The Age of Information (AoI) is a critical metric in next-generation communication networks, quantifying data freshness essential for latency-sensitive applications in 6G systems, such as autonomous driving and industrial IoT. This paper presents an AoI analysis within a downlink full-duplex (FD) cooperative simultaneous wireless information and power transfer (SWIPT) system, employing rate-splitting multiple access (RSMA) for short packet communication to enhance timely data updates. By integrating RSMA with SWIPT and FD capabilities, we propose a robust framework to reduce the AoI. In this regard, closed-form expressions of the average block error rate of the RSMA-enabled FD cooperative SWIPT system are derived and validated via Monte Carlo simulations. The results demonstrate that RSMA outperforms non-orthogonal multiple access (NOMA) and FD cooperative SWIPT NOMA in terms of error performance, while also reducing the inherent system design complexity. Our findings reveal that RSMA is a promising approach for minimizing AoI across various system configurations, offering valuable insights for designing future 6G networks that prioritize low latency, high reliability, and data freshness.

Due to the rapid development of the Internet of Things (IoT) applications, which generate vast volumes of data at high speeds, security and privacy issues have become challenging. IoT devices use the advanced encryption standard algorithm before transmission. However, since the system communicates through amplify-and-forward relays, the data may be leaked through the untrusted relays. Depending on a mathematical tool may affect the data security vulnerability. Thus, to enhance the system security, physical layer security is used to transmit a jamming signal to confuse the untrusted relay nodes. Hence, the proposed framework ensures security by combining the physical and data layer security which comes with a cost regarding system complexity, system latency, and energy consumption. The proposed framework addresses the joint problem of physical and data layer security, multicell association, task offloading, users’ power allocation, and service caching in multitier communication and edge computing networks. The objective is to minimize the system latency and energy consumption under the secrecy capacity constraint. Due to the NP-hard nature of the joint problem, we use a low-complexity Lyapunov drift-plus-penalty optimization technique based on the Gibbs sampling algorithm. The simulation results demonstrate the proposed framework’s superiority over the state-of-the-art in terms of high secrecy capacity and low computational complexity. When the secrecy capacity threshold increases, the secrecy capacity is enhanced by approximately 5.72%, while the system latency and energy consumption increase by 38.18% and 69.99%, respectively, compared to the literature.

Recently, serverless computing, particularly the Function-as-a-Service (FaaS) programming model, has become an important emerging technology for developers and cloud providers. It relieves developers from the burden of explicitly managing the computing resources and provides more accurate billing of the exact service and execution time. However, as a side effect of the service-side resource management, the system often inactivates a set of execution dockers, introducing a significant cold start time for the following invocation, resulting in unpredictable latency. Existing solutions mainly rely on improving an end-point management policy or scheduling into other same-tier endpoints and, more recently, considering a promising but simplified edge-cloud tier with available management information. The latter can mitigate latency by relying on offloading to a resource-rich cloud. In this paper, we consider extending the two-tier edge-cloud approach to not rely on any management information from the service side or account for function-dependent communication latency but to rely on a scheduler based on the multi-armed bandit (MAB) upper confidence bound (UCB) algorithm that dynamically learns from the prevailing real-time conditions to choose the best cloud platform to execute functions with minimal latency. A test bed was implemented, comprising an OpenWhisk system deployed on a local Kubernetes cluster (kind) and two commercial FaaS systems: Amazon Web Services (AWS) Lambda and Google Cloud Functions (GCF). The scheduler was tested in real-time using the serverless benchmark suite (SeBS). Our results show that the MAB UCB is superior to single-tier systems. The MAB UCB can achieve execution time within a close margin of an oracle scheduler and also can fail in some extreme cases.

This paper presents a performance analysis of the Age of Information ($A$oI) in full-duplex (FD) cooperative (C) non-orthogonal multiple access (NOMA) integrated with simultaneous wireless information and power transfer (FD-C-SWIPT-NOMA). Particularly, approximated closed-form expressions for the average block error rate (BLER) are derived for the proposed scheme and validated by Monte Carlo simulations. Based on the derived expressions of the average BLER, the expected weighted sum of AoI (EWSAoI) is obtained for the proposed scheme. Additionally, the proposed scheme is compared with other baseline schemes such as orthogonal multiple access (OMA), NOMA, and half duplex (HD) cooperative SWIPT NOMA schemes in terms of EWSAoI. Finally, the effect of variable power allocation, distances, and residual self-interference (RSI) on the AoI is analysed and simulation results demonstrate the superiority of the proposed scheme compared to baseline schemes in terms of the EWSAoI.

Accurate channel estimation is crucial for the proper operation of reconfigurable intelligent surfaces (RIS). This paper introduces a convolutional neural network (CNN) approach for multi-user RIS channel estimation that incorporates the zero-shot noise-to-noise (N2N) methodology within its architecture. In contrast to techniques that rely on clean training data, the proposed method learns from the noisy data itself to figure out how to remove the noise. The proposed zero-shot N2N self-learning demonstrates improved performance and a fast convergence rate in the RIS channel estimation.

Due to the wide range of intensive computational applications and ubiquitous connectivity of the Internet of Things (IoT) paradigms, it has become crucial to develop a new platform that can achieve low delay, high network throughput, and enhanced quality of service (QoS). This paper proposes a joint user association, service caching, and task offloading strategy to reduce delay and enhance users’ QoS in multi-tier communication and multi-tier edge computing heterogeneous networks (HetNets). The considered system model consists of multi-users with different tasks and service data sizes communicating in a heterogeneous network of one massive multiple-input multiple-output (M-MIMO) macro base station and some small base stations. The proposed work investigates user association, power allocation , optimum service data caching, and task offloading strategies at the computing network edges. Thereby, the objectives of this work are to propose an efficient framework to reduce the system delay, increase the network throughput, and meet the user requirements in multi-tier communication and multi-tier edge computing heterogeneous networks . The simulation results show that the proposed algorithm outperforms the state-of-the-art with a 49.48% decrease in system delay, 80% reduction in cost and hardware complexity in terms of the reduced number of installed antennas, and 48.58% enhancement in the network throughput.

In vehicular communications environments, channels are characterized as dynamic and highly mobile. As uch, estimating the vehicular communication channel with a massive number of antennas installed at the transmitter and receiver is considered a daunting task for conventional estimators and deep-learning approaches. Classical estimators provide inaccurate estimation results, and the deep learning algorithms require a huge dataset for training the model. This paper proposes a transfer learning and meta-learning approach for channel estimation in outdoor vehicular environments with millimeter-wave transmission frequencies above 6 GHz. The proposed system learns a good initialization of the model weight parameters using a few samples and a small number of gradient steps to achieve model convergence. Simulation results show that the proposed algorithm outperforms the conventional least square estimator in the outdoor millimeter-wave vehicular environments.

The integration of unmanned aerial vehicles (UAVs) into wireless networks is gaining significant attention in the fifth generation (5G) and beyond technologies. The use of UAVs has the potential of expanding coverage and providing efficient and reliable communication services, especially in remote and inaccessible areas. While other studies have explored sum-rate maximization via user pairing considering a multi-armed bandit (MAB) for a single UAV, the use of MAB in multiple UAVs especially under non-orthogonal multiple access (NOMA) scheme is not fully explored. This paper presents an algorithm for user pairing targeting sum-rate maximization of multi-UAV NOMA networks by applying multi-agent bandits that employ two-sided matching. The proposed method performs user association and power allocation with no coordination among the UAVs. The simulation results demonstrate the superior performance of the proposed method which is very close to that achieved by exhaustive search and outperforms random matching.

This paper investigates the performance of joint uplink-downlink communication of intelligent reflecting surface (IRS) assisted non-orthogonal multiple access (NOMA) network. Unlike most existing works that considered time-division duplexing (TDD) system to exploit the IRS uplink-downlink channel reciprocity, we adopt frequency-division duplexing (FDD) to achieve a fair trade-off between the signal reception reliability and system spectral efficiency. We analyze the system outage probability and outage-throughput, and derive their associated performance bounds in closed-form expressions. Moreover, for the second user in decoding order, we formulate two optimization problems over the IRS elements phase-shifts. The first optimization problem aims to maximize the minimum SNR in the uplink and downlink and the second optimization problem targets maximizing both SNRs. We employ genetic algorithms (GA) to solve the two problems. Monte-Carlo simulations are applied to validate the analytically driven bounds and to compare between the solutions of the proposed optimization problems.

Intelligent reflecting surface (IRS) can play a major role in relaying data in 6G networks. This paper studies the system performance of IRS selection (IRS-S) for both non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA). We first present two selection schemes in NOMA scenario for IRS relays, namely, the two-stage and the max-min IRS-S strategy. Then, we drive a closed-form expression for the system outage probability for the two-stage IRS-S scheme and the OMA scenario. In addition, simulation results are provided to validate the analytically driven expressions of the outage probability for the proposed schemes. The results confirm that the two-stage IRS-S strategy has superior performance compared to all selection schemes in both NOMA and OMA setups.

This paper studies the precoding of a heterogeneous network composed of a massive multi-input-multi-output macro base station overlaid with several small cells. The objective of the precoding problem is maximizing the total sum rate of all users while limiting the total system power to a certain pre-defined acceptable threshold. A distributed approach is proposed for the problem and is studied for the case when the cross channels are known and when they are not. The distributed precoding is then compared against a centralized approach. Numerical results asses the performance of the two schemes in terms of the total system throughput under different conditions.

In this paper, a cross-tier interference mitigation framework based on interference alignment and coordinated beamforming (IA-CB) is investigated for downlink non-orthogonal multiple access (NOMA) heterogeneous networks (HetNets). In the proposed technique, named cross-tier IA-CB (CrIA-CB), the conventional IA-CB is extended to eliminate the cross-tier interference between the macro cell (MC) and the underlaid small cells (SC) in HetNets. The proposed CrIA-CB utilizes the degrees of freedom provided by the massive multiple input multiple output (mMIMO) technology for designing the transmit and receive beamforming vectors to null the cross-tier interference at the user side while decreasing the sharing channel state information (CSI) between SCs and MC. Simulation results validate the performance improvement of the proposed technique in terms of system sum rate over the conventional techniques.

Properly designing scattered pilot pattern over orthogonal frequency division multiplexing (OFDM) subcarriers is important to improve the accuracy of the sparse channel estimation, while decreasing the number of the required pilots. In this paper, we propose a pilot-subcarrier allocation scheme that optimizes the pilot subcarrier patterns without any knowledge of channels, where channel estimation is done by interpolating the pilot-subcarriers scattered over frequency domain. The proposed pilot allocation scheme utilizes the mutual incoherence property (MIP) of the compressive sensing (CS) theory to formulate the pilot allocation problem into infinity norm problem. Then, MIP based weighted fast iterative shrinkage-thresholding algorithm (MIP-WFISTA) is proposed to solve the formulated problem. Simulation results validate that, compared with the conventional techniques, the proposed pilot design scheme achieves more accurate channel estimation and as a result better bit error rate (BER) performance while decreasing the number of the required pilots in frequency-selective fading environments.

In this paper, we investigate the mutual mobility effect for independent multiple primary users and numerous secondary users on the spectrum sensing capability of secondary users. The proposed system model adopts two different mobility models; namely, the random walk (RWL) model and random waypoint (RWP) model with varying numbers of primary mobile users and secondary mobile users in the network. Closed-form expressions for the detection capability and normalized sensing capacity were derived. The results show that the increase in the number of independent mobile users following a random mobility model has an impact on the total sensing capacity over unoccupied bands.

Spectrum sensing task is an essential operation in cognitive radio networks. As such, this paper considers the impact of primary mobile users whose location and dynamic spectrum use can be tracked and sensed by collaborative secondary users. We evaluate the probability of detection for two simplistic random mobility models by acquiring compressed signal measurements at the fusion center. The collaborative signal acquisition is to reduce the computation at the secondary user side. Combined with the recovered signal via compressed sensing, a localization technique by Kalman filtering is used to track a primary mobile user in the network region. For a random mobility model, it is shown that better spectrum sensing performance can be achieved with a high probability of detection. Further, for pseudo-static movements having pause time factor, the detection performance increases. Simulation results are given to corroborate the approach used in the evaluation.

The introduction of cognitive radio technology in licensed 5G networks could significantly enhance the overall system capacity and number of served users. In this context, this work discusses the admission of new cognitive radio secondary users (SUs) in the fifth generation (5G) Heterogeneous Networks (HetNets) as well as the allocation of the channels over the secondary cognitive network, using a many-to-one matching game and auctions theory. Simulation results show that the used matching algorithm for users admission is of low complexity as well as the existence of a Walrasian equilibrium point for the channels allocation problem.

This paper investigates the secrecy performance of a multiuser system that utilizes transmit antenna selection scheme at the base station and adopts threshold-based selection diversity opportunistic scheduling over legitimate nodes. The legitimate transmission suffers from the presence of noncolluding (Non-Col) and colluding (Col) multiple passive eavesdroppers. Both the legitimate and eavesdropping nodes are assumed to suffer from co-channel interference (CCI) signals from independent channels that follow Rayleigh fading. Closed-form expressions for the probability density functions and cumulative density functions of the end-to-end signal-to-interference-plus-noise ratio for both eavesdropping scenarios in the presence of CCI signals are derived. In addition, closed-form expressions for the network secrecy outage probability (SOP) for Non-Col/Col scenarios are derived. At the high signal-to-noise ratio values, closed-form expressions for the asymptotic secrecy outage probabilities are obtained. Following this obtained asymptotic analysis, an optimization problem for power allocation is formulated and solved to improve the secrecy performance of the network by minimizing the asymptotic SOP for both Col and Non-Col cases. The derived analytical expressions are then validated using both simulations and numerical results.

This paper presents the application of a particular class of matching game models, namely the Boston school choice approach, to the problem of user association in heterogeneous networks. Simulation results are provided to assess the performance of the Boston mechanism and the Gale-Shapley algorithm used in the college admission game concerning overall performance, average user utility and execution time.

This paper studies the rate maximization problem of a heterogeneous network composed of a massive multi-input-multi-output macro base station overlaid with small cells. The rate maximization is formulated as an optimization problem that maximizes the total sum rate of all users while maintaining an acceptable quality of service per user and total system power threshold. Numerical results show that the presence of small cells improves the total sum rate compared to relying on the macro base station alone under the same total system power constraint.

Non-Orthogonal Multiple Access (NOMA) is a promising multiple access technique for the 5thgeneration (5G) mobile communication systems. In this paper, an energy efficient power allocation scheme is derived for a general number of users per cluster multiple input multiple output (MIMO) downlink NOMA system. The proposed scheme idea is based on converting the difficult energy efficiency power allocation problem into an equivalent spectral efficiency power allocation problem and then dividing this equivalent problem into multiple simple cluster sum rate maximization problems. In addition, a user clustering scheme is proposed to maximize the NOMA system energy efficiency by first designing the detection vectors to convert the MIMO users channel matrices into their equivalent channel vectors and distribute the users on the clusters based on their equivalent channel gains. The users are clustered such that those with the largest equivalent channel gains are selected as cluster heads and the rest of users in each cluster are selected to maximize the equivalent channel gain difference between each other. Numerical results show that the proposed framework improves the system energy efficiency of the MIMO NOMA system at different values of the total transmit power, the minimum required date rates, the number of users per cluster and at different users' distance distribution scenarios.

This paper investigates the secrecy performance of a multiuser threshold-based transmit antenna selection scheme (TAS/tSD) in the presence of co-channel interference (CCI) signals and the existence of a passive eavesdropping node. In particular, an exact closed-form expression for the secrecy outage probability is derived. Then, the asymptotic secrecy outage closed-form expression is obtained at the high signal-to-noise ratio (SNR) values. Based on the obtained asymptotic analysis, an optimization problem for power allocation is formulated and solved to improve the secrecy performance of the system concerning minimizing the asymptotic secrecy outage probability. Numerical and simulation results are obtained to justify the obtained analysis.

In this study, downlink channel estimation of three‐dimensional massive multiple‐input multiple‐output (3D‐MIMO) system operating in the frequency division duplexing (FDD) mode is considered. Inspired by the channel sparsity property, this study proposes a compressive sensing algorithm to exploit the channel sparsity structure in the angle–time domain. The proposed algorithm, named AMP‐ADM, combines the multiple approximate message passing (M‐AMP) algorithm with the alternative direction of multiplier (ADM) technique to efficiently exploit the sparsity structure of the 3D massive MIMO channel. First, the proposed AMP‐ADM is implemented in the case of the conventional estimation for the FDD protocol where the channel is estimated individually at each user equipment. Then, building on this algorithm, a low complexity feedback AMP‐ADM‐T scheme at the transmitting base station (BS) side is proposed. In the proposed feedback AMP‐ADM‐T technique the users' channels are jointly estimated at the BS to fully exploit the common sparsity basis. Complexity and convergence analyses are provided for both the AMP‐ADM and feedback AMP‐ADM‐T algorithms. Simulation results prove the improved performance of the proposed feedback AMP‐ADM‐T algorithm compared to different state‐of‐the‐art joint channel estimation techniques.

Fitting the huge number of pilots needed for massive multiple inputs multiple outputs antennas (MIMO) channel estimation within the available time and frequency resources is a challenging problem. Generally, compressed sensing (CS) channel estimation algorithms face the dilemma of trading off the estimation accuracy and the computational complexity. In this paper, we propose a weighted fast iterative shrinkage thresholding algorithm (W-FISTA). The proposed algorithm provides higher estimation efficiency with the same complexity as the original FISTA. With low computational complexity, multiple measurement vectors (MMV) version of the W-FISTA is proposed to estimate the 3D massive MIMO channel. The proposed MMV-WFISTA estimate the channel coefficients by exploiting its joint sparsity structure in the angle-delay sparse domain. The complexity analysis and the simulation results indicate a clear improvement in the performance of the proposed MMV-WFISTA over joint estimation algorithms.

Sensing the spectrum is the central operation to enable cognitive users to identify the spectrum occupancy. Wideband spectrum sensing enables detecting occupancy at different bands. In this paper, we propose a wavelet based multitask compressive sensing (WMCS) algorithm for constructing the spectrum edges directly from the compressive measurement. The WMCS algorithm forms new compressive sensing (CS) tasks by using the wavelet transform of the power spectral density at different scales. The algorithm then forms the spectrum subbands using the estimated edges and classifies the subbands as either occupied or sparse. Simulation results show the improved performance of the proposed algorithm.

Exploiting the full benefits of massive multiple input multiple output (MIMO) technology can be directly affected by the efficiency of the channel state information (CSI) estimation. This paper focuses on frequency division duplexing (FDD) channel estimation and feedback. To the sparsity of the frequency selective massive MIMO channels, a two-step multiple approximate message passing algorithm is developed in the angle-time domain. The angle-time domain can efficiently capture the essential degrees of freedom. Consequently, it is capable of representing the channel with the minimum number of coefficients. The proposed technique for the channel estimation and feedback is divided into two main stages. The first stage is concerned with retrieving the positions of the nonzero time domain dominant taps, while the second stage focuses on estimating the channel coefficients at these taps through exploiting the common and individual sparsity pattern that appears in the angle-time domain using the proposed M-AMP algorithm. Simulation results demonstrate the improved performance of the proposed framework.

High quality videos became an essential requirement in recent applications. High efficiency video coding (HEVC) standard provides an efficient solution for high quality videos at lower bit rates. On the other hand, HEVC comes with much higher computational cost. In particular, motion estimation (ME) in HEVC, consumes the largest amount of computations. Therefore, fast ME algorithms and hardware accelerators are proposed in order to speed‐up integer ME in HEVC. This study presents a fast centre search algorithm (FCSA) and an adaptive search window algorithm (ASWA) for integer pixel ME in HEVC. In addition, centre adaptive search algorithm, a combination of the two proposed algorithms FCSA and ASWA, is proposed in order to achieve the best performance. Experimental results show notable speed‐up in terms of encoding time and bit rate saving with tolerable peak signal‐to‐noise ratio (PSNR) quality degradation. The proposed fast search algorithms reduce the computational complexity of the HEVC encoder by 57%. This improvement is accompanied with a modest average PSNR loss of 0.014 dB and an increase by 0.6385% in terms of bit rate when compared with related works.

This paper proposes an approach of compressed sensing (CS) of video in which distributed video coding DVC and CS are integrated as in [1], and the sensing matrix is modulated in suit of [2] but with proposed fixed weighting strategy to certain DCT coefficients in an effort to improve the visual quality of reconstruction.

In this paper, we propose using different quantization values, including 1-bit compressed sensing for perceptual audio signal compression in perceptual systems[1], in order to clarify the effect of the quantization process on the achievable quality of audio signal. Simulations results show that reasonable performance is achieved for different quantization CS compared to quantized classical CS.

The innovative concept of Compressed Sensing (CS) presents a breakthrough that enables the acquisition of sparse signals at much lower sampling rates compared to the conventional Nyquist rate. The scope of CS is not limited only to sparse signal but it is also applicable to compressible signals, such as multimedia signals including audio signals. Representing the random samples from CS process using finite-precision is a crucial problem in communication systems. In this paper, we focus on 1-bit quantized CS. We propose to take into account the perceptual CS model for audio compression, where the perceptual properties are taken into account. We propose two models, the first applies perceptual effect at the transmitter side. In the other model, a modified Binary Iterative Hard Thresholding (BIHT) is proposed to improve the performance of 1-bit compressed sensing by taking the perceptual properties of the received audio signal into account. The Mean Opinion Score (MOS) is used to compare the perceptual quality of the received signal for the proposed 1-bit perceptual CS algorithms. Simulation results show that a better performance is achieved using the proposed algorithms.

Analog network coding has been successfully employed in conventional two way relaying systems. However, analog two way relaying is successful when the two sender and destination nodes are symmetric. In practical cellular-based mobile communication systems of the likes of Long Term Evolution (LTE), the system is composed of several powerful base-stations and low power users' equipment. Applying classical two way relaying will cause unbearable interference. In this paper we propose applying network coding between two user equipment in the same cell in the uplink direction. We investigate Single Input Single Output (SISO) and Multiple Input Multiple Output (MIMO) models for this scenario. We also study the effect of interference of other nodes in surrounding cells.

In this paper, we apply an efficient scaling strategy on layered scaled min-sum LDPC decoder. In addition, we propose a joint optimization strategy for the quantization and scaling parameters of layered scaled min-sum LDPC decoder. The study of our optimization results, for DVB-S2 LDPC codes with different constellation sizes and code rates, shows that each constellation size code rate pair has different optimal scaling and quantization parameters. In order to maximize the achievable performance, we propose using the different scaling and quantization parameters for each constellation size code rate pair. The simulation results show the performance improvement of separately using optimal scaling parameters or optimal quantization parameters, and the overall performance enhancement of using both optimal scaling and quantization parameters.

Interference alignment (IA) emerged on the communication scene as a solution to the interference problem in all interference-limited networks, including heterogeneous cellular systems. However, the performance of IA is greatly related to the accuracy of the channel state information at transmitters (CSIT), namely the number of feedback bits. Accordingly, in order to improve the performance of IA, it would be useful to analyze the number of feedback bits with respect to the sum rate loss. Motivated by that, this paper studies a limited feedback-based IA scheme suitable for two tier macrocell-femtocell heterogeneous networks. First, an approximate analytical expression for the upper bound on the total sum rate loss due to limited feedback in the studied IA system, is derived. Then, a simulation based evaluation of the sum-rate loss due to the implementation of limited feedback IA in heterogeneous networks is obtained. Simulation results confirmed the severe effect of quantization of CSI on the interference alignment performance.

Coordinated multipoint (CoMP) is one technique that can be used to extend the coverage area by solving the Inter Cell Interference (ICI) problem occurring at the cell-edge. On the other hand, Distributed Space-Time/Frequency Block Coding (DSTBC/DSFBC) technique is used to improve the reliability in large modern wireless networks e.g. 3GPP LTE advanced. The cell-edge user is usually power limited. Hence, reducing processing load in such a terminal is preferable. In this paper, we propose a low complexity decoding method named Adaptive K-Best Sphere Decoder (AKBSD) to serve a high mobility cell edge user facing different varying frequency selective channels from multiple base stations. AKBSD adapts the number of K-paths the decoder processes while performing the tree search depending on the estimated received signal strengths and the channel quality of each transmission link in a DSFBC open-loop CoMP environment. The simulation results confirm a good trade-off between performance and complexity achieved by AKBSD under this scenario where about 20% reduction in complexity is achieved over a high K-value fixed KBSD for the price of 0.4 dB reduction in performance at BER value of 10-4.

In this paper, we propose a novel low complexity scaling strategy of min-sum decoding algorithm for irregular LDPC codes. In the proposed method, we generalize our previously proposed simplified Variable Scaled Min-Sum (SVS-min-sum) by replacing the sub-optimal starting value and heuristic update for the scaling factor sequence by optimized values. Density evolution and Nelder-Mead optimization are used offline, prior to the decoding, to obtain the optimal starting point and per iteration updating step size for the scaling factor sequence of the proposed scaling strategy. The optimization of these parameters proves to be of noticeable positive impact on the decoding performance. We used different DVB-T2 LDPC codes in our simulation. Simulation results show the superior performance (in both WER and latency) of the proposed algorithm to other Min-Sum based algorithms. In addition to that, generalized SVS-min-sum algorithm has very close performance to LLR-SPA with much lower complexity.

The multiple access broadcast (MABC) is an effective two phases transmission (2P) ANC protocol for the half-duplex (HD) communication mode. However, MABC does not make use of channel gain of the direct link (DL) no matter how strong it is. On the other hand, the time division broadcast (TDBC) is known as a three phases transmission (3P) protocol which enables transceivers to utilize DL and thus offers the possibility to achieve higher performance compared with the MABC at the expense of a reduced spectral efficiency due to the one extra transmission phase. In this paper, we investigate a joint single relay selection and power allocation schemes for energy-efficient wireless communication systems with analog network coding (ANC) for TDBC, where two-way relay channel with two end nodes and N parallel relay nodes is considered under an assumption of perfect channel-state information (CSI). Our objective is to minimize the total system transmit power consumption under quality-of-service (QoS) constraints for TDBC protocol with joint single relay selection and nodes power allocation. In addition, a zero-forcing based relay signal combining technique that combines the signals received at the 1st and 2nd transmission phases, also known as zero-forcing relay power allocation (ZF-RPA), is also investigated. Numerical simulation shows that the traditional VG-RPA is more energy-efficient than the ZF-RPA scheme for TDBC in cases with and without utilizing DL.

In this paper, two BCH soft decoders are proposed suitable for high rate codes with medium to large word length. The proposed decoders provide a programmable performance gain, with a reduced critical path allowing for an increase upto m/2 times the operating frequency of algebraic decoders, where m is the Galois field size. Our proposed decoders operate only on the least reliable bits, which leads to a reduction in the decoder complexity by removing the Chien search procedure.

Underlay cognitive radio (CR) permits unlicensed secondary users (SUs) to transmit their own data over the licensed spectrum unless the interference from the SUs on the licensed primary user (PU) exceeds an acceptable level. This paper proposes two interference alignment (IA)-based distributed optimization designs for multiple secondary transceivers in underlay cognitive radio case with channel uncertainty. The precoding and power allocation matrices for each SU are either independently or jointly optimized for imperfect channel knowledge to maximize the secondary rates and to control the secondary interference on the primary receiver to be below the acceptable limit that is determined by the primary receiver. Numerical results prove the ability of the proposed methods to support significant secondary rates and to protect the PU from extra interference, within the acceptable primary range, even in presence of channel uncertainty case. In addition, joint optimization design has higher secondary performance than the independent optimization design.

New interference alignment (IA) design for secondary users' transceivers in multi-secondary users cognitive radio system is presented. The proposed scheme allows opportunistic transmitters (secondary users) equipped with multiple antennas (MIMO) to use the same frequency band already occupied by the pre-existing primary user. This can be done by aligning and cancelling the interference on the primary user when local channel state information (CSI) is known. A novel closed form solution for precoding, postcoding, power allocation designed for the secondary users is derived. The proposed solution eliminates the interference of secondary users on primary user while whitening the interference caused by the primary user and other secondary users on each secondary user in the system. Numerical results prove the effectiveness of our novel IA scheme compared to other iterative methods that are used for the same purpose.

Cognitive radio (CR) permits multiple unlicensed users, secondary users, to share the same channels with the licensed users, primary users, without affecting the latter performance. The main challenge resides in the design of the secondary users' transceivers (precoding, postcoding, and power allocation) such that both the primary and secondary receivers enjoy satisfactory quality of service (QoS). In this paper, two novel methods are proposed for designing secondary users' postcoding and power allocation matrices. The proposed methods consider both perfect and imperfect channel state information (CSI) in the design. The proposed techniques align the interference at the primary user's receiver in a direction orthogonal to the primary desired signal space by using interference alignment (IA). On the other hand, postcoding and power allocation matrices are chosen such that they maximize secondary users' rates by whitening the interference at secondary user's receiver, while satisfying the interference constraint on the primary user's receiver in case of channel imperfection. Numerical results show the ability of the proposed methods to allow multiple secondary users to transmit without degrading the primary user's performance and to maximize their rates in case of channel uncertainty.

This paper considers a modified iterative version of the two-stage hybrid algorithm for decoding low-density parity- check (LDPC) codes. The hybrid-decision scheme is a decoding scheme used that combines two iterative decoding algorithms for decoding LDPC codes. This scheme is suitable for many applications such as audio and video transmission that are sensitive to time. The hybrid-decision scheme mixes between the characteristics of the soft-decision decoding scheme and the hard- decision decoding scheme to reduce the computational complexity of the whole decoding algorithm. The modification proposed in this paper is applied to the implementation-efficient reliability ratio weighted bit-flipping (IERRWBF) algorithm which represents hard-decision scheme in the hybrid algorithm. This modification is capable of achieving better performance than that of the hybrid decoding algorithm with reducing the number of iterations required at each SNR and approaching more to the performance of the SPA. This reduction is more observable as the maximum number of iterations assigned for the algorithm increases or as the code length increases with improving the error performance as proved by simulation results.

A new algorithm for blind signal separation of speech signals that does not require pre-whitening is proposed in this paper. The algorithm is based on second order optimization using Riemannian geometry. The algorithm employs several practical approximations to the Hessian matrix of the maximum-likelihood blind separation cost function, to produce a computationally efficient algorithm that is capable of working on-line. Simulation results show the improved performance of the proposed algorithm with different mixing data