this setting for discrete memoryless channels as well as additive white Gaussian noise channels. We also derive lower bounds on the rate of the secret key that is needed for the transmitter and the receiver to achieve the covert capacity.

The main challenge of ultra-reliable machine-to-machine (M2M) control applications is to meet the stringent timing and reliability requirements of control systems, despite the adverse properties of wireless communication for delay and packet errors, and limited battery resources of the sensor nodes. Since the transmission delay and energy consumption of a sensor node are determined by the transmission power and rate of that sensor node and the concurrently transmitting nodes, the transmission schedule should be optimized jointly with the transmission power and rate of the sensor nodes. Previously, it has been shown that the optimization of power control and rate adaptation for each node subset can be separately formulated, solved and then used in the scheduling algorithm in the optimal solution of the joint optimization of power control, rate adaptation and scheduling problem. However, the power control and rate adaptation problem has been only formulated and solved for continuous rate transmission model, in which Shannon's capacity formulation for an Additive White Gaussian Noise (AWGN) wireless channel is used in the calculation of the maximum achievable rate as a function of Signal-to-Interference-plus-Noise Ratio (SINR). In this paper, we formulate the power control and rate adaptation problem with the objective of minimizing the time required for the concurrent transmission of a set of sensor nodes while satisfying their transmission delay, reliability and energy consumption requirements based on the more realistic discrete rate transmission model, in which only a finite set of transmit rates are supported. We propose a polynomial time algorithm to solve this problem and prove the optimality of the proposed algorithm. We then combine it with the previously proposed scheduling algorithms and demonstrate its close to optimal performance via extensive simulations.