Realization of Deterministic Interleaver Versus Random Interleaver

Here, we propose a novel tree based interleaver (TBI) for generating user specific chip-level interleaving sequences for various users in an IDMA system, which reduces computational complexity drastically. This interleaver also solves the memory cost problem and reduces the amount of information exchange between mobile stations and base stations required to specify the interleaver. Simulation results are presented to show that the proposed TBI perform well as compared to master random and random interleavers in an IDMA scheme.

Millimeter-wave (mmWave) systems have been considered as a promising candidate for 5G networks because of their potential advances in significant bandwidth enhancement. However, due to the extremely high operating frequency of mmWave systems, they generally suffer from severe frequency-selective propagation and nonlinear distortion in the power amplifier, which introduces unfavorable impact on the signal detection process. We discover that for indoor mmWave communications, the constellation of signals becomes much more “clean and tidy” at the receiver side compared with the current wireless systems (e.g., LTE and WiFi), thanks to the channel sparsity characteristic of mmWave communications. Motivated by this observation, we propose in this paper several detection algorithms. Specifically, K-means clustering (KMC) algorithm is first introduced into clustering signal detection due to its advantage in the circle or spherical cluster shape. Then, an improved KMC detector is proposed to avoid the deficiencies of KMC for the error floor and high complexity. Moreover, a density and distance-based clustering detector, a non-iterative algorithm, is proposed and it does not need to preset the number of clusters. The above-proposed algorithms do not require any prior information about the power amplifier and the channel state information at the receiver end, which presents noticeable practical achievements on cost, complexity, and hardware constraints. The simulation results verify the effectiveness of the proposed schemes.

An efficient design of an interleaver is essential to achieve near Shannon capacity performance with turbo codes. The turbo code performance curve can change its slope in low bit error rate (BER) region, if the interleaver is not designed properly. In this paper the design of an interleaver that combines randomization and de-correlation property with regular permutation, and yet guaranties an enlarged minimum distance, is proposed. Our solution to design a deterministic interleaver outperforms the random and semi-random interleavers particularly for the turbo codes with short block length. Distance spectrum distribution and simulation results are presented and compared with well established S -random interleaver.

In this letter, the error-rate (ER) performance of multiple input and multiple output multi-carrier code-division-multiple-access (MIMO MC-CDMA) is evaluated with the aid of cognitive radio network (CRN). MIMO is proven to be useful for high data rate application. MC-CDMA is used to accommodate higher number of user by eliminating channel impairments. CRN is suggested for 5G network to offer higher bandwidth by exposing idle spectrum. Multi-carrier modulation is processed inverse fast-Fourier transform at transmitter and demodulation at each receiver using fast-Fourier transform. Multi-carrier technique is introduced to obtain bandwidth efficiency and overcome the problem of frequency selectivity. At each mobile station, we estimate user’s information using MMSE based iterative algorithm. Further; the system performance is tested using channel encoder. We structure channel encoder using turbo code which is designed with the help of two convolutional encoder. The input information is interleaved using random interleaver and is fed to second convolutional encoder. We create puncturing matrix using input information and output of two convolutional encoders. Then we puncture parity bit information to obtain necessary code rate. Further, we decode and estimate information using iterative decoder which ensure higher performance with lower signal-to-noise ratio. It is vindicated from simulations that CRN based MIMO MC-CDMA system with iterative decoder swell better ER while ensuring higher data rate for downlink signal transmission.

In this contribution, we consider the performance of multiple-input multiple-output (MIMO) code division multiple access (CDMA) system for digital image transmission over correlated Rayleigh fading channel. MIMO system employs multiple antennas both at the transmitter and receiver. In our work, we consider turbo coded system to investigate the performance of MIMO-CDMA system. We realize turbo code using parallel concatenated convolutional encoder and second convolutional encoder is preceded by random interleaver. Further the output of two convolutional encoder are punctured to get rate of ½. At the receiver, we employ iterative decoding algorithm to reduce the effect of multiple access interference (MAI) and Inter-Symbol-Interference (ISI) and median filters to reduce the effect of salt and pepper noise.. In particular, we evaluate the performance of coded MIMO-CDMA system using Zero forcing (ZF) - multi-user detection (MUD) and minimum mean square error (MMSE)-MUD detection technique to mitigate the effects of multi-stream interference (MSI). It is shown through the simulation study that our considered coded MIMO-CDMA system with MMSE algorithm achieves better picture quality with less signal-to-noise ratio (SNR) as compared to ZF MUD.

Motivation: 1.5T CEST is more challenging to recognize tumor regions compared to 3T due to reduced signal-to-noise ratio (SNR) and a coarser spectral resolution. Goal(s): Optimize CEST saturation to maximize the tumor-cNAWM contrast. Approach: Seven-pool Bloch-McConnell equation were utilized to fit 1.5T and 3T CEST data of health volunteers. Parameters were then fine-tuned to match cNAWM and tumor data. Fitted parameters were utilized in simulation under different saturation parameters to maximize tumor-cNAWM contrast. Results: Ten 100ms Gaussian pulses, 95% DC and 1 μT B1 are optimized to generate maximum tumor-cNAWM contrast at 1.5T, subject to practical acquisition time and hardware constraints. Impact: Our optimized CEST saturation parameters maximizes tumor contrast at 1.5T, facilitating the applicability of CEST MRI with reduced SNR and a coarser spectral resolution at 1.5T.

The present work introduces an efficient workflow for AI-enhanced decision-making in Field Development Planning Optimization. Despite the clear importance of uncertainty quantification in decision-making, we find that constraints in time, hardware, and costs are often limiting factors during field evaluation, with the drawback of having a biased uncertainty description or a wrong risk perception. The proposed work encompasses history matching, solution analysis, and production optimization with special emphasis on reducing both simulation and processing time, maximizing what we can call the result per core hour. At the center of our work is an AI-guided optimizer suited to avoid excessive convergence bias and maintain an optimal exploration vs. exploitation performance. The optimizer allows the integration of a multi-objective (MO) formulation in standard history matching and optimization workflows. Despite the flexibility of MO optimization and the vast literature in the energy industry, its usage in real-field cases has always been quite limited due to its formulation availability in commercial software and the increased computation time. This work will show improvement in solution accuracy and formulation flexibility compared to Single Objective (SO) formulations at no increase in runtime. MO is based on the iterative convergence of an efficient frontier from the results generated by the simulation. This same concept has been brought to a user analysis step to allow the identification of best solutions across multiple evaluation workflows, lowering the expertise level for a solution.

Evaluation of mechanisms for anomaly and attack detection is still a challenging task and hard to achieve. This especially holds for the evaluation of the large-scale behavior and efficiency of distributed detection mechanisms. Since testbeds and real networks are no feasible means for large-scale evaluation, we present in this paper a toolchain for the large-scale evaluation of distributed attack detection based on the simulator OMNeT++. Particular focus is placed on simplicity and usability of the toolchain. The interplay of the individual tools is shown by means of an exemplary attack detection. Furthermore, a performance evaluation of the individual tools is presented that shows their limitations in terms of hardware and time constraints.

The Interleave-Division Multiple-Access (IDMA) scheme is known for property of orthogonality amongst its user-specific interleavers. The interleavers are, therefore, referred has the heart of IDMA Transceivers. In the literature, researchers have proposed Tree Based Interleaver (TBI) which not only reduces the bandwidth requirement in comparison to that required in case of Random Interleavers (RI) but also reduces computational complexity drastically when compared with that of power interleavers. The memory requirement at receiver end and its cost problem are also solved by tree based interleaver with further reduction of the amount of information exchange between mobile stations and base stations required to specify the user-specific interleavers. In this paper, the performance analysis of iterative IDMA receivers have been evaluated with BPSK & QPSK modulation techniques employing tree based interleavers and random interleavers in uncoded and rate 1/2 covolutionally coded conditions with Additive White Gaussian Noise (AWGN) channel. During the simulation, it has been observed that IDMA system with QPSK modulation techniques employing TBI outperforms its counterparts optimally.

Even as mobile computing and network computing are gaining momentum, Internet security is sharing the spotlight. Security and authentication on open networks is difficult even without the additional risks posed by wireless media. At the same time, the hardware and software constraints imposed by “small” mobile devices such as PDA’s and smart phones are leading to the pervasive use of application-level proxies to mediate between clients and servers. To date, less attention has been focused specifically on securing the connection between the client and an application-level proxy, independently of any particular service. We describe how to provide secure access to application-level proxied services using an indirect protocol called Charon, which leverages the strong protocol and deployed infrastructure of Kerberos IV. Charon partitions the Kerberos client functionality into a very simple client module that does little more than DES encryption, and a Unix-hosted unprivileged proxy module that runs the remainder of the Kerberos client protocol and can interoperate with other proxy code that provides application-level proxy services. The client module is extremely lightweight and amenable to implementation even on the most modest PDA-class devices. The proxy is trusted as much as any other Kerberized service, but it never learns the user’s Kerberos password or ticketgranting service session key. As a result of this partitioning, clients can continue to enjoy the benefits of proxy-mediated access in a secure way, in addition to gaining secure access to and interoperability with existing Kerberized services.

The generation of huge data with high velocity creates alteration in the distribution of the stream data, which is defined as the concept of drifts. The concept drifts negatively influence the classification accuracy and the stability of the data streams. Numerous machine learning-based models are developed to detect the concept drift in machine learning techniques. Yet, these models are inadequate for real-time applications due to time and memory constraints. Hence, this research devises dynamic streaming data analytics depending on the optimized hybrid deep learning classifier and Optimized Key Windowing (OKW) approach to effectively handle the time and memory constraints. An optimized hybrid deep learning classifier is the base classifier model developed by integrating deep Long short-term memory (LSTM) and deep Recurrent Neural Networks (RNN) to detect the concept drifts in streaming data. The model’s main advantage lies in enhanced accuracy in drift detection by a hybrid classifier due to optimal hyper parameter tuning, which is performed by the proposed intelligent preying algorithm. The other advantage of the proposed model lies in adapting the classifier with varying data patterns, which is performed using OKW. The experimentation is done using the benchmark dataset, such as Apache, Hadoop, Linux, Spark, and Cloud monitoring. The experimental analysis demonstrates the proposed model attaining higher sensitivity, accuracy, specificity, precision, and recall for Hadoop data. This shows a higher result than that of the competent techniques.

Compiling the statistics of large-scale IP address data is an essential task in network traffic measurement. The statistical results are used to evaluate the potential impact of user behaviors on network traffic. This requires algorithms that are capable of storing and retrieving a high volume of IP addresses within time and memory constraints. In this paper, we present two efficient algorithms for collecting the statistics of large-scale IP addresses that balance time efficiency and memory consumption. The proposed solutions take into account the sparse nature of the statistics of IP addresses while maintaining a dynamic balance among layered memory blocks. There are two layers in the first proposed method, each of which contains a limited number of memory blocks. Each memory block contains 256 elements of size 256?8 bytes for a 64-bit system. In contrast to built-in hash mapping functions, the proposed solution completely avoids expensive hash collisions while retaining the linear time complexity of hash-based solutions. Moreover, the mechanism dynamically determines the hash index length according to the range of IP addresses, and can balance the time and memory constraints. In addition, we propose an efficient parallel scheme to speed up the collection of statistics. The experimental results on several synthetic datasets show that the proposed method substantially outperforms the baselines with respect to time and memory space efficiency.

In recently proposed multiple access techniques like IDMA and OFDM-IDMA, the user separation is done by user specific interleavers in contrast to the conventional CDMA scheme, where user separation is assured with user-specific signature sequences. The user specific interleavers must demonstrate minimum probability of collision amongst each other in addition to other merits, including minimal consumption of bandwidth, least hardware for their generation, and least memory requirement. In this paper, the authors propose an interleaver based on prime numbers for the generation of user specific interleavers to remove the problem of high consumption of bandwidth. The simulation results demonstrate the optimal performance of prime interleaver (PI), which is based on prime numbers, apart from other merits in comparison to random and other interleavers.

Everyone in the world always wants to use a better wireless network which provide high capacity and high data rates. High data rates and capacity can be produce by Multiple Input Multiple Output(MIMO) systems. This paper mainly focused on MIMO systems because the MIMO systems have multiple number of antennas in transmitting and receiving end which can produce more data rates and capacity compared to SISO, SIMO, MISO systems. The multiple antennas allow MIMO systems to produce multiplexing gain and diversity gain. The capacity of MIMO systems will be increased by increased the number of antennas in transmitter and receiver end.

Data transmission over the wireless transmission channel is adversely affected by attenuation, distortion, interference, and noise, that hampers the ability of the receiver to correctly receive the transmitted message signal. Thus error detection and correction methods are implemented to mitigate these effects. Convolution encoder is one such channel encoding technique used at transmitter end for deep space and wireless communication whereas at receiver end the Viterbi decoder decodes the encoded data. Viterbi algorithm is based on principles of maximum likelihood where the optimal trellis path is identified that is followed at the encoder using cumulative hamming distance. This paper presents an SoC based Hardware-Software codesign approach of implementing the Viterbi Decoder along with the entire communication system comperising of random binary pattern generator, convolution encoder, QPSK modulator, QPSK demodulator and quantizer built on a Zynq-7000 All Programmable SoC chip. The blocks are designed using Verilog HDL (Hardware Description Language) using the tools Vivado IDE (Integrated Development Environment) 2017.4 by Xilinx and HDL coder toolbox of MATLAB 2018 $a$ by MathWorks. Simulink model of the communication chain is implemented to simulate the design considering the effect of AWGN (Additive White Gaussian Noise) through the channel and then the partial design is translated to be implemented on a Zynq-7000 AP SoC. This design can find potential applications in Satellite communication, SDR (Software Defined Radio).