Summary Wireless sensor networks (WSNs) are faced with the challenge of energy conservation, which makes efficient routing protocols crucial for prolonging network lifetime. In addition, delay time from sensors to the base station is critical in applications such as military, medical, and security monitoring systems. Chain‐based protocols like PEGASIS, CCBRP, and CCM have been developed to address routing in WSNs, with the aim of reducing energy consumption and delay. However, node failures are inevitable due to energy reduction and node mobility. Therefore, fault tolerance techniques must be integrated into routing protocols. A new fault tolerance method has been proposed to prevent early chain failures in WSNs by formulating network availability and reliability using Markov chain analysis. The results indicate that chain‐based routing protocols with one spare node are more reliable than those with two spare nodes.

Abstract Health complications during the gestation period have evolved as a global issue. These complications sometimes result in the mortality of the fetus, which is more prevalent in developing and underdeveloped countries. The genesis of machine learning (ML) algorithms in the healthcare domain have brought remarkable progress in disease diagnosis, treatment, and prognosis. This research deploys various ML algorithms to predict fetal health from the cardiotocographic (CTG) data by labelling the health state into normal, needs guarantee, and pathology. This work assesses the influence of various factors measured through CTG to predict the health state of the fetus through algorithms like support vector machine, random forest (RF), multi‐layer perceptron, and K‐nearest neighbours. In addition to this, the regression analysis and correlation analysis revealed the influence of the attributes on fetal health. The results of the algorithms show that RF performs better than its peers in terms of accuracy, precision, recall, F1‐score, and support. This work can further enhance more promising results by performing suitable feature engineering in the CTG data.

Abstract Medical internet of things (IoT) includes various devices, sensor, machines, equipment which exchange data over wide network. Industry 5.0 and 5G technology have ramped mIoT data and cost‐effective sensors. There is sudden upsurge in the medical IoT for enhancing the medical care. Integration of cloud server for data storage and cloud computing has led to the emergence of time and cost‐effective management of medical resources and improved patient lifestyle. However, cloud data is always at risk of data leak and malicious attack by unwanted users for their personal gains and agenda. With the increase of data exchange in the medical field, there is an urgency of keeping all the transaction safe and secure. There is a growing malicious data attacks and vulnerability. Therefore, to overcome such situations, the proposed model is a step towards sustainable technology. Keeping the focus on cost effective data security, the present work has developed a framework of modified Lamport Merkle Digital Signature method for signature generation and verification. It makes use of central healthcare controller (CHC) which determine the root of generated signature along with verification and authentication. For verification, the validation hash public key with generate key is required to validate the signature. This led to the efficient, cost effective and faster security when compared to the existing methods.

In this paper, we propose a novel received signal strength (RSS) and machine learning (ML) based system for recognizing human motion walking patterns. Our proposed system eliminates the need to attach sensors on a human body, thereby enabling a non-invasive monitoring system. The proposed system model exploits the potential of a single channel RSS in precisely identifying unique human motion patterns in indoor environments and implements a support vector machine (SVM) algorithm for higher pattern detection accuracy. We validate our proposed model by developing a testbed setup based on state-of-the-art Software Defined Radios (SDRs) and provide a comparative analysis of machine learning models used in the patterns classification process. Our study results reveal that unique walking patterns embedded within RSS and with machine learning classifier, can precisely help in identifying human motion patterns with detection accuracy of approximately 99 percent. The study results impact research scholars actively engaged in developing human motion recognition systems, intrusion detection systems, or healthcare monitoring systems, and in those developing innovative and efficient techniques for monitoring and control systems.

This paper studies the prediction of the received signal strength at a receiver that tracks an automated guided vehicle (AGV) as it moves along a factory route. We apply machine learning to predict a sliding-window pattern of the received signal strength indication (RSSI) signal and further improve the prediction performance by using multiple receivers. The performance evaluation processes wireless data collected from actual received signal strength measurement experiments recorded from an OFDM transmitter in the 2.4 GHz band. The performance is evaluated for vehicle movement along routes with both repetitive and random sections and with and without position errors.

The next-generation wireless networks are expected to become denser and more heterogeneous in order to boost the network capacity. However, densely deployed base stations (BSs) in heterogeneous networks (HetNets) can give rise to interference. On the other hand, a limited number of channels is allocated within the HetNets. Therefore, the efficient assignment of channels among BSs is considered to be an important issue. Furthermore, the density and heterogeneity of the networks motivate self-organizing resource management techniques. In this paper, we address the problem of channel allocation in HetNets, and propose a satisfaction based channel allocation algorithm. The problem is modeled as a game in satisfaction form, in which BSs act as the players with the constraint given by the loads at the BSs. The objective is to meet the data rate requirements of user equipments. In this regard, the BSs aim at seeking a satisfaction solution rather than the optimal one. In order to learn the satisfaction equilibrium, a fully distributed algorithm based on the individual utility is applied. Simulation results show that the proposed approach can increase the average BS's throughput compared to the benchmark algorithms.

We have recently proposed a multi-band wireless local area network (WLAN) system as a solution to the increasingly crowded frequency space. Efficiency can be improved by an agile transceiver that transmits on an idle channel on either or both bands concurrently, and a busy/idle (B/I) predictor will form part of the sensing unit for such a system. A probabilistic neural network (PNN) is studied here for predicting upcoming WLAN B/I status based on pattern matching and classification of previous state patterns. IEEE 802.11 wireless data frames were captured at two hot-spots on multiple channels and the B/I status estimated. The prediction performance is compared for two different locations, channels, prediction matrix dimensions, B/I vs channel occupancy ratio (COR) input types, and frequency of retraining. Results show that the PNN has good potential to estimate the number of idle slots in the upcoming 20 slots and the performance improves with regular retraining.

The co-existence of small cell base stations (SBSs) with conventional macrocell base station is a promising approach to boost the capacity and coverage of cellular networks. However, densifying the network with a viral deployment of SBSs can significantly increase energy consumption. To reduce the reliance on unsustainable energy sources, one can adopt self-powered SBSs that rely solely on energy harvesting. Due to the uncertainty of energy arrival and the finite capacity of energy storage systems, self-powered SBSs must smartly optimize their ON and OFF schedule. In this paper, the problem of ON/OFF scheduling of self-powered SBSs is studied, in the presence of energy harvesting uncertainty with the goal of minimizing the operational costs consisting of energy consumption and transmission delay of a network. For the original problem, we show that an algorithm can solve the problem in the illustrative case. Then, to reduce the complexity of the original problem, an approximation is proposed. To solve the approximated problem, a novel approach based on the ski rental framework, a powerful online optimization tool, is proposed. Using this approach, each SBS can effectively decide on its ON/OFF schedule autonomously, without any prior information on future energy arrivals. By using competitive analysis, a deterministic online algorithm and a randomized online algorithm (ROA) are developed. The ROA is then shown to achieve the optimal competitive ratio in the approximation problem. Simulation results show that, compared with a baseline approach, the ROA can yield performance gains reaching up to 15.6% in terms of reduced total energy consumption of SBSs and up to 20.6% in terms of per-SBS network delay reduction. The results also shed light on the fundamental aspects that impact the ON time of SBSs while demonstrating that the proposed ROA can reduce up to 69.9% the total cost compared with a baseline approach.

Next generation mobile networks will face the unprecedented demand for higher data rates. To satisfy this demand, the dense deployment of heterogeneous wireless networks (HetNets) is a promising solution. One of the major challenges in dense HetNets is to dynamically allocate the resources such as power and channel so that the energy efficiency and throughput of the network improve. One of the important techniques for improving the energy efficiency of the base station (BS) is BS ON-OFF switching which allows the BS to turn off some of its components in lower load situations. On the other side, due to the proximity of BSs in the dense HetNets, co-channel interference (CCI) becomes a critical problem and significantly impacts the performance of the network. In this paper, we propose a dynamic channel assignment based on a learning algorithm (DCA-LA). Moreover, we combine DCA-LA with a BS ON-OFF switching algorithm in order to improve the energy efficiency of the system. In particular, the proposed DCA-LA/ON-OFF switching algorithm is self-organizing and performs in a fully distributed manner. Simulation results indicate that our proposed algorithm balances load among BSs and yields better performance in terms of average energy consumption, average load, average utility per BS and average rate per user, compared to the baseline algorithms.

The viral and dense deployment of small cell base stations (SBSs) will lie at the heart of 5G cellular networks. However, such dense networks can consume a significant amount of energy. In order to reduce the network's reliance on unsustainable energy sources, one can deploy self-powered SBSs that rely solely on energy harvesting. Due to the uncertainty of energy arrival and the finite capacity of energy storage systems, self-powered SBSs must smartly schedule their ON and OFF operation. In this paper, the problem of ON/OFF scheduling of self-powered SBSs is studied in the presence of energy harvesting uncertainty with the goal of minimizing the tradeoff between power consumption and flow-level delay. To solve this problem, a novel approach based on the ski rental framework, a powerful online optimization tool, is proposed. To find the desired solution of the ski rental problem, a randomized online algorithm is developed to enable each SBS to autonomously decide on its ON/OFF schedule, without knowing any prior information on future energy arrivals. Simulation results show that the proposed algorithm can reduce power consumption and delay over a given time period compared to a baseline that turns SBSs ON by using an energy threshold. The results show that this performance gain can reach up to 12.7% reduction of the total cost. The results also show that the proposed algorithm can eliminate up to 72.5% of the ON/OFF switching overhead compared to the baseline approach.

The deployment of heterogeneous networks (Het- Nets) is a promising candidate for the next-generation wireless networks. This is envisioned to deliver higher rate data services in flexible and cost-effective manner. However, in the HetNets, due to the coexistence of various nodes, traffic load variations are more pronounced which requires more dynamic resource management. On the other side, the effective management of resource can significantly impact on the experience of user equipment (UE) and network's performance. In this paper, we address a joint UE association (UEA) and downlink resource allocation problem in the HetNets. In this regard, we propose a UEA policy based on base stations' (BSs') estimated load and signal-to-interference-and-noise-ratio at the location of UE. Furthermore, we apply a self- organizing mechanism to allocate the resource for jointly optimizing the power and channel allocation in a fully distributed way. Our proposed joint UEA and self-organizing resource allocation balances load among BSs and improves the energy efficiency of the network. Simulation results show that our proposed joint UEA and resource allocation yields significant performance gains up to 26.4% and 22% compared to the other benchmark schemes in terms of average energy consumption and average BS load, respectively.

Two important challenges facing 5G are energy efficiency and mobile users' mobility in heterogeneous wireless networks (HetNets). One of the important techniques for improving energy efficiency is base station (BS)'s switching between ON and OFF modes which allows the BS to turn off some its components in lower load situations. In this paper, we address user's seamless mobility problem and propose a handoff (HO) algorithm based on BS's estimated load. The proposed HO algorithm based on estimated load (PHA-EL) balances load by imposing HOs from highly loaded BSs to lightly loaded BSs. When a BS is overloaded, the user's quality of service (QoS) will degrade and therefore the PHA-EL is used to improve system throughput. The PHA-EL algorithm is combined with BSs which are able to switch between ON and OFF modes (PHA-EL/ON-OFF switching) in order to improve the energy efficiency of the system.Therefore, this algorithm achieves both energy- and spectral- efficiency. Simulation results indicate that the proposed algorithm yields better performance in terms of average number of HOs, average load per BS and average payoff per BS, compared to baseline algorithm.

In this paper, a cognitive machine-to-machine (M2M) communication network is considered, in which a cellular network shares the spectrum with the M2M communication network with M machine-type devices (MTDs), one half-duplex relay, and one MTD gateway for data gathering. One key challenge is that in the future 5G wireless networks, there will be billions of those small MTDs, and therefore, a MTD selection protocol is required for managing data transmission between MTDs. A joint buffer-aided MTD selection and power allocation protocol is proposed to maximize the MTDs' sum-rate provided that the induced interference to the cellular network is limited. In particular, in the proposed scheme, at each time slot and each subcarrier, the cognitive M2M network optimally decides on whether to be silent or to select either the relay or one of the MTDs for data transmission. To this end, for each MTD, there exists a buffer at the relay to avoid data loss. The closed-form expressions for the power coefficients of MTDs are calculated. Simulation results show that the proposed policy improves the sum-rate of the MTDs in comparison with the other proposed schemes for M2M communication without buffer.

The demand for wireless resources is increasing at high pace. Heterogeneous networks (HetNets), i.e., network composed by base stations (BSs) with different coverage areas, are useful solutions to cope with this increasing demand. In this paper, we propose two handover (HO) algorithms.First HO algorithm is based on user equipment's (UE's) velocity, UE's position, UE's received signal strength (RSS) from BS and traffic load of BS.In the first phase of the first HO algorithm, UE employs parameters such as the distance of UE from its connected BS, velocity of UE and UE's RSS from its connected BS to determine the necessity of HO. If HO is needed, in the second phase, i.e., HO execution phase, UE selects the new BS based on the distance of UE from BS to which UE approaches, RSS and average traffic load of BS which is advertised through beacon signal. UE uses strength of received beacon signal as RSS. UE transmits the connection request signal to selected BS. Average traffic load means time average of traffic load. UE gets information of its position and its velocity via GPS. Second HO algorithm is based on only UE's RSS from BS and traffic load of BS. This HO algorithm has similar structure as the first one. However, in the first phase, only UE's instantaneous RSS and average RSS which means time average of RSS are employed to determine the necessity of HO by UE. In HOEP, UE selects the new BS based on instantaneous RSS, average RSS and average traffic load of BS. UE transmits the connection request signal to selected BS. A game-theoretic sleep mode algorithm is executed parallel to each HO algorithm in BS. The performance of the two proposed algorithms, such as total number of HOs, UE's throughput and system power consumption is evaluated by means of computer simulation.

In this paper, a green wireless communication system in which base stations are powered by renewable energy sources is considered. This system consists of a capacity-constrained renewable power supplier (RPS) and a base station (BS) that faces a predictable random connection demand from mobile user equipments (UEs). In this model, the BS, which is powered via a combination of a renewable power source and the conventional electric grid, seeks to specify the renewable power inventory policy, i.e., the power storage level. On the other hand, the RPS must strategically choose the energy amount that is supplied to the BS. An M/M/1 make-to-stock queuing model is proposed to investigate the decentralized decisions when the two parties optimize their individual costs in a noncooperative manner. The problem is formulated as a noncooperative game whose Nash equilibrium (NE) strategies are characterized to identify the causes of inefficiency in the decentralized operation. A set of simple linear contracts are introduced to coordinate the system so as to achieve an optimal system performance. The proposed approach is then extended to a setting with one monopolistic RPS and N BSs that are privately informed of their optimal energy inventory levels. In this scenario, we show that the widely used proportional allocation mechanism is no longer socially optimal. To make the BSs truthfully report their energy demand, an incentive compatible (IC) mechanism is proposed for our model. Simulation results show that using the green energy can present significant traditional energy savings for the BS when the connection demand is not heavy. Moreover, the proposed scheme provides valuable energy cost savings by allowing the BSs to smartly use a combination of renewable and traditional energy, even when the BS has a heavy traffic of connections. Also, the results show that performance of the proposed IC mechanism will be close to the social optimal when the green energy production capacity increases.

Abstract Standard least mean square/fourth (LMS/F) is a classical adaptive algorithm that combined the advantages of both least mean square (LMS) and least mean fourth (LMF). The advantage of LMS is fast convergence speed while its shortcoming is suboptimal solution in low signal‐to‐noise ratio (SNR) environment. On the contrary, the advantage of LMF algorithm is robust in low SNR while its drawback is slow convergence speed in high SNR case. Many finite impulse response systems are modeled as sparse rather than traditionally dense. To take advantage of system sparsity, different sparse LMS algorithms withlp‐LMS andl0‐LMS have been proposed to improve adaptive identification performance. However, sparse LMS algorithms have the same drawback as standard LMS. Different from LMS filter, standard LMS/F filter can achieve better performance. Hence, the aim of this paper is to introduce sparse penalties to the LMS/F algorithm so that it can further improve identification performance. We propose two sparse LMS/F algorithms using two sparse constraints to improve adaptive identification performance. Two experiments are performed to show the effectiveness of the proposed algorithms by computer simulation. In the first experiment, the number of nonzero coefficients is changing, and the proposed algorithms can achieve better mean square deviation performance than sparse LMS algorithms. In the second experiment, the number of nonzero coefficient is fixed, and mean square deviation performance of sparse LMS/F algorithms is still better than that of sparse LMS algorithms. Copyright © 2013 John Wiley & Sons, Ltd.

In this paper, we address the resource allocation (RA) problem for uplink single input multiple output (SIMO) single carrier frequency division multiple access (SC-FDMA). A novel RA algorithm for joint user scheduling and resource block allocation is presented. Particularly, with the aim of improving the spectral efficiency (SE) and reducing the computational complexity, a sub-optimal power projection method based on users' spatial correlation is employed, which chooses the users for each RB. A parametric multiple access channel (MAC) model is used for modelling the receive antenna correlation at the base station. Our simulation results show that the proposed scheme improves the the SE considerably, while keeping the complexity at an acceptable level. We also investigate the variation of different system design parameters, i.e., RB size, number of receive antenna, etc, on the SIMO SC-FDMA uplink SE, to shed insight on the role of these parameters in system SE optimization.

Sparse system identification problems often exist in many applications, such as echo interference cancellation, sparse channel estimation, and adaptive beamforming. One of popular adaptive sparse system identification (ASSI) methods is adopting only one sparse least mean square (LMS) filter. However, the adoption of only one sparse LMS filter cannot simultaneously achieve fast convergence speed and small steady-state mean state deviation (MSD). Unlike the conventional method, we propose an improved ASSI method using affine combination of two sparse LMS filters to simultaneously achieving fast convergence and low steady-state MSD. First, problem formulation and standard affine combination of LMS filters are introduced. Then an approximate optimum affine combiner is adopted for the proposed filter according to stochastic gradient search method. Later, to verify the proposed filter for ASSI, computer simulations are provided to confirm effectiveness of the proposed filter which can achieve better estimation performance than the conventional one and standard affine combination of LMS filters.

In this paper, we propose a fairness-controlled multiuser scheduling and resource allocation (RA) algorithm for uplink spatial division multiple access (SDMA)/ single carrier frequency division multiple access (SC-FDMA). In fact, with the aim of improving the spectral efficiency (SE) and at the same time controlling the network fairness, a sub-optimal power projection method based on users' spatial correlation is employed, which chooses the users for each resource block (RB). We assume a single-input multiple-output (SIMO) scheme with correlation at receive antenna. By changing the maximum allowed number of simultaneously accessing users, we can choose the desired balance between SE and the fairness. Numerical analysis verifies that the proposed scheme improves the SE considerably at the target defined fairness level. We also investigate the variations in fairness for different system design parameters, i.e., RB size, number of receive antenna, etc.

Global connectivity, at anyplace and anytime, to provide high-speed, high-quality, and reliable communication channels, is now becoming a reality. The credit mainly goes to the recent technological advances in wireless communications which consist of a wide range of technologies and applications to fulfill the particular needs of end-users in different deployment scenarios (Bluetooth, Wi-Fi, WiMAX, and 3G/4G cellular systems). In such a heterogeneous wireless environment, one of the key ingredients to provide efficient ubiquitous computing, is the design of intelligent handoff algorithms, which select the optimal target network. This paper presents a novel approach for the design and implementation of a multi-criteria vertical handoff decision algorithm for heterogeneous wireless networks to achieve seamless mobility while maximizing end-users' satisfaction. After weighting the network parameters, a ranking algorithm based on the fuzzy extension of the the Techniques for Order Preference by Similarity to Ideal Solution (TOPSIS) is used to prioritize all the available networks within the coverage of the mobile user. Simulation results are provided and compared with a benchmark for a single user scenario.

Broadband signal transmission over frequency-selective fading channel often requires accurate channel state information at receiver. One of the most attracting adaptive channel estimation (ACE) methods is least mean square (LMS) algorithm. However, its performance is often degraded by random scaling of input training signal. To overcome this degradation, in this paper we consider the use of standard least mean square/fourth (LMS/F) algorithm. Since the broadband channel is often described by sparse channel model, such sparsity could be exploited as prior information. First, we propose an adaptive sparse channel estimation (ASCE) method with zero-attracting LMS/F (ZA-LMS/F) algorithm by introducing an ℓ1-norm sparse constraint into the cost function. Then, to exploit the sparsity more effectively, an improved ASCE with reweighted zero-attracting LMS/F (RZA-LMS/F) algorithm is proposed. For different channel sparsity, we propose a Monte Carlo method for a regularization parameter selection in RA-LMS/F and RZA-LMS/F to achieve better steady-state estimation performance. Simulation results show that the proposed ASCE methods achieve better estimation performance than the conventional one.

Channel estimation problem is one of the key technical issues in time-variant multiple-input multiple-output (MIMO) communication systems. To estimate the MIMO channel, least mean square (LMS) algorithm was applied to adaptive channel estimation (ACE). Since the MIMO channel is often described by sparse channel model#65292;such sparsity can be exploited to improve the estimation performance by adaptive sparse channel estimation (ASCE) methods using sparse LMS algorithms. However, conventional ASCE methods have two main drawbacks: 1) sensitive to random scale of training signal and 2) unstable in low signal-to-noise ratio (SNR) regime. To overcome the two harmful factors, in this paper, we propose a novel ASCE method using normalized LMS (NLMS) algorithm (ASCE-NLMS). In addition, we also proposed an improved ASCE method using normalized least mean fourth (NLMF) algorithm (ASCE-NLMF). Two proposed methods can exploit the channel sparsity effectively. Also, stability of the proposed methods is confirmed by mathematical derivation. Computer simulation results show that the proposed sparse channel estimation methods can achieve better estimation performance than conventional methods.

Channel state information (CSI) is required at receiver in orthogonal frequency division modulation (OFDM) communication systems due to the fact that frequency-selective fading channel leads to inter- symbol interference (ISI) over data transmission. Broadband channel model is often described by very few dominant channel taps and they can be probed by sparse channel estimation (SCE) methods, e.g., subspace pursuit (SP) algorithm, can take the advantage of sparse structure effectively in broadband channels as for prior information. However, these developed methods are vulnerable to both noise, interference and column coherence of training signal matrix. In other words, the primary objective of these conventional methods is to catch the dominant channel taps without a report of posterior channel uncertainty. To improve the estimation performance, we proposed a Bayesian sparse channel estimation (BSCE) method which not only exploits the channel sparsity but also mitigates the unexpected channel uncertainty. The proposed method can reveal potential ambiguity among multiple channel estimators that are ambiguous due to observation noise or correlation interference among columns in the training matrix. Computer simulations show that our technique can improve the estimation performance with comparable computational complexity when comparing with conventional SCE methods.

Accurate channel impulse response (CIR) is required for coherent detection and it also helps to improve the quality of service in next-generation wireless communication systems. Linear channel estimation methods, e.g., least square (LS), have been proposed to estimate the CIR. However, these methods never take advantage of the channel sparsity and they also cause performance loss. In this paper, we propose a sparse channel estimation method for multi-input multi-output orthogonal frequency-division multiplexing (MIMO-OFDM) amplify and forward two-way relay networks (AF-TWRN), to exploit the sparse structure information in the CIR for each user. Sparse channel estimation problem is formulated as compressed sensing (CS) using sparse decomposition theory and the estimation process is implemented by LASSO algorithm. Computer simulation results are given to confirm the superiority of the proposed method over the LS-based channel estimation.

Global roaming and high-quality wireless communication, is now becoming a reality. This is mainly due to recent technological advances in wireless communications which consist of a wide range of technologies and applications to fulfill the particular needs of end-users in different deployment scenarios (Bluetooth, Wi-Fi, WiMAX, and 3G/4G cellular systems). In such a heterogeneous wireless environment, one of the key ingredients to provide efficient ubiquitous computing is the design of intelligent handoff algorithms which select the optimal target network. This paper presents a novel approach for the design and implementation of a multi-criteria vertical handoff decision algorithm for heterogeneous wireless networks to achieve seamless mobility while maximizing end-users' satisfaction. A weighting scheme is first proposed for the system parameters and consequently, two important modules are proposed. The first module estimates the necessity of handoffs and the other module selects the best network for future connection using a FTOPSIS ranking algorithm. Performance of our scheme is illustrated by an exemplary deployment scenario and also by simulating a practical heterogeneous wireless environment for a single user scenario.

In a highly integrated ubiquitous wireless environment, the selection of a network that can fulfill end-users' service requests while keeping their overall satisfaction at a high level, is vital. The wrong selection can lead to undesirable conditions such as unsatisfied users, weak Quality of Service (QoS), network congestions, dropped and/or blocked calls, and wastage of valuable network resources. The selection of these networks is performed during the handoff process when a Mobile Station (MS) switches its current Point of Attachment (PoA) to a different network due to the degradation or complete loss of signal and/or deterioration of the provided QoS. Traditional schemes perform the handoff necessity estimation and trigger the network selection process based on a single metric such as Received Signal Strength (RSS). These schemes are not efficient enough, as they do not take into consideration the traffic characteristics, user preferences, network conditions and other important system metrics. This paper presents a novel multi-attribute vertical handoff algorithm for heterogeneous wireless networks which achieves seamless mobility while maximizing end-users' satisfaction. Two modules are designed to estimate the necessity of handoff and to select the target network. These modules utilize parallel Fuzzy Logic Controllers (FLCs) with reduced rule-set in combination with a network ranking algorithm developed based on Fuzzy VIKOR (FVIKOR).

Coherent detection and demodulation at the receiver requires channel state information (CSI). We investigate channel estimation problem in sparse multipath non-orthogonal amplify-and- forward (NAF) cooperative networks. Traditional linear estimation methods can obtain lower bound at the cost of spectrum efficiency which is becoming more and more scarcity. In this paper, system model is described from sparse representation perspective. Based on the compressed sensing theory, we propose several compressed channel estimation methods to exploit sparsity of the cooperative channels. Simulation results confirm the superiority of proposed methods than LS-based linear estimation method.

Emergence of new wireless technologies has facilitated the way to higher data rates and more robust communication links. Ultra wideband (UWB) is one of these promising technologies that, despite its several outstanding benefits, can cause interference to other networks operating in the same frequency range because of its large bandwidth. In this study, the authors introduce a solution for interference management in heterogeneous UWB networks. The analysis can be generalised for other coexistence scenarios where we have wireless networks with different specifications. Game theory is used to study the joint power and rate control problem under mutual interference between two UWB standards, namely, multiband orthogonal frequency division multiplexing UWB and direct-sequence UWB. A non-cooperative joint rate and power control game with pricing (NRPGP) in which each node seeks to choose its possible transmit power and rate in order to maximise its own utility while satisfying its target signal-to-interference-and-noise ratio as quality-of-service requirement is introduced. Simulation results are provided to evaluate the performance of the proposed game, NRPGP.

ABSTRACT The mutual interference between the two ultra wideband (UWB) technologies, which use the same frequency spectrum, will be a matter of concern in the near future. In this context, we present a performance analysis of direct‐sequence (DS) UWB communication in the presence of multiband orthogonal frequency division multiplexing (MB‐OFDM) UWB interfering transmissions. The channel fading is modeled according to Nakagami‐m distribution, and multi‐user interference is taken into account. The DS‐UWB system performance is evaluated in terms of bit error rate (BER). Specifically, using the characteristic function approach, an analytical expression for the average BER is derived conditioned on the channel impulse response. Numerical and simulation results are provided and compared for different coexistence scenarios. Copyright © 2011 John Wiley & Sons, Ltd.

Several attractive features of ultra wideband (UWB) communications make it a good candidate for physical-layer of wireless sensor networks (WSN). These features include low power consumption, low complexity and low cost of implementation. In this paper, we present an opportunistic power assignment strategy for distributed detection in parallel fusion WSNs, considering a Nakagami-m fading model for the communication channel and time-hopping (TH) UWB for the transmitter circuit of the sensor nodes. In a parallel fusion WSN, local decisions are made by local sensors and transmitted through wireless channels to a fusion center. The fusion center processes the information and makes the final decision. Simulation results are provided for the global probability of detection error and relative performance gain to evaluate the efficiency of the proposed power assignment strategy in different fading environments.

The wide spectrum of ultra wideband (UWB) communications makes it inevitable to consider strategies for avoiding and mitigating interference from narrowband wireless systems such as GPS, UMTS, and WLAN, or other UWB wireless technologies. In this paper, we provide a performance analysis of multiband orthogonal frequency division multiplexing (MB-OFDM) UWB in the presence of binary phase-shift keying time-hopping (BPSK-TH) UWB or BPSK-DS UWB interfering transmissions under Nakagami-m fading. In the bit-error rate (BER) analysis, several UWB interferers are considered to affect the MB-OFDM signal. A Gaussian approximation is considered for the UWB interferers and used in the analysis of the BER performance of the MB-OFDM UWB system. The Nakagami-m distribution is applied to characterise the amplitude of the fading channels for both the reference signal and the interference signals. Furthermore, a waveforming technique is considered for mitigating the effect of interference and its efficiency is illustrated in terms of BER improvement. Numerical and simulation results are provided and compared for different coexistence scenarios.

Ultra wideband (UWB) technology is one of the promising solutions for future short-range communication which has recently been receiving increasing attention by many researchers. So far, two standards have been proposed to the IEEE 802.15.3a task group (TG3a) as high-speed physical technologies for next-generation wireless personal area networks (WPAN). These standards are based on multiband orthogonal frequency division multiplexing (MB-OFDM) UWB or direct-sequence (DS) UWB, two different technologies that will have to coexist in the near future. In this paper, we provide a performance analysis of MB-OFDM UWB communication in the presence of binarv phase-shift keying time-hopping (BPSK-TH) UWB or BPSK-DS UWB interfering transmissions. In the bit error rate (BER) analysis, it is considered that there are multiple UWB interferers affecting the MB-OFDM signal. A Gaussian approximation is considered for the DS-UWB and TH-UWB interferers under consideration and used in the analysis of the BER performance of a MB-OFDM UWB system. Furthermore, a waveforming technique is suggested for mitigating the effect of interference and its efficiency is illustrated in terms of BER improvement. Numerical and simulation results are provided and compared for different coexistence scenarios.

Rapid growth of demand for various wireless communication services has led the industry and the researches to investigate on a new generation of telecommunication systems. One of the main objectives in designing and developing these systems is to provide the mobile user with a robust connection to different networks so that the users can move freely between heterogeneous networks while running their computing applications. Handoff is a process, which maintains a mobile user's active connection as it moves within wireless networks and vertical handoff considers handoff between different types of networks or otherwise stated between different network layers. In this paper a location estimation technique in multitier (overlay) networks with application to use with vertical handoff algorithm for optimization and network conditions improvement is proposed. First the concept of proposed algorithm for location estimation is discussed, then we talk about the simulation environment and some optimizations for our pattern classifier, finally a performance analysis demonstrates that significant improvement can be achieved from the proposed technique and its optimization.