Multi-receiver connectivity via non-orthogonal multiple access (NOMA) is a popular candidate scheme for next generation multiple access (NGMA) as it facilitates resource block sharing (RBS) through power-domain layered stacking of various users’ signals. Dividing base station (BS) power among users’ signals precisely and activating candidate receivers properly for RBS are essential to efficiently utilize the available RBs for optimized communication speed. Data-driven based operation is a rising machine-learning framework which can be tailored to attack this resource management problem. In this paper, we propose a data-driven based algorithm to opportunistically grant receivers paired access and jointly calibrate their allotted portions of the total BS power available for NOMA-based downlink connectivity experiencing backhaul link capacity limitations. The proposed data-driven approach is combined with preemptive caching to store locally popular high-demand data in order to mitigate the throttling effects of the burdened backhaul. Resource management operation is designed by leveraging the ability of data-driven based operation to produce progressive linear approximations of complex models, with the objective of boosting the total attainable rate level. We present simulation-based results to validate the ability of the proposed technique to arrive at efficient solutions.

A peak cancellation technique with in-band distortion compensation has been developed to reduce the peak-to-average power ratio (PAPR) in massive multi-input multi-output orthogonal frequency division multiplexing (mMIMO-OFDM) systems using a uniform planar array. With this approach, in-band distortion compensation signals are transmitted using a subset of transmitting antennas to ensure that each user receives its dedicated data signals free of in-band distortion. This paper presents a proposal of an in-band distortion-aware modified minimum mean square error (MMSE)-based precoding (mMMSE) strategy that mitigates in-band distortion caused by peak cancellation and power amplifier nonlinearity in mMIMO-OFDM with a uniform-planar array. Simulation results demonstrate the effectiveness of the proposed precoding for improving the bit error rate and increasing the average system throughput compared to traditional approaches using mMIMO-OFDM configurations with peak cancellation and in-band distortion compensation while reducing PAPR of the transmit signal effectively.

Recent studies have explored various object detection methods that use channel state information (CSI) in wireless local area networks (WLANs) such as IEEE 802.11ac. However, evaluations used with WLAN-based approaches by experimentation mainly target indoor object detection scenarios. Therefore, the effects of antenna placement of access points (AP) and stations (STA) on detection performance in outdoor environments have not been clarified. As described herein, by experimentation, we evaluate the performance of our developed WLAN-based object detection system with distributed antennas in outdoor and large-scale indoor scenarios. Then we clarify the effects of AP and STA placements on the achieved detection performance, where an off-the-shelf WLAN device is used to capture feedback CSI, including beamforming weight information. We leverage these data as feature information for machine learning (ML)-based object detection. Findings indicate that the object detection method can increase the detection probability effectively in specific locations by appropriate location of AP and STA, while reducing the necessary complexity for ML model training and detecting the target in outdoor and large-scale indoor scenarios.

The integration of artificial intelligence (AI) with fog radio access networks (F-RANs) has garnered great interest, primarily motivated by the needs for efficient network operation and for ensuring high service availability. Fog access points (F-APs) can help with computation offloading and thereby alleviate the huge computational burdens of terminal devices in F-RANs. However, the overall system energy consumption must to be minimized. As described herein, we propose a computation offloading strategy for industrial internet-of-things (IIoT) devices that is centered around deep reinforcement learning (DRL) based user and F-AP association, which can learn high-dimensional data and which can respond to dynamic changes in the environment. The proposed DRL model adopts a framework that deploys the agent at the user side to address the challenge of high dimensionality in the action space. Specifically, each IIoT device is assigned a dedicated DRL model within the framework, facilitating the identification of an appropriate F-AP based on the environment state. Once the user and F-AP association process is completed, a computationally efficient greedy algorithm is used at each FAP, considering the limited capability, aiding in determining the subset of offloading requests that should be forwarded to the cloud for additional processing. The simulation results showcase the superior performance of the proposed DRL algorithm over traditional algorithms, including the random algorithm and the greedy algorithm, in terms of energy consumption. Under the same operation time, DRL also outperforms the genetic algorithm.

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.

Mobile edge computing (MEC) is expected to provide low-latency computation service for wireless devices (WDs). However, when WDs are located at cell edge or communication links between base stations (BSs) and WDs are blocked, the offloading latency will be large. To address this issue, we propose an intelligent reflecting surface (IRS)-assisted cell-free MEC system consisting of multiple BSs and IRSs for improving the transmission environment. Consequently, we formulate a min–max latency optimization problem by jointly designing multiuser detection (MUD) matrices, IRSs’ reflecting beamforming vectors, WDs’ offloading data size and edge computing resource, subject to constraints on edge computing capability and IRSs phase shifts. To solve it, an alternating optimization algorithm based on the block coordinate descent (BCD) technique is proposed, in which the original nonconvex problem is decoupled into two subproblems for alternately optimizing computing and communication parameters. In particular, we optimize the MUD matrix based on the second-order cone programming (SOCP) technique, and then develop two efficient algorithms to optimize IRSs’ reflecting vectors based on the semi-definite relaxation (SDR) and successive convex approximation (SCA) techniques, respectively. Numerical results show that employing IRSs in cell-free MEC systems outperforms conventional MEC systems, resulting in up to about 60% latency reduction can be attained. Moreover, numerical results confirm that our proposed algorithms enjoy a fast convergence, which is beneficial for practical implementation.

Recognizing if two objects are in close physical contact (CPC) is the basis of various Internet-of-Things services such as vehicle proximity alert and radiation exposure reduction. This is achieved traditionally through tailor-made proximity sensors that proactively transmit wireless signals and analyze the reflection from an object. Despite its feasibility, the past few years have witnessed the prosperity of reactive CPC detection techniques that do not need spontaneous signal transmission and merely exploit received wireless signals from a target. Unlike existing approaches entailing additional effort of multiple antennas, dedicated signal emitters, human intervention, or a back-end server, this article presents TONARI, an effortless CPC detection framework that performs in a reactive manner. TONARI is developed for the first time with LoRa, the representative of unlicensed low-power wide area network (LPWAN) technologies, as the wireless signal for CPC detection. At the heart of TONARI lies a novel feature arbitrator that decides whether two devices are in CPC or not by distinguishing different types of LoRa chirp-based additive sample magnitude sequences. Software-defined radio-based experiments are conducted to show that the achievable CPC detection accuracy via TONARI can reach 100% in most practical cases.

This paper proposes a heterogeneous carrier-sense multiple access (CSMA) protocol named LoHEC as the first research attempt to improve energy fairness when applying CSMA to long-range wide area network (LoRaWAN). LoHEC is enabled by Channel Activity Detection (CAD), a recently introduced carrier-sensing technique to detect LoRaWAN signals even below the noise floor. The design of LoHEC is inspired by the fact that existing CAD-based CSMA proposals are in a homogeneous manner. In other words, they require LoRaWAN end devices to perform identical CAD regardless of the differences of their used network parameter – spreading factor (SF). This causes energy consumption imbalance among end devices since the consumed energy during CAD is significantly affected by SF. By considering the heterogeneity of LoRaWAN in terms of SF, LoHEC requires end devices to perform different numbers of CAD operations with different CAD intervals during channel access. Particularly, the number of needed CADs and CAD interval are determined based on the CAD energy consumption under different SFs. We conduct extensive experiments regarding LoHEC with a practical LoRaWAN testbed including 60 commercial off-the-shelf end devices. Experimental results show that in comparison with the existing solutions, LoHEC can achieve up to$0.85\times $improvement of the energy fairness on average.

Wireless sensing is a promising technology for future wireless communication networks to realize various application services. Wireless local area network (WLAN)-based localization approaches using channel state information (CSI) have been investigated intensively. Further improvements of detection performance will depend on selecting appropriate feature information and determining the placements of distributed antenna elements. This paper presents a proposal of an enhanced device-free WLAN-based localization scheme with beam-tracing-based antenna placement optimization and spatially localized regression, where beam-forming weights (BFWs) are used as feature information for training machine-learning (ML)-based models localized to partitioned areas. By this scheme, the antenna placement at the access point (AP) is determined by solving a combinational optimization problem with beam-tracing between AP and station (STA) without knowledge of the CSI. Additionally, we propose the use of localized regression to improve localization accuracy with low complexity, where classification and regression-based ML models are used for coarse and precise estimations of the target position. We evaluate the proposed scheme effects on localization performance in an indoor environment. Experiment results demonstrate that the proposed antenna placement and localized regression scheme improve the localization accuracy while reducing the necessary complexity for both off-line training and on-line localization relative to other reference schemes.

In massive MIMO, precoding has an interference cancellation issue because the channel size has a considerable dimension based on the number of antennas and receivers. This paper proposes complexity-efficient precoding for single-cell multiuser massive MIMO systems that iteratively subtract interference replica signals based on a maximal ration transmission (MRT) weight. Moreover, it additionally proposes zeroing minute replica components to avoid excessive interference cancellation. Its remaining power can be diverted to the desired signal, improving the signal-to-interference-plus-noise power ratio (SINR) ratio. In addition, processing related to zero-ed components can be omitted, contributing to reduced computation complexity. Simulation results show its effectiveness in a specified region.

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.

Various object detection schemes using channel state information (CSI) in wireless local area networks (WLANs) such as IEEE802.11ac have been investigated recently. For further detection performance improvement, adopting a proper feature selection technique and using an appropriate antenna placement to obtain more effective CSI is important As proposed herein, a device-free WLAN-based object detection scheme with reflectors is developed, where the feedback CSI in WLANs (i.e., beamforming weight information) is captured at an off-the-shelf WLAN device and is used as feature information for machine learning (ML)-based object detection. In this scheme, reflectors are placed to create a multipath rich condition in an observation area. By experimentation, we demonstrate that reflectors can improve object detection performance. Additionally, we clarify that proper antenna placement effectively improves the detection accuracy, even when using a small number of antenna elements in an indoor environment. Experiment results demonstrate that the object detection performance of WLAN systems in an indoor environment can be improved by selecting antenna placement appropriately and by using measured concatenated CSI as effective feature information.

Wireless local area network (WLAN) based object detection techniques have attracted much attention. However, the detection performance of WLAN-based object detection is highly dependent on the antenna topology used. In this paper, we experimentally evaluate performance of antenna topology on the achieved object detection performance using channel state information (CSI) in WLAN systems, where spatially concatenated CSI is utilized as effective feature information for machine learning (ML)-based object detection. Based on the experiment results, we discuss how much object detection performance improvement is expected when the proper antenna topology is used at the access point side. We also experimentally clarify that the detection accuracy is improved when concatenated CSI is used as feature information for ML-based object detection for various antenna placement scenarios.

Reconfigurable intelligent surface (RIS)-aided terahertz (THz) communications have been regarded as a promising candidate for future 6G networks because of its ultra-wide bandwidth and ultra-low power consumption. However, there exists the beam split problem, especially when the base station (BS) or RIS owns the large-scale antennas, which may lead to serious array gain loss. Therefore, in this paper, we investigate the beam split and beamforming design problems in the THz RIS communications. Specifically, we first analyze the beam split effect caused by different RIS sizes, shapes and deployments. On this basis, we apply the fully connected time delayer phase shifter hybrid beamforming (FC-TD-PS-HB) architecture at the BS and deploy distributed RISs to cooperatively mitigate the beam split effect. We aim to maximize the achievable sum rate by jointly optimizing the hybrid analog/digital beamforming, time delays at the BS and reflection coefficients at the RISs. To solve the formulated problem, we first design the analog beamforming and time delays based on different RISs’ physical directions, and then it is transformed into an optimization problem by jointly optimizing the digital beamforming and reflection coefficients. Next, we propose an alternatively iterative optimization algorithm to deal with it. Specifically, for given the reflection coefficients, we propose an iterative algorithm based on the minimum mean square error technique to obtain the digital beamforming. After, we apply Lagrangian dual reformulation (LDR) and multidimensional complex quadratic transform (MCQT) methods to transform the original problem to a quadratically constrained quadratic program, which can be solved by alternating direction method of multipliers (ADMM) technique to obtain the reflection coefficients. Finally, the digital beamforming and reflection coefficients are obtained via repeating the above processes until convergence. Simulation results verify that the proposed scheme can effectively alleviate the beam split effect and improve the system capacity.

This paper proposes reinforcement learning (RL)-based solutions for unmanned aerial vehicle (UAV) data offloading in B5G mmWave-enabled communications. This is particularly useful for ad-hoc transmission scenarios within environments experiencing connectivity issues with the main servicing network as in disaster-stricken areas. Double deep Q-network and multiarmed bandit-based algorithms are proposed to tackle the joint problem of UAV-positioning and Rx-node activation and power allocation for data offloading in downlink NOMA transmissions. Numerical simulations are performed to ensure the proposed RL-based algorithms can adequately provide high data transfer rates, along with random and exhaustive search solutions as benchmarks for lower and upper bounds on the achievable sum-rate levels.

Next-generation smart building schemes using information and communication technologies have been developed recently. Attracting particular attention are wireless positioning techniques used to manage workers' activities and their locations in buildings under construction. However, no wireless networks exist in such buildings. Moreover, building conditions can change drastically from moment to moment. Therefore, a simple positioning approach using a temporary use network is required. As described herein, we propose a floor height estimation method using an unlicensed band low-power wide-area network (LPWAN) for three-dimensional indoor positioning in buildings under construction, where a few 920 MHz LoRa nodes are deployed as a temporary use wireless network. The proposed method is based on a simple algorithm that estimates the height of a floor on which a target exists by comparing the received signal strength information (RSSI) from different floors. Experiment results clarify that the proposed method is effective for estimating a floor height of the target in both line-of-sight (LOS) and non-line-of-sight (NLOS) conditions.

This paper proposes an in-band distortion cancellation (IDC) scheme for peak cancellation (PC) based peak-to-average power ratio (PAPR) reduction in massive multi-input multi-output orthogonal frequency division multiplexing (mMIMO-OFDM) with a uniform planar array (UPA). In the proposed scheme, properly selected transmit antennas, i.e., in-band distortion compensation antenna, are designed to compensate in-band distortion because of PC, where the beam-formed IDC signals are transmitted through the compensation antennas so that each user can receive its own data signal with no in-band distortion. Based on the idea that higher in-band distortion compensation accuracy is achieved if the IDC signal itself is less affected by power amplifier nonlinearity, the proposed scheme performs the in-band distortion cancellation by selecting appropriate antenna distribution for compensation to minimize nonlinear distortion of the IDC signal. Simulation results show that the proposed scheme achieves better BER performance than conventional schemes and that instantaneous power at the complementary cumulative distribution function (CCDF)=10-4is reduced to 3dB.

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.

The application of compressed sensing (CS) based techniques to non-orthogonal multiple access (NOMA) systems has been investigated recently. In this paper, we propose a quality-of-service (QoS) aware low complexity CS-based user pairing and power allocation scheme for downlink NOMA systems, where the tolerable interference threshold is mathematically designed to achieve a given QoS requirement so that user pairing and power allocation problem is relaxed to l1 norm optimization problem under a QoS constraint based on the tolerable interference threshold. Simulation results show that the proposed scheme is effective in improving user rate compared with other reference schemes while the QoS requirement is approximately satisfied as long as the required QoS value is feasible.

Machine learning based object detection that utilizes channel state information (CSI) in wireless local area network (WLAN) systems is an effective approach for indoor positioning. In this paper, we propose a real-time CSI-based object detection scheme using interleaved subcarrier selection techniques for WLAN systems with distributed antennas, where CSI frames are collected and used as data-set for machine learning and object detection. To improve real-time detection performance, we investigate two approaches; interleaved sampling (IS), and interleaved sampling and clustering (ISC). In the IS scheme, a part of subcarriers are selected among all subcarriers in an interleaved manner to reduce data-set size while maintaining the object detection accuracy. In the ISC scheme, all subcarriers (their CSI) are grouped into several clusters in an interleaved manner and detect a target by integrating cluster-by-cluster machine-learning results. Furthermore, we demonstrate the effectiveness of the proposed approach through real-time experimental evaluations in an indoor environment scenario. Experimental results show that the ISC scheme improves object detection probability than the case without clustering, while the IS scheme is effective in reducing data-set size for obtaining almost the same performance. The results also indicate that the improved detection performance is obtained by using the proposed scheme with a distributed antenna array.

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.

Wireless sensor networks (WSNs)-based Internet of Things (IoT) are among the fast booming technologies that drastically contribute to different systems' management and resilience data accessibility. Designing a robust IoT network imposes some challenges, such as data trustworthiness (DT) and power management. This article presents a repeated game model to enhance clustered WSNs-based IoT security and DT against the selective forwarding (SF) attack. Besides, the model is capable of detecting the hardware (HW) failure of the cluster members (CMs), preserving the network stability, and conserving the power consumption due to packet retransmission. The model relies on the TDMA protocol to facilitate the detection process and to avoid collision between the delivered packets at the cluster head (CH). The proposed model aims to keep packets transmitting, isotropic or nonisotropic transmission, from the CMs to the CH for maximizing the DT and aims to distinguish between the malicious CM and the one suffering from the HW failure. Accordingly, it can manage the consequently lost power due to the malicious attack effect or HW malfunction. The simulation results indicate the proposed mechanism improved performance with TDMA over six different environments against the SF attack that achieves the Pareto-optimal DT as compared to a noncooperative defense mechanism.

In orthogonal frequency division multiplexing (OFDM) with massive multi-input multiple-output (mMIMO), the reduction of the high peak-to-average power ratio (PAPR) is a challenging problem. Recently, an adaptive peak cancellation is proposed to reduce the transmitted signal's PAPR, while keeping the out-of-band leakage power (ACLR) as well as an in-band distortion power (EVM) below the predetermined and permissible value. In this paper, we propose an analytical method to evaluate achievable BER performance of downlink OFDM with the peak cancellation in massive multi-input-multioutput (mMIMO) systems using arbitrary numbers of transmit antennas and served users. In this method, bit error rate (BER) is derived based on the assumption that in-band distortion due to peak cancellation is approximated as random variable following Gaussian distribution, provided that variance of the Gaussian distribution in two user case is known. The results clarify that theoretical BER expressions for arbitrary numbers of transmit antennas and served users show good agreements with its simulation results. In addition, we clarified the impact of the increase of the number of transmit antennas on achievable BER and PAPR reduction capability in MRC precoded mMIMO-OFDM system with the peak cancellation.

In this paper, we propose a two-dimensional pilot allocation scheme over frequency- and delay-time domains (2D-PFD) for channel estimation in massive multiple-input multiple-output (MIMO)/time division duplex (TDD) system, where two- dimensional pilot resources are simultaneously allocated to each user for their uplink channel estimation. We evaluate bit error rate (BER) performance of massive MIMO/TDD system using the 2D-PFD scheme by computer simulation in order to clarify the effectiveness of the proposed pilot allocation compared with single dimensional pilot allocation over either delay-time domain or frequency domain, respectively.

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 energy-efficient resource allocation problem in an uplink non-orthogonal multiple access (NOMA) millimeter wave system, where the fully-connected-based sparse radio frequency chain antenna structure is applied at the base station (BS). To relieve the pilot overhead for channel estimation, we propose a codebook-based analog beam design scheme, which only requires to obtain the equivalent channel gain. On this basis, users belonging to the same analog beam are served via NOMA. Meanwhile, an advanced NOMA decoding scheme is proposed by exploiting the global information available at the BS. Under predefined minimum rate and maximum transmit power constraints for each user, we formulate a max-min user energy efficiency (EE) optimization problem by jointly optimizing the detection matrix at the BS and transmit power at the users. We first transform the original fractional objective function into a subtractive one. Then, we propose a two-loop iterative algorithm to solve the reformulated problem. Specifically, the inner loop updates the detection matrix and transmit power iteratively, while the outer loop adopts the bi-section method. Meanwhile, to decrease the complexity of the inner loop, we propose a zero-forcing (ZF)-based iterative algorithm, where the detection matrix is designed via the ZF technique. Finally, simulation results show that the proposed schemes obtain a better performance in terms of spectral efficiency and EE than the conventional schemes.

In this paper, we investigate pilot allocation problem in two-tier time division duplex (TDD) heterogeneous network (HetNet) with mMIMO. First, we propose a new pilot allocation scheme to maximize ergodic downlink sum rate of macro users (MUs) and small cell users (SUs), where the uplink pilot overhead and cross-tier interference are jointly considered. Then, we theoretically analyze the formulated problem and propose a low complexity one-dimensional search algorithm to obtain the optimum pilot allocation. In addition, we propose two suboptimal pilot allocation algorithms to simplify the computational process and improve SUs' fairness, respectively. Finally, simulation results show that the performance of the proposed scheme outperforms that of the traditional schemes.

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.

In this paper, we investigate the dynamic small cell (SC) clustering strategy and their precoding design problem for interference coordination in two-tier heterogeneous networks (HetNets) with massive MIMO (mMIMO). To reduce interference among different SCs, an interference graph-based dynamic SC clustering scheme is proposed. Based on this, we formulate an optimization problem as design precoding weights at macro base station (MBS) and clustered SCs for maximizing the downlink sum rate of SC users (SUs) subject to the power constraint of each SC BS (SBS), while mitigating inter-cluster, eliminating inter-tier, intra-cluster and multi-macro user (MU) interference. To eliminate the inter-tier and multi-MU interference simultaneously, we propose a clustered SC block diagonalization precoding scheme for the MBS. Next, each SU's precoding vector at clustered SCs is designed as the product of the following two parts. The first part is designed with singular value decomposition to remove the intra-cluster interference. The second part is designed to coordinate the inter-cluster interference for maximizing the downlink sum rate of SUs, which is a non-convex optimization problem and difficult to solve directly. A non-cooperative game-based distributed algorithm is proposed to obtain a suboptimal solution. Meanwhile, we prove the existence and uniqueness of Nash equilibrium for the formed game. Finally, simulation results verify the effectiveness of our proposed schemes.

In this paper, we investigate the bandwidth and power allocation problem in remote radio head cluster (RRHC)-based millimeter wave (mm-wave) massive MIMO heterogeneous cloud radio access networks with limited fronthaul capacity. The coordinated multipoint transmission is applied in each RRHC for cancelling the intra-cluster interference. To avoid the inter-tier interference, distinct bandwidths are allocated to macro base station and RRHs. Following this, we formulate a bandwidth and power allocation optimization problem to maximize the downlink weighted sum rate of the system subject to per-RRHC power and fronthaul capacity constraints, which is a non-convex optimization problem and is difficult to directly solve. Next, we fix the bandwidth allocation and the original problem can be divided into two independent optimization problems, i.e., the weighted sum rate maximization problems of MUs and RRH users, respectively. For the former, the convex optimization technique can be used to solve it. As for the latter, a two-loop iterative algorithm is proposed to deal with it. Specifically, we propose the price-based outer iteration to control the fronthaul capacity and the weighted minimum mean square error-based inner iteration to obtain the power allocation. To this end, a 1-D search method is adopted to find the optimal bandwidth allocation. Finally, numerical results are conducted to verify the effectiveness of the proposed algorithms under different parameters.

Due to the fast boom of security threats in wireless sensor networks (WSNs) sensitive applications, we propose a game-theoretic protection approach for sensor nodes in a clustered WSN based on a repeated game. The proposed game model is developed for detecting malicious sensor nodes that drop the high priority packets (HPPs) aiming at maximizing the high priority data trustworthiness (HPT). Simulation results indicate the improved HPT of the proposed protection model which attains the Pareto optimal HPT as compared to a non-cooperative defense mechanism.

Wireless Sensor Networks (WSNs) security plays an intrinsic role to guarantee efficient data transmission, stable network topologies, and robust routing algorithms. In this paper, we propose a modified Stackelberg game of a previous work for high assurance of data trustworthiness in a Power Grid Network (PGN). The proposed approach is presented to mitigate a more severe attack scenario compared to that considered in the previous work; this attack scenario frequently manipulates sets of the deployed nodes in the PGN, which cannot be treated using the previously proposed approach. Our proposed scheme reduces the required number of nodes to be protected to achieve the desired data trustworthiness. Simulation results prove efficient detection for corrupted transmitted data based on limited number of nodes as compared to the previously proposed approach.

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.

Pilot contamination due to the pilot reuse in adjacent cells is a serious problem in time-division duplex (TDD) massive multi-input multiple-output (MIMO) system. Therefore, the pilot allocation is significant for improving the performance of the system. In this paper, we formulate the pilot allocation optimization problem for maximizing uplink sum rate of the system. To reduce the required complexity for finding the optimum pilot allocation, we propose a low-complexity pilot allocation algorithm, where the formulated problem is decoupled into multiple subproblems; in each subproblem, the pilot allocation at a given cell is optimized while fixing the pilot allocation in other cells. This process is continued until the achievable sum rate converges. Through multiple iterations, the optimum pilot allocation is found. In addition, to improve users' fairness, we formulate a fairness aware pilot allocation as maximization problem of sum of user's logarithmic rate and solve the formulated problem using a similar algorithm. Simulation results show that the proposed algorithms obtain good performance comparable to the exhaustive search algorithm, meanwhile the users' fairness is improved.

The authors propose a game‐theoretic approach using the Stackelberg game for securing cognitive radio sensor network (CRSN) against the spectrum sensing data falsification attack; this attack aims at corrupting the spectrum decisions communicated from the ambient sensor nodes (ASNs) to the fusion centre by imposing interference power. The proposed game approach is developed for two different attack–defence scenarios. In the first scenario, the attacker selects to attack a group of delivered reports of the ASNs that have a protection degree below a specific threshold. In the second scenario, the attacker applies its maximum attack interference power to the delivered reports of the ASNs that have been reported to be least protected in the previous round. Simulation results indicate the improved performance of the proposed protection model as compared with two baseline defence mechanisms, namely, the random and equal‐protection defence mechanisms with static signal‐to‐noise ratio (SNR) and variable SNRs. Consequently, Stackelberg game features prove to be beneficial for securing communication over CRSN.

In this paper, we propose a game-theoretic protection model for Wireless Sensor Network (WSN) nodes within a cluster based on a Stackelberg game. The proposed game approach is developed for two different attack-defense scenarios. In the first scenario, the attacker selects to attack a group of nodes that have a protection degree below a specific threshold. In the second scenario, the attacker targets the nodes that have been reported to be least protected in the previous round. Simulation results indicate the improved performance of the proposed protection model as compared to the no-defense case.

The traditional K-best sphere decoder retains the best K-nodes at each level of the search tree; these K-nodes, include irrelevant nodes which increase the complexity without improving the performance. A variant of the K-best sphere decoding algorithm for ill-conditioned MIMO channels is proposed, namely, the ill-conditioned reduced complexity K-best algorithm (ill-RCKB). The ill-RCKB provides lower complexity than the traditional K-best algorithm without sacrificing its performance; this is achieved by discarding irrelevant nodes that have distance metrics greater than a pruned radius value, which depends on the channel condition number. A hybrid-RCKB decoder is also proposed in order to balance the performance and complexity in both well and ill-conditioned channels. Complexity analysis for the proposed algorithms is provided as well. Simulation results show that the ill-RCKB provides significant complexity reduction without compromising the performance.

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.

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.

Recently, an adaptive peak cancellation was proposed to reduce the high peak-to-average power ratio (PAPR), while keeping the out-of-band (OoB) power leakage as well as an in-band distortion power (EVM) below the pre-determined (permissible) level. However, the peak cancellation in MIMO-OFDM systems was not considered. In this paper, we propose a peak cancellation method for linearly pre-coded MIMO-OFDM systems using eigen-beam space division multiplexing (E-SDM). We evaluate and discuss the performance of the system using the proposed peak cancellation in terms of bit error rate (BER), complementary cum-mulative distribution function (CCDF) of PAPR and the system's computational complexity. Our results show the improvements with respect to both the achievable BER and PAPR with the proposed peak cancellation in E-SDM systems under the restriction of OoB power radiation.

Analog-to-digital (A/D) converter (ADC) and related analog hardware designs are important factors to simplify the transceiver circuits in wireless communication systems. In order to mitigate the nonlinearity of a low-resolution ADC that reduces the required analog hardware complexity, we have investigated two nonlinearity mitigation techniques for A/D conversion, i.e., the dither-ADC and the hysteresis-ADC. In this paper, we evaluate the effect of the nonlinearity mitigated A/D conversion techniques on the achievable performance in single carrier offset-quadrature-amplitude-modulation (OQAM)and QAM systems, where the receiver adopts either the dither-ADC or the hysteresis-ADC. Simulation results prove that both the dither-ADC and the hysteresis-ADC are effective in improving BER performance of both OQAM and QAM systems affected by the nonlinearity of ADC, while the transmitter employs a selected mapping technique that achieves a low peak-to-average power ratio (PAPR).

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.

In MIMO space division multiplexing (SDM) systems, the peak power of the transmit signal at each antenna element is increased by the precoder and hence it causes the power efficiency degradation and/or nonlinear distortion at the power amplifier. To solve this problem, this paper present a peak power aware precoding scheme for MIMO-SDM systems, where the precoder is designed to mitigate the peak output power at each antenna element so that peak amplitude of the transmit signal at each antenna is reduced while minimizing the performance degradation caused by the peak power restriction. The proposed system employs an alternative iterative optimization algorithm that determines the precoding matrix so that the precoding and virtual post-coding matrices are alternatively optimized under the constraint of peak output at each antenna element. Simulation results show that the proposed method is effective in restricting the peak transmission power at each antenna within a permissible level while mitigating performance degradation in bit error rate (BER) performance of the precoded SDM systems.

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, cooperative compressive spectrum sensing is considered to enable accurate sensing of the wide-band spectrum. The proposed algorithm is based on compressive sensing theory and aims to reduce the hardware complexity of the cognitive radio receiver by distributing the sensing work among groups of sensing nodes. The proposed algorithm classifies the cooperated sensing nodes into different sensing groups depending on the quality of the reporting channel between the sensing node and the fusion center (FC). To sense the wide- band analog signal and take a global decision about spectrum occupancy, each node uses its local sensing matrix, which is assigned to its sensing group and a part of a global sensing matrix at the FC. The size of the local sensing matrix of each sensing node,and consequently the contribution of this node in the overall measurement vector, depends on its sensing group. The FC classifies and rearranges the compressed data to formulate one global measurement vector which is used with a global sensing matrix to estimate the wide-band signal spectrum. The receiver operation characteristics (ROC) of the overall spectrum sensing system show that the proposed receiver provides more protection to primary users (higher detection probability) at the same secondary user throughput (probability of false alarm).

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.

Fractional Frequency Reuse (FFR) is an efficient method to mitigate Inter Cell Interference in multicellular Orthogonal Frequency Division Multiple Access (OFDMA) systems. In this paper, we analyze the downlink worst case Signal to Interference Ratio for FFR schemes. A closed form expression is derived analytically for the worst SIR, outage probability, and Spectral Efficiency (SE). The proposed analytical technique is used to configure a FFR solution for the downlink of OFDMA cellular system. The analysis is performed using two-tiers cellular network with uniform user density and for three different cases of FFR, namely, Frequency Reuse Factor (FRF) = 3, FRF=4 and sectored FFR. The inner radius configuration depends on equalizing the worst SIR for both inner and outer edges of the cell. Numerical results show that sectored FFR yields the highest SE and low outage probability. Sectored FFR highly balances the needs of interference reduction and resource efficiency.

In this paper, we propose a peak-to-average power ratio (PAPR) reduction scheme based on adaptive peak amplitude cancellation for multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems, where peak amplitude of OFDM signal is iteratively suppressed by adding a specific peak cancellation (PC) signal which has flat frequency spectrum over signal bandwidth in frequency domain and sharp peak amplitude at the midpoint of pulse waveform in time domain. In the proposed scheme, both adjacent channel leakage power ratio (ACLR) and error vector magnitude (EVM) are automatically adjusted so as to meet a given requirement. In the proposed scheme, both PAPR reduction performance and system requirements such as out-of-band spectrum radiation and in-band distortion are simultaneously taken into consideration; both ACLR and EVM are automatically controlled to meet the requirements. Computer simulation results prove that the proposed PAPR suppression scheme effectively reduce PAPR of the OFDM signal in MIMO systems, while both ACLR and BER requirements are simultaneously fulfilled.

As a solution to high peak-to-average power ratio (PAPR) problems in vector coding (VC) systems, phase control-based methods such as selected mapping (SLM) and partial transmit sequence have been investigated. As described in this study, a phase sequence blind estimation method is presented for PAPR reduction based on phase control in multi-input multi-output (MIMO) adaptive modulated VC systems, where turbo code is employed. On the receiver side, the phase sequence component is estimated using a maximum likelihood method that selects the most likely sequence among all candidate sequences by exploiting soft-output information of the decoder. Computer simulation results show that when the number of candidate sequences in SLM is M=16, instantaneous power of the transmit signal at the complementary cumulative distribution function of 10−5 can be reduced by about 4.0 and 3.5 dB for single-input single-output and MIMO cases as compared with the case without PAPR reduction, although almost identical block error rate performance and the same throughput performance as in the case of perfect phase sequence estimation are achieved in an attenuated six-path Rayleigh fading condition.

The authors have proposed Multi-Input Multi-Output (MIMO)-Constant Envelope Modulation, (MIMO-CEM), as power and complexity efficient alternative to MIMO-OFDM, suitable for wireless backhaul network in which relay nodes are fixed in their positions. One of the major problems to withstand real application of MIMO-CEM is to estimate MIMO channel characteristics. The MIMO-CEM is based upon two contrary schemes; one is the nonlinear CEM modified Maximum Likelihood Sequence Estimator (MLSE) proposed by the authors, which needs accurate channel information to replicate the received signal passing through channel. The other is low resolution analog-to-digital converter (ADC), i.e., 1-bit in the default operation and 2 or 3 bits in the optional operations, which means that the received channel amplitude information may be completely or partially destroyed. These two contrary demands make MIMO-CEM channel estimation a big challenge; how we can accurately estimate the channel in these severe low ADC resolution conditions. We consider this issue through designing an efficient MIMO-CEM channel estimator based upon a block based adaptive filter. In addition, in order to speed up the parameter convergence rate in the proposed adaptive estimator, we design a correlator estimator as initial channel state estimation used in the adaptive estimator. We prove the effectiveness of the proposed MIMO-CEM channel estimation under different channel scenarios and different low ADC resolutions such as 1, 2 and 3-bit.

In this paper, a subcarrier-phase control based peak-to-average power (PAPR) reduction scheme without side-information transmission is proposed for LDPC coded OFDM systems. On the transmitter side, peak power of OFDM signal is reduced by multiplying the selected phase-sequence. On the receiver side, the phase sequence used for PAPR reduction is estimated without side information by utilizing the decoding property of LDPC code, i.e., based on the decoding information in single iteration sum-product calculation, limited number of candidate sequences is selected from all possible phase sequence patterns and the correct phase-sequence can be estimated by comparing the decoding results corresponding to these selected candidate sequences. Computer simulation results show that, when LDPC code with code-rate of R ¿ 1/2 is used, PAPR of OFDM signal can be reduced by about 2.4 dB without significant degradation in BLER performance as compared to perfect WF estimation in attenuated 12-path Rayleigh fading condition.

As a method to realize an orthogonal multicarrier transmission, offset quadrature amplitude modulation based multi-carrier modulation (OQAM-MCM) has been known, where each subcarrier is strictly band-limited by a filter. Objective of this paper is to discuss the use of OQAM-MCM system instead of orthogonal frequency division multiplexing (OFDM) system as a countermeasure against frequency-selective fading. We investigate the performance of OQAM-MCM system with a small number of subcarriers employing a simplified time-domain per-subcarrier equalizer. Computer simulation results show that OQAM-MCM system can achieve both better spectrum efficiency and lower bit error rate (BER) performance than those of OFDM systems with insufficient guard interval (GI), when a time-domain per-subcarrier equalizer is used in both systems.

In this paper, we propose a weighting factor (WF) iterative estimation method for a turbo-coded multi-carrier code division multiplexing (MC-CDM) system using partial-transmit sequence (PTS) based peak-to-average power (PAPR) reduction, where the transmitter structure in the proposed system is an extended version of the PTS and the systematic bits of turbo-code is adaptively flipped by multiplying WFs so as to reduce PAPR of MC-CDM signal. On the receiver side, WF estimation and error correction are jointly performed with turbo decoding, where WFs are estimated by exploiting the decoding results of two data streams using different spreading code. When PTS like PAPR reduction using 9 clusters is applied to MC-CDM signal with spreading factor of 16, PAPR of the transmit signal at the CCDF of 10-4can be reduced by about 2.0 dB, where the number of multiplexed codes is 8. With the proposed method, WF estimation accuracy is improved as the subcarrier modulation level increases. The degradation in block error rate (BLER) performance as compared with case of the perfect WF estimation is about 0.7 dB at BLER=10-2for QPSK-MC-CDM signal with PAPR reduction using 5 clusters in attenuated 12-path Rayleigh fading condition. In the MC-CDM system using 64QAM, the proposed method achieves almost the same BLER performance as case of the perfect WF estimation, even when the number of clusters is L = 9.

MIMO system is a technology to realize high data rates and high capacity. Among various MIMO systems, Eigenbeam MIMO (E-MIMO) system achieves the theoretical maximum capacity. The E-MIMO system uses eigenvectors of channel autocorrelation matrix as transmit and receive weights. As a method to find these eigenvectors, eigenvalue decomposition or singular value decomposition (SVD) are generally well-known. In this paper, we propose a least mean square (LMS) based algorithm to find the transmit and receive weights without significant increase of computational complexity for E-MIMO system. Computer simulation shows that the proposed algorithm gives almost the same performance as that of SVD.

An iterative optimization method of transmit/receive frequency domain equalization (FDE) is proposed for single carrier transmission systems, where both transmit and receive FDE weights are iteratively determined with a recursive algorithm so as to minimize the error signal at a virtual receiver. The computer simulation results show that SC systems using the proposed transmit/receive equalization method achieves better BER performance than those using the conventional receive FDE. BER performance of SC systems using the proposed method was improved by about 2.7 dB at BER=10-3compared to case of those using conventional receive FDE in attenuated 6-path quasistatic Rayleigh fading with normalized delay spread value of tau/T = 0.769. In addition, when decision feedback equalizer (DFE) with sufficient number of feedback taps is adopted in both systems, the proposed system achieves better BER performance than the conventional system in a low Eb/N0region and BER performance of the proposed system becomes close to that of the conventional one as Et/No increases in the above channel condition.

Adaptive predistorter is an effective technique to compensate nonlinear distortion in a power amplifier. As a method to improve the parameter convergence speed in the predistorter, a series expansion technique with an orthogonal polynomials has been proposed. In this paper, we propose an adaptive predistorter based on orthogonal polynomial for the power amplifier with memory effect, where the parameters of predistorter are determined so as to minimize out-of-band radiation components detected by high pass filter (HPF). With computer simulation using the Advanced Design System (ADS) software, we show that the adaptive predisterter using orthogonal polynomials achieves faster convergence time than that with that of non-orthogonal one, while nonlinear distortion is compensated by memory predistorter using orthogonal polynomials.

In this paper, we propose a peak power reduction method for LDPC coded OFDM system, where transmit data sequence is grouped into several clusters based on structure of a low density parity-check matrix and the phase or each cluster is adjusted so as to minimize PAPR of OFDM signal by multiplying weighting factors (WFs). At the receiver, WFs are estimated with sufficient accuracy by exploiting the decoding property of LDPC code. The proposed method can be applied to not only systematic coded signal but also non-systematic one. Computer simulation results show that, when LDPC code with code-rate of R ap 1/2 is used, PAPR of OFDM signal can be reduced by about 2.4 dB without significant degradation in BLER performance as compared to perfect WF estimation in attenuated 12-path Rayleigh fading condition.

A problem with the OFCDM (orthogonal frequency and code division multiplexing) system is that PAPR (peak to average power ratio) of the signal becomes high. In this paper, we propose a novel peak reduction method which suppresses the inter-signal interference caused by peak power limitation for OFCDM signal. In the proposed method, peak power is reduced by adding the peak reducing signal generated with peak components detected by soft-clipping function. At the receiver, the inter-signal interference is compensated by estimating the peak components removed at the transmitter from the received signal. Performance of a total system including modulation, peak-limiter, predistorter, a model of 2 GHz class A/B power amplifier and the receiver is investigated by computer simulation. Computer simulation results show the peak power reduction performance is improved about 1.4 dB as compared with that of the conventional PRSA (peak reducing signal addition) method.

In this paper, we propose a weighting factor (WF) estimation scheme for phase-control based peak power reduction (PPR) of turbo-coded OFDM signal. In this PPR scheme, the peak power of OFDM signal is reduced by adjusting the phase of parity-carriers by multiplying WFs. At the receiver, WFs are estimated at turbo-decoder without using any side-information. The proposed WF estimation scheme is based on an iterative decoding of turbo-code, i.e. the turbo decoder provides not only error correction capability but also WF estimation function. When a turbo-code of the constraint length of K = 4 and the code rate of R = 1/2 is employed, PAPR of OFDM signal at the CCDF of 10-4can be reduced by about 2.1 dB by applying the PPR scheme. The proposed scheme achieves good BER performance comparable to the case of perfect WF estimation in attenuated 12-path Rayleigh fading condition. The degradation in BER performance is about 1.0 dB, 0.4 dB and 0.2 dB at BER=10-4for turbo-coded OFDM signals using QPSK, 16QAM and 64QAM, respectively

As a method to compensate for the nonlinear distortion of a power amplifier, the adaptive predistorter and the negative feed-back system are known. Although the feedback method is a simple technique, its instability becomes a problem for a high feedback gain to achieve a high compensation effect. On the other hand, the predistorter needs long time to calculate the suitable parameters. In this paper, we propose a nonlinear distortion compensation method for a narrow-band signal. In this method, adaptive predistorter and negative feedback are combined. With computer simulations, we show that the proposed scheme achieves both five times faster convergence speed than that of the predistorter and three times longer permissible delay time in the feed-back amplifier than that of a negative feed-back only amplifier, while keeping the required compensation performance.

In this paper, we propose a frequency-domain channel estimation scheme using co-phased pilot-signals for a multi-carrier modulation (MCM) system with bandlimited subcarriers: pilot signals are inserted periodically in each subcarrier and are added up (multiplexed) to be inphase at the midpoint of the pilot-symbol duration at the transmitter. Then, the multiplexed pilot signal exhibits a sharp peak in amplitude at the midpoint of the pilot-symbol duration. Thus, the channel characteristics can be estimated with a high time resolution of the pilot signal. We evaluated the performance of an MCM system with a frequency-domain equalizer using the proposed scheme by computer simulation. The BER performance was improved with the channel estimation scheme.

This paper proposes an adaptive channel selection method that reduces signal distortion due to frequency selective fading and thereby improving the performance of mobile communication. In this method a channel is adaptively selected by estimating current channel condition with mean square decision error as a measure. This selected frequency channel corresponds to the channel in which the delayed signals add-up inphase. Hence, BER performance is improved with the proposed scheme. Computer simulation results show the effectiveness of the proposed scheme for a system with multiple terminals and also for a multiple base station (cellular) system.