To examine the electrophysiological underpinnings of the functional networks involved in music listening, previous approaches based on spatial independent component analysis (ICA) have recently been used to ongoing electroencephalography (EEG) and magnetoencephalography (MEG). However, those studies focused on healthy subjects, and failed to examine the group-level comparisons during music listening. Here, we combined group-level spatial Fourier ICA with acoustic feature extraction, to enable group comparisons in frequency-specific brain networks of musical feature processing. It was then applied to healthy subjects and subjects with major depressive disorder (MDD). The music-induced oscillatory brain patterns were determined by permutation correlation analysis between individual time courses of Fourier-ICA components and musical features. We found that (1) three components, including a beta sensorimotor network, a beta auditory network and an alpha medial visual network, were involved in music processing among most healthy subjects; and that (2) one alpha lateral component located in the left angular gyrus was engaged in music perception in most individuals with MDD. The proposed method allowed the statistical group comparison, and we found that: (1) the alpha lateral component was activated more strongly in healthy subjects than in the MDD individuals, and that (2) the derived frequency-dependent networks of musical feature processing seemed to be altered in MDD participants compared to healthy subjects. The proposed pipeline appears to be valuable for studying disrupted brain oscillations in psychiatric disorders during naturalistic paradigms.

We study the behavior of the belief-propagation (BP) algorithm affected by erroneous data exchange in a wireless sensor network (WSN). The WSN conducts a distributed multidimensional hypothesis test over binary random variables. The joint statistical behavior of the sensor observations is modeled by a Markov random field whose parameters are used to build the BP messages exchanged between the sensing nodes. Through linearization of the BP message-update rule, we analyze the behavior of the resulting erroneous decision variables and derive closed-form relationships that describe the impact of stochastic errors on the performance of the BP algorithm. We then develop a decentralized distributed optimization framework to enhance the system performance by mitigating the impact of errors via a distributed linear data-fusion scheme. Finally, we compare the results of the proposed analysis with the existing works and visualize, via computer simulations, the performance gain obtained by the proposed optimization.

Recent continuous task studies, such as narrative speech comprehension, show that fluctuations in brain functional connectivity (FC) are altered and enhanced compared to the resting state. Here, we characterized the fluctuations in FC during comprehension of speech and time-reversed speech conditions. The correlations of Hilbert envelope of source-level EEG data were used to quantify FC between spatially separate brain regions. A symmetric multivariate leakage correction was applied to address the signal leakage issue before calculating FC. The dynamic FC was estimated based on a sliding time window. Then, principal component analysis (PCA) was performed on individually concatenated and temporally concatenated FC matrices to identify FC patterns. We observed that the mode of FC induced by speech comprehension can be characterized with a single principal component. The condition-specific FC demonstrated decreased correlations between frontal and parietal brain regions and increased correlations between frontal and temporal brain regions. The fluctuations of the condition-specific FC characterized by a shorter time demonstrated that dynamic FC also exhibited condition specificity over time. The FC is dynamically reorganized and FC dynamic pattern varies along a single mode of variation during speech comprehension. The proposed analysis framework seems valuable for studying the reorganization of brain networks during continuous task experiments.

In this paper, we investigate distributed inference schemes, over binary-valued Markov random fields, which are realized by the belief propagation (BP) algorithm. We first show that a decision variable obtained by the BP algorithm in a network of distributed agents can be approximated by a linear fusion of all the local log-likelihood ratios. The proposed approach clarifies how the BP algorithm works, simplifies the statistical analysis of its behavior, and enables us to develop a performance optimization framework for the BP-based distributed inference systems. Next, we propose a blind learning-adaptation scheme to optimize the system performance when there is no information available a priori describing the statistical behavior of the wireless environment concerned. In addition, we propose a blind threshold adaptation method to guarantee a certain performance level in a BP-based distributed detection system. To clarify the points discussed, we design a novel linear-BP-based distributed spectrum sensing scheme for cognitive radio networks and illustrate the performance improvement obtained, over an existing BP-based detection method, via computer simulations.

Blockchain has been gradually applied to different Internet-of-Things platforms. As the efficiency of the blockchain mainly depends on the network computing capability, how to make sure the acquisition of the computational resources and participation of the devices would be the driving force. In this article, we focus on investigating incentive mechanism for rational miners to purchase the computational resources. An edge-computing-based blockchain network is considered, where the edge service provider (ESP) can provide computational resources for the miners. Accordingly, we formulate a two-stage Stackelberg game between the miners and ESP. The aim is to investigate SE of the optimal mining strategy under the two different mining schemes, in order to find the optimal incentive for the ESP and miners to choose autofit strategies. Through theoretical analysis and numerical simulations, we can demonstrate the effectiveness of the proposed scheme on encouraging devices to participate the blockchain.

To accommodate the explosively growing demands for mobile traffic service, wireless network virtualization is proposed as the main evolution towards 5G. In this work, a novel contract theoretic incentive mechanism is proposed to study how to manage the resources and provide services to the users in the wireless virtualized networks. We consider that the infrastructure providers (InPs) own the physical networks and the mobile virtual network operator (MVNO) has the service information of the users and needs to lease the physical radio resources for providing services. In particular, we utilize the contract theoretic approach to model the resource trading process between the MVNO and multiple InPs. Two scenarios are considered according to whether the information (such as the radio resource they can provide) of the InPs are globally known. Subsequently, the corresponding optimal contracts regarding the user association and transmit power allocation are derived to maximize the payoff of the MVNOs while maintaining the requirements of the InPs in the trading process. To evaluate the proposed scheme, extensive simulation studies are conducted. It can be observed that the proposed contract theoretic approach can effectively stimulate InPs' participation, improve the payoff of the MVNO, and outperform other schemes.

In this paper, we disclose the statistical behavior of the max-product algorithm configured to solve a maximum a posteriori estimation problem in a network of distributed agents. Specifically, we first build a distributed hypothesis test conducted by a max-product iteration over a binary-valued pairwise Markov random field and show that the decision variables obtained are linear combinations of the local log-likelihood ratios observed in the network. Then, we use these linear combinations to formulate the system performance in terms of the false-alarm and detection probabilities. Our findings indicate that, in the hypothesis test concerned, the optimal performance of the max-product algorithm is obtained by an optimal linear data-fusion scheme and the behavior of the max-product algorithm is very similar to the behavior of the sum-product algorithm. Consequently, we demonstrate that the optimal performance of the max-product iteration is closely achieved via a linear version of the sum-product algorithm, which is optimized based on statistics received at each node from its one-hop neighbors. Finally, we verify our observations via computer simulations.

Tensor decomposition is a powerful tool for analyzing multiway data. Nowadays, with the fast development of multisensor technology, more and more data appear in higher-order (order > 4) and nonnegative form. However, the decomposition of higher-order nonnegative tensor suffers from poor convergence and low speed. In this study, we propose a new nonnegative CANDECOM/PARAFAC (NCP) model using proximal algorithm. The block principal pivoting method in alternating nonnegative least squares (ANLS) framework is employed to minimize the objective function. Our method can guarantee the convergence and accelerate the computation. The results of experiments on both synthetic and real data demonstrate the efficiency and superiority of our method.

Real-world data exhibiting high order/dimensionality and various couplings are linked to each other since they share some common characteristics. Coupled tensor decomposition has become a popular technique for group analysis in recent years, especially for simultaneous analysis of multi-block tensor data with common information. To address the multiblock tensor data, we propose a fast double-coupled nonnegative Canonical Polyadic Decomposition (FDC-NCPD) algorithm in this study, based on the linked CP tensor decomposition (LCPTD) model and fast Hierarchical Alternating Least Squares (Fast-HALS) algorithm. The proposed FDCNCPD algorithm enables simultaneous extraction of common components, individual components and core tensors from tensor blocks. Moreover, time consumption is greatly reduced without compromising the decomposition quality when handling large-scale tensor blocks. Simulation experiments of synthetic and real-world data are conducted to demonstrate the superior performance of the proposed algorithm.

The characterization of dynamic electrophysiological brain activity, which form and dissolve in order to support ongoing cognitive function, is one of the most important goals in neuroscience. Here, we introduce a method with tensor decomposition for measuring the task-induced oscillations in the human brain using electroencephalography (EEG). The time frequency representation of source-reconstructed single-trail EEG data constructed a third-order tensor with three factors of time · trails, frequency and source points. We then used a non-negative Canonical Polyadic decomposition (NCPD) to identify the temporal, spectral and spatial changes in electrophysiological brain activity. We validate this method using both simulation EEG data and real EEG data recorded during a task of irony comprehension. The results demonstrated that proposed method can track dynamics of the temporal-spectral modes of the rhythm in the brain on a timescale commensurate to the task they are undertaking.

Radio Frequency (RF) fingerprinting-based localization has become a research interest due to its minimum hardware requirement and satisfiable positioning accuracy. However, despite the significant attention this topic has gained, most of the research focused on the calculation of position estimates. In this paper, we propose a simple and novel method that can be used as an indicator of fingerprinting positioning error. The method is based on cluster radius evaluation of multiple fingerprinting data during the test phase, which can be used by a Location Based Service (LBS) provider to predict the user position estimation accuracy. This method can be used effectively in real-time to predict the estimation error and thereby assists the LBS to offer a better quality of service. The cluster radius also reveals the quality of the recorded radio map.

Digital technologies are rapidly shaping the modern concepts of urbanization. It is a key element of developing practical smart cities of the future. In fact, they are the catalyst for the increasing networking of all areas of life in a smart city. Recent development in the domain of communication technologies has opened new avenues to realize the concept of smart cities. One of such communication technology is non-orthogonal multiple access (NOMA) for future cellular communications. This article, therefore, focuses on the interference management of uplink cellular NOMA systems. Specifically, we propose a power optimization technique for NOMA to improve the sum-rate in a multi-cell environment. We also consider Nakagami-m faded links to analyze the applicability of our proposed scheme under various channel conditions. The simulation results show that the proposed NOMA approach outperforms conventional orthogonal multiple access (OMA) technique in the multi-cell uplink scenario.

In this paper, an energy-efficient resource allocation problem is investigated for the wireless power transfer (WPT)-enabled OFDMA multicell networks. In the considered system, multiple base stations (BSs) with a large number of antennas are responsible to provide WPT in the downlink, and the users can recycle and utilize the received energy for uplink data transmission. The role of BS is to execute WPT; thus, there are no data transmissions in the downlink. A time-division protocol is considered to divide the time of downlink WPT and uplink wireless information transfer into separate time slots. With the objective to improve the energy efficiency, we propose the time, subcarrier, and power allocation schemes and antenna selection algorithms. As the perfect channel state information (CSI) is hard to obtain in the practical systems, we also take the case where only estimated CSI is available into consideration when executing resources allocation decisions and analyze the corresponding performance. Due to the non-convexity of the formulated optimization problem, we first apply the nonlinear programming scheme to convert it to a convex optimization problem. Then, an efficient alternating direction method of multipliers-based distributed resource allocation algorithm is applied to address the transformed problem. Performance evaluations are conducted to demonstrate the advantages of the proposed schemes.

Energy harvesting relays are predicted to play a pivotal role in large scale networks. This paper evaluates the secrecy performance of a system that employs a two-way decode-and-forward relay assisting transmission between two nodes. The relay has RF energy harvesting capability and it can receive energy from the RF signal and the transmission of the system can be overheard by an eavesdropper. More specifically, we derive an exact expression for the interception probability when the main and wiretap links experience generalized κ-μ fading. The impacts of the power- splitting factor at the relay and the fading parameters on the secrecy performance of the considered system are also assessed. Numerical and simulation results are presented to verify the derived results.

The advancement in vehicles has enabled crowdsensing in vehicular fog computing (VFC), where vehicles are recruited to be assigned different subtasks and participate sensing activities that may disclose their sensitive information. To stimulate more participants, VFC systems should be able to provide reliable and privacy-preserving data transmission and processing mechanisms for the sensing report. To ensure the report process, we present a reliable and privacy- preserving task recomposition (REPTAR) for multiple subtasks sensing in VFC. Modified homomorphic Paillier encryption and superincreasing sequence are employed for aggregating hybrid subtasks into one ciphertext. Reliability is verified by means and variances of each aggregated subtasks from different vehicular fog nodes. Detailed security analysis and performance evaluation are provided to demonstrate the security, privacy-enhancement, efficiency and low complexity of the proposed REPTAR.

In this work, we propose a distributed user association and resource allocation scheme for the wireless virtualized information-centric networks with hybrid energy supply, where the base station (BS) equipped with caching and energy harvesting capabilities to reduce the COPEX and OPEX cost. In particular, with the objective to obtain the utility maximization for the network operators, a joint user association, caching, spectrum and power problem is presented. To tackle the formulated mixed combinatorial and non-convex optimization problem with low complexity, the original problem is divided into two subproblems and we propose an alternating direction method of multipliers (ADMM)-based distributed algorithm to address them efficiently and effectively. Extensive simulation studies demonstrate the advantages of our presented system architecture and proposed schemes.

In this study, an energy‐efficient optimisation scheme for a large‐scale multiple‐antenna system with wireless power transfer (WPT) is presented. In the considered system, the user is charged by a base station with a large number of antennas via downlink WPT and then utilises the received power to carry out uplink data transmission. Novel antenna selection, time allocation and power allocation schemes are presented to optimise the energy efficiency of the overall system. In addition, the authors also consider channel state information cannot be perfectly obtained when designing the resource allocation schemes. The non‐linear fractional programming‐based algorithm is utilised to address the formulated problem. Their proposed schemes are validated by extensive simulations and it shows superior performance over the existing schemes.

Fog computing system is an emergent architecture for providing computing, storage, control, and networking capabilities for realizing Internet of Things. In the fog computing system, the mobile devices (MDs) can offload its data or computational expensive tasks to the fog node within its proximity, instead of distant cloud. Although offloading can reduce energy consumption at the MDs, it may also incur a larger execution delay including transmission time between the MDs and the fog/cloud servers, and waiting and execution time at the servers. Therefore, how to balance the energy consumption and delay performance is of research importance. Moreover, based on the energy consumption and delay, how to design a cost model for the MDs to enjoy the fog and cloud services is also important. In this paper, we utilize queuing theory to bring a thorough study on the energy consumption, execution delay, and payment cost of offloading processes in a fog computing system. Specifically, three queuing models are applied, respectively, to the MD, fog, and cloud centers, and the data rate and power consumption of the wireless link are explicitly considered. Based on the theoretical analysis, a multiobjective optimization problem is formulated with a joint objective to minimize the energy consumption, execution delay, and payment cost by finding the optimal offloading probability and transmit power for each MD. Extensive simulation studies are conducted to demonstrate the effectiveness of the proposed scheme and the superior performance over several existed schemes are observed.

In this paper, we investigate the energy efficient computation offloading scheme in a multi-user fog computing system. We consider the users need to make the decision on whether to offload the tasks to the fog node nearby, based on the energy consumption and delay constraint. In particular, we utilize queuing theory to bring a thorough study on the energy consumption and execution delay of the offloading process. Two queuing models are applied respectively to model the execution processes at the mobile device (MD) and fog node. Based on the theoretical analysis, an energy efficient optimization problem is formulated with the objective to minimize the energy consumption subjects to execution delay constraints. In order to address the formulated problem, an alternating direction method of multipliers (ADMM)-based distributed algorithm is proposed. Extensive simulation studies are conducted to demonstrate the effectiveness of the proposed scheme and the superior performance over the other existed schemes can be observed.

Mobile-edge cloud computing is a new cloud platform to provide pervasive and agile computation augmenting services for mobile devices (MDs) at anytime and anywhere by endowing ubiquitous radio access networks with computing capabilities. Although offloading computations to the cloud can reduce energy consumption at the MDs, it may also incur a larger execution delay. Usually the MDs have to pay cloud resource they used. In this paper, we utilize queuing theory to bring a thorough study on the energy consumption, execution delay and price cost of offloading process in a mobile-edge cloud system. Specifically, both wireless transmission and computing capabilities are explicitly and jointly considered when modelling the energy consumption and delay performance. Based on the theoretical analysis, the multi-objective optimization problem is formulated with the joint objectives to minimize the energy consumption, execution delay and price cost by finding the optimal offloading probability and optimal transmission power for each MD. The scalarization scheme and interior point method are applied to address the formulated problem. Through extensive simulations, the effectiveness of the proposed scheme can be demonstrated.

In this paper, we investigate the energy efficient resource allocation problem for the wireless powered OFDMA multi-cell networks. In the considered system, the users who have data to transmit in the uplink are empowered by the wireless power obtained from multiple base stations (BSs) with a large scale of multiple antennas in the downlink. A time division protocol is considered to divide the time of wireless power transfer (WPT) in the downlink and wireless information transfer (WIT) in the uplink into separate time slot. With the objective to improve the energy efficiency (EE) of the system, we propose the antenna selection, time allocation, subcarrier and power allocation schemes. Due to the non-convexity of the formulated optimization problem, we first apply the nonlinear programming scheme to convert it to a convex optimization problem and then address it through an efficient alternating direction method of multipliers based distributed resource allocation algorithm. Extensive simulations are conducted to show the effectiveness of proposed schemes.

In this paper, a new cooperation structure for spectrum sensing in cognitive radio networks is proposed, which outperforms the existing commonly used ones in terms of energy efficiency. The efficiency is achieved in the proposed design by introducing random interruptions in the cooperation process between the sensing nodes and the fusion center, along with a compensation process at the fusion center. Regarding the hypothesis testing problem concerned, first, the proposed system behavior is thoroughly analyzed and its associated likelihood-ratio test is provided. Next, based on a general linear fusion rule, the statistics of the global test summary are derived and the sensing quality is characterized in terms of the probability of false alarm and probability of detection. Then, the optimization of the overall detection performance is formulated according to the Neyman-Pearson criterion (NPC) and it is discussed that the optimization required is indeed a decision-making process with uncertainty which incurs prohibitive computational complexity. The NPC is then modified to achieve a good affordable solution by using semidefinite programming (SDP) techniques and it is shown that this new solution is nearly optimal according to the deflection criterion. Finally, the effectiveness of the proposed architecture and its associated SDP is demonstrated by simulation results.

Wireless network virtualization is envisioned as a promising framework to provide efficient and customized services for next-generation wireless networks. In wireless virtualized networks (WVNs), limited radio resources are shared among different services providers for providing services to different users with heterogeneous demands. In this paper, we propose a resource allocation scheme for an orthogonal frequency division multiplexing-based WVN, where one small cell base station equipped with a large number of antennas serves the users with different service requirements. In particular, with the objective to obtain the energy efficiency in the uplink, a joint power, subcarrier, and antenna allocation problem is presented considering availability of both perfect and imperfect channel state information. Subsequently, relaxation and variable transformation are applied to develop the efficient algorithm to solve the formulated non-convex and combinational optimization problem. Extensive simulation studies demonstrate the advantages of our presented system architecture and proposed schemes.

In addition to comforting passengers' journey, the modern railway system is responsible to support a variety of on-board Internet services to meet the passenger's demands on seamless service provisioning. In order to provide wireless access to the train, one idea attracting increasing attention is to deploy a series of track-side access points (TAPs) with high-speed data rates along the rail lines dedicated to the broadband mobile service provisioning on board. Due to the heavy data traffic flushing into the base stations (BSs) of the cellular networks, TAPs act as a complement to the BSs in data delivery. In this paper, we focus on the TAP association problem for service provisioning in a heterogeneous wireless railway network, where the TAP and BS coexist by applying a queueing game theoretic approach. Specifically, we present comprehensive theoretical analysis of the delay performance on the circumstances of partially observed, totally unobserved, and totally observed state of the system. Moreover, based on the considered payoff model and the derived association delay time, the passenger's equilibrium strategies on association behaviors, i.e., whether to associate with a TAP or not, are studied. Finally, performance evaluations and discussions are provided to illustrate our proposed passenger-TAP association scheme for the heterogeneous wireless railway communication system.

Introducing random interruptions in the cooperation of the sensing nodes with the fusion center is a new approach to increase the efficiency of the centralized cooperative spectrum sensing in cognitive radio (CR) networks. In this paper, we extend this approach to the multiband cooperative spectrum sensing. Specifically, first, we formally show how to incorporate the random interruptions in a multiband joint detection scenario. Next, we formulate the system performance in terms of the \emph{aggregate opportunistic throughput} of the CR network and the \emph{aggregate interference} experienced by the primary users. Then, based on these metrics, we formulate the system performance optimization subject to a constraint on the energy and signal processing resources consumed in the mutiband cooperative sensing. Finally, we demonstrate the effectiveness of the proposed method and optimization via computer simulations.

This paper proposes an energy-efficient resource allocation scheme for a wireless power transfer (WPT) enabled multi-user massive MIMO system with imperfect channel estimation. In the considered system, the users who have data to be transmitted only can be empowered by the WPT in the downlink from a base station (BS) with a large scale of multiple antennas. The problem of optimizing the energy efficiency objective is formulated with consideration of beamforming design, antenna selection, power allocation and time division protocol based on the practical consideration, i.e., imperfect channel state information (CSI) at the BS. In particular, the proposed antenna selection scheme is to find the optimal number of antennas and then employ the energy beamforming. The nonlinear fractional programming based scheme is utilized to address optimization of energy efficiency and to find the optimal power and time allocation. Performance evaluation is presented to demonstrate the effectiveness of the proposed schemes.

In this work, we investigate the problem of context-aware data caching in the heterogeneous small cell networks (HSCNs) to provide satisfactory to the end-users in reducing the service latency. In particular, we explore the storage capability of base stations (BSs) in HSCNs and propose a data caching model consists of edge caching elements (CAEs), small cell base stations (SBSs), and macro cell BS (MBS). Then, we concentrate on how to efficiently match the data contents to the different cache entities in order to minimize the overall system service latency. We model it as a distributed college admission (CA) stable matching problem and tackle this issue by utilizing contextual information to generate the preference lists of cache entities and contents, respectively. In the CA model, we leverage the resident-oriented Gale-Shapley (RGS) algorithm to find a stable matching between the contents and the cache entities. Through numerical results, we illustrate the advantages of of our proposed methods.

In this paper, we address the energy efficient resource allocation problem for a secure orthogonal frequency division multiple access (OFDMA) relay system. In particular, we consider there is a eavesdropper near the base station (BS) which tries to overtake the information sent by BS. We formulate the joint optimization problem with the objective to optimize the energy efficiency of the considered system by considering subcarrier pairing, secret data rate and power allocation. In addition, the system can assign different priority to different users such that the security of information transmission can be guaranteed. The proposed iterative algorithm not only maximizes the system energy efficiency but can guarantee quality-of-service (QoS) properties. Simulation results show that the proposed scheme can obtain the energy efficiency maximization and also illustrate that the strategy of giving different priority to different users obtain a higher energy efficiency.

Energy-efficiency (EE) is critical for cognitive device-to-device (D2D) communications due to limited battery capacity of user equipments (UEs) and hash quality of service (QoS) requirements. In this paper, we address the EE optimization problem by proposing a game theory and matching based resource allocation algorithm. Noncooperative game is adopted to analyze the interactions among UEs and establish mutual preferences, both of which vary dynamically with channel states and interference levels. We then employs the Gale-Shapley (GS) algorithm to match D2D pairs with cellular UEs (CUs), which is proved to be stable and weak Pareto optimal. We also extend the algorithm to address scalability issues in large-scale networks by introducing some tie-breaking and preference deletion rules. Simulation results demonstrate that the proposed algorithm achieves significant EE performance and UE satisfaction gains compared to heuristic algorithms.

In order to fully enjoy high rate broadband multimedia services, prolonging the battery lifetime of user equipment is critical for mobile users, especially for smartphone users. In this paper, the problem of distributing cellular data via a wireless power transfer enabled collaborative mobile cloud (WeCMC) in an energy efficient manner is investigated. WeCMC is formed by a group of users who have both functionalities of information decoding and energy harvesting, and are interested for cooperating in downloading content from the operators. Through device-to-device communications, the users inside WeCMC are able to cooperate during the downloading procedure and offload data from the base station to other WeCMC members. When considering multi-input multi-output wireless channel and wireless power transfer, an efficient algorithm is presented to optimally schedule the data offloading and radio resources in order to maximize energy efficiency as well as fairness among mobile users. Specifically, the proposed framework takes energy minimization and quality of service requirement into consideration. Performance evaluations demonstrate that a significant energy saving gain can be achieved by the proposed schemes.

An extension of group independent component analysis (GICA) is introduced, where multi-set canonical correlation analysis (MCCA) is combined with principal component analysis (PCA) for three-stage dimension reduction. The method is applied on naturalistic functional MRI (fMRI) images acquired during task-free continuous music listening experiment, and the results are compared with the outcome of the conventional GICA. The extended GICA resulted slightly faster ICA convergence and, more interestingly, extracted more stimulus-related components than its conventional counterpart. Therefore, we think the extension is beneficial enhancement for GICA, especially when applied to challenging fMRI data.

Automated and timely detection of malfunctioning cells in Long-Term Evolution (LTE) networks is of high importance. Sleeping cell is a particular type of cell degradation hardly detectable by traditional network monitoring systems. Recent introduction of Minimization of Drive Test (MDT) functionality enables to collect user-level statistics from regular user devices without expensive and time-consuming drive-test and measurement campaigns. In this study data mining techniques are used to process MDT measurements to detect efficiently a sleeping cell. The developed earlier data mining framework is briefly overviewed in the paper. Special attention is devoted to post-processing stage as one of the key elements of the detection scheme. In practice, location information of collected measurements might contain considerable errors. This factor impacts the precision of malfunctioning cell detection. Therefore several post-processing algorithms are proposed, where location accuracy is taken into account. The performance of the algorithms is compared based on the results of thorough system-level LTE network simulations. Combined post-processing method shows the best reliability against location errors in terms of Root Mean Squared Error (RMSE) and percent gain.

Interrupted reporting has recently been introduced as an effective method to increase the energy efficiency of cooperative spectrum sensing schemes in cognitive radio networks. In this paper, joint optimization of the reporting and fusion phases in a cooperative sensing with interrupted reporting is considered. This optimization aims at finding the best weights used at the fusion center to construct a linear fusion of the received interrupted reports, jointly with Bernoulli distributions governing the statistical behavior of the interruptions. The problem is formulated by using the deflection criterion and as a nonconvex quadratic program which is then solved for a suboptimal solution, in a computationally-affordable fashion, by a semidefinite relaxation technique. The system performance is then demonstrated by a set of numerical results which compare the performance of the system for the cases with and without the optimal linear fusion.

In this work, a game theoretical approach for addressing the virtual network service providers (NSPs), small cell provider (SCP) and user interaction in heterogenous small cell networks is presented. In particular, we consider the users can select the services of different NSPs based on their prices. The NSPs have no dedicated hardware and need to rent from the SCP in term of radio resources, e.g., small cell base stations (SBSs) in order to provide satisfied services to the users. Due to the fact that the selfish parties involved aim at maximizing their own profits, a hierarchical dynamic game framework is presented to address interactive decision problem. In the lower-level, a Stackelberg game is formulated to model and analyze the adaptive service selection of non-atomic users. In the upper-level, the NSPs and SCP sequentially determine the leasing and pricing strategies, respectively, by taking into account the service selection in the lower-level game. Performance evaluation shows the effectiveness and advantages of the proposed game theoretic approaches.

In this paper we propose a novel cluster-based RF fingerprinting method for outdoor user-equipment (UE) positioning using both LTE and WLAN signals. It uses a simple cost effective agglomerative hierarchical clustering with Davies-Bouldin criterion to select the optimal cluster number. The positioning method does not require training signature formation prior to UE position estimation phase. It is capable of reducing the search space for clustering operation by using LTE cell-ID searching criteria. This enables the method to estimate UE positioning in short time with less computational expense. To validate the cluster-based positioning real-time field measurements were collected using readily available cellular mobile handset equipped with Nemo Handy software. Output results of the proposed method were compared with a single grid-cell layout based RF fingerprinting method. Simulation results show that if a single LTE and six WLAN signal strengths are used then the proposed method can improve positioning accuracy of 35% over the grid-based RF fingerprinting.

The Sleeping Cell problem is a particular type of cell degradation in Long-Term Evolution (LTE) networks. In practice such cell outage leads to the lack of network service and sometimes it can be revealed only after multiple user complains by an operator. In this study a cell becomes sleeping because of a Random Access Channel (RACH) failure, which may happen due to software or hardware problems. For the detection of malfunctioning cells, we introduce a data mining based framework. In its core is the analysis of event sequences reported by a User Equipment (UE) to a serving Base Station (BS). The crucial element of the developed framework is an anomaly detection algorithm. We compare performances of distance, centroid distance and probabilistic based methods, using Receiver Operating Characteristic (ROC) and Precision-Recall curves. Moreover, the theoretical comparison of the methods' computational efficiencies is provided. The sleeping cell detection framework is verified by means of a dynamic LTE system simulator, using Minimization of Drive Testing (MDT) functionality. It is shown that the sleeping cell can be pinpointed.

This paper proposes a generic measurement architecture for automating the collection of User Equipment (UE) radio measurements to minimize the need of manual drive-tests in heterogeneous small cell networks consisting of LTE (Long Term Evolution) and WLAN (Wireless Local Area Network) access nodes. The proposed architecture relies on Minimization of Drive Tests (MDT) functionality specified in Third Generation Partnership Project (3GPP) and consists of user plane solution with trace assistance. In the proposed solution, trace assistance information, which is signaled to UE as part of MDT configuration, is provided to UE WLAN measurement unit allowing exchanging and correlating measurements later between LTE and WLAN network management systems. In addition, the prospects and the constraints of the architecture are discussed highlighting the importance in various operator use cases. Finally, the RF fingerprint positioning performance improvement of the proposed concept is analyzed by means of field measurements.

In order to fully exploit the high speed broadband multimedia services, prolonging the battery life of user equipment is critical, especially for the current smartphones. In this work, we investigate the problem of designing a content sharing collaborative mobile cloud (CMC) via user cooperation to reduce the energy consumption at terminal side. Given a group of users interested in downloading the same content from an operator, a grouping and scheduling based algorithm is proposed in order to select the proper data receiver in each scheduling time. The objective of the presented algorithm is to obtain energy efficiency as well as user fairness among the members of CMC. The proposed scheme can take both base station and terminal aspects into consideration and it is shown that the significant energy saving performance can be achieved without scarifying and drowning the battery of any terminal.

Maximizing the so-called deflection coefficient is commonly used as an effective approach to design cooperative sensing schemes with low computational complexity. In this paper, an extension to the deflection coefficient is proposed which captures the effects of the quantization processes at the sensing nodes, jointly with the impact of linear combining at the fusion center. The proposed parameter is then used to formulate a new mixed-integer nonlinear programming problem as a fast suboptimal method to design a distributed detection scenario where the nodes report their sensing outcomes to a fusion center through nonideal digital links. Numerical evaluations show that the performance of the proposed method is very close to the optimal case.

Sleeping cell problem is a particular type of cell degradation. There are various software and hardware reasons that might cause such kind of cell outage. In this study a cell becomes sleeping because of Random Access Channel (RACH) failure. This kind of network problem can appear due to misconfiguration, excessive load or software/firmware problem at the Base Station (BS). In practice such failure might cause network performance degradation, which is hardly traceable by an operator. In this paper we present a data mining based framework for the detection of problematic cells. In its core is the analysis of event sequences reported by a User Equipment (UE) to a serving BS. The choice of N in N-gram feature selection algorithm is considered, because of its significant impact on computational efficiency. Moreover, qualitative and heuristic performance metrics have been developed to assess the performance of the proposed detection algorithm. Sleeping cell detection framework is verified by means of dynamic LTE (Long-Term Evolution) system simulator, using Minimization of Drive Testing (MDT) functionality. It is shown that sleeping cell can be determined with very high reliability even using 1-gram algorithm.

Non-negative tensor factorization (NTF) has been successfully applied to analyze event-related potentials (ERPs), and shown superiority in terms of capturing multi-domain features. However, the time-frequency representation of ERPs by higher-order tensors are usually large-scale, which prevents the popularity of most tensor factorization algorithms. To overcome this issue, we introduce a non-negative canonical polyadic decomposition (NCPD) based on low-rank approximation (LRA) and hierarchical alternating least square (HALS) techniques. We applied NCPD (LRAHALS and benchmark HALS) and CPD to extract multi-domain features of a visual ERP. The features and components extracted by LRAHALS NCPD and HALS NCPD were very similar, but LRAHALS NCPD was 70 times faster than HALS NCPD. Moreover, the desired multi-domain feature of the ERP by NCPD showed a significant group difference (control versus depressed participants) and a difference in emotion processing (fearful versus happy faces). This was more satisfactory than that by CPD, which revealed only a group difference.

This paper addresses the resource allocation problem in collaborative relay-assisted OFDMA networks. Recent works on the subject usually ignored either the selection of relays, asymmetry of the source-to-relay and relay-to-destination links or the imperfections of channel state information. In this article we take into account all these together and our focus is two-fold. Firstly, we consider the problem of asymmetric radio resource allocation, where the objective is to maximize the system throughput of the source-to-destination link under various constraints. In particular, we consider optimization of the set of collaborative relays and link asymmetries together with subcarrier and power allocation. Using a dual approach, we solve each sub-problem in an asymptotically optimal and alternating manner. Secondly, we pay attention to the effects of imperfections in the channel-state information needed in resource allocation decisions. We derive theoretical expressions for the solutions and illustrate them through simulations. The results validate clearly the additional performance gains through an asymmetric cooperative scheme compared to the other recently proposed resource allocation schemes.

Future wireless communication systems are expected to offer several gigabits data rate. It can be anticipated that the advanced communication techniques can enhance the capability of mobile terminals (MTs) to support high data traffic. However, aggressive technique induces high energy consumption for the circuits of MTs, which drain the batteries fast and consequently limit mobility. In order to solve such a problem, a scheme called distributed mobile cloud (DMC) is foreseen as one of the potential solutions to reduce energy consumption per node in a network by exploiting collaboration within a cluster of nearby mobile terminals. In this paper we provide a detailed analysis of the energy consumption of a terminal joining the DMC and also analyze the conditions for energy savings opportunities. Numerical results are also provided to illustrate the analysis and show the potential of significant reduction of the per-node energy consumption in the mobile cloud concept.

This work addresses the radio resource allocation problem for cooperative relay assisted OFDMA wireless network. The relays adopt the decode-and-forward protocol and can cooperatively assist the transmission from source to destination. Recent works on the subject have mainly considered symmetric source-to-relay and relay-to-destination resource allocations, which limits the achievable gains through relaying. In this paper we consider the problem of asymmetric radio resource allocation, where the objective is to maximize the system throughput of the source-to-destination link under various constraints. In particular, we consider optimization of the set of cooperative relays and link asymmetries together with subcarrier and power allocations. We derive theoretical expressions for the solutions and illustrate them through simulations. The results validate clearly the additional performance gains through asymmetric cooperative scheme compared to the other recently proposed resource allocation schemes.

Sleeping cell detection in a wireless network means to find the cells which are not working properly due to various reasons. The research in the area has mostly focused on cell outage detection, e.g. due to hardware failures at the base station antennas or non-optimal network planning. In this paper we extend the research into a more challenging setting which is overlooked in the literature: the case where no outages occur in the network. The essence of the proposed method for detection of problematic cells is to analyze the sequences of the events reported by the mobile terminals to the serving base stations. The suggested n-gram analysis includes dimensionality reduction and classification of the data and ends up with providing a set of abnormal users, which at the end reveal the location of the problematic cell. We verify the proposed framework with simulated LTE network data and using the minimization of drive testing (MDT) functionality to gather the training and testing data sets.

This study presents a method to analyze blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) signals associated with listening to continuous music. Semi-blind independent component analysis (ICA) was applied to decompose the fMRI data to source level activation maps and their respective temporal courses. The unmixing matrix in the source separation process of ICA was constrained by a variety of acoustic features derived from the piece of music used as the stimulus in the experiment. This allowed more stable estimation and extraction of more activation maps of interest compared to conventional ICA methods.

Future wireless services induces higher demands for the circuits of mobile terminals, which will subsequently increase energy usage and hence limit users' abilities to experience high quality of multimedia services offered by the high data rate wireless systems. In order to address this problem, we advocate a model called collaborative mobile cluster (CMC), that is foreseen as one of the potential solutions to reduce energy consumption per terminal in a network by enabling collaboration within a cluster of mobile terminals. We first compare the energy efficiency performance of unicast and multicast transmission strategies within the CMC. In addition, we propose an algorithm that can dynamically use unicast as an additional support for multicast, ultimately overcoming the inherent drawbacks of sole multicast. Analytical results are derived and illustrated by simulations. The analysis demonstrates that: i) CMC enables a great potential to reduce the per-terminal energy consumption; ii) unicast and multicast transmissions are two optional candidates, but a proper combination of them allows better energy saving gain while still fulfilling the minimum data rate requirement.

Non-negative Canonical Polyadic decomposition (NCPD) and non-negative Tucker decomposition (NTD) were compared for extracting the multi-domain feature of visual mismatch negativity (vMMN), a small event-related potential (ERP), for the cognitive research. Since signal-to-noise ratio in vMMN is low, NTD outperformed NCPD. Moreover, we proposed an approach to select the multi-domain feature of an ERP among all extracted features and discussed determination of numbers of extracted components in NCPD and NTD regarding the ERP context.

This study proposes a novel approach for the analysis of brain responses in the modality of ongoing EEG elicited by the naturalistic and continuous music stimulus. The 512-second long EEG data (recorded with 64 electrodes) are first decomposed into 64 components by independent component analysis (ICA) for each participant. Then, the spatial maps showing dipolar brain activity are selected in terms of the residual dipole variance through a single dipole model in brain imaging, and clustered into a pre-defined number (estimated by the minimum description length) of clusters. Subsequently, the temporal courses of the EEG theta and alpha oscillations of each component for each cluster are produced and correlated with the temporal courses of tonal and rhythmic features of the music. Using this approach, we found that the extracted temporal courses of the theta and alpha oscillations along central and occipital area of scalp in two of the selected clusters significantly correlated with the musical features representing progressions in the rhythmic content of the stimulus. We suggest that this demonstrates that with the proposed approach, we have managed to discover what kinds of brain responses were elicited when a participant was listening continuously to the long piece of naturalistic music.

Recently, the IEEE 802.11 standard based Wireless Local Area Networks (WLAN) have become more popular and are widely deployed. It is anticipated that WLAN will play an important rule in the future wireless communication systems in order to provide several gigabits data rate. IEEE 802.11ac is one of the ongoing WLAN standard aiming to support very high throughput (VHT) with data rate of up to 6 Gbps below the 6 GHz band. In the development of IEEE 802.11ac standard, several new physical layer (PHY) and medium access control layer (MAC) features are taken into consideration, such as employing wider bandwidth in PHY and incrementing the limits of frame aggregation in MAC. However, due to the newly introduced features, some traditional techniques used in previous standards could face some problems. This paper presents a performance analysis of 802.11ac Distributed Coordination Function (DCF) in presence of hidden nodes in overlapping BSS (OBSS) environment. The effectiveness of DCF in IEEE 802.11ac WLAN when using different primary channels and different frequency bandwidth has also been discussed. Our results indicate that the traditional RTS/CTS handshake mechanism faces shortcomings and needs to be modified in order to support the newly defined 802.11ac amendment.

Through exploiting temporal, spectral, time-frequency representations, and spatial properties of mismatch negativity (MMN) simultaneously, this study extracts a multi-domain feature of MMN mainly using non-negative tensor factorization. In our experiment, the peak amplitude of MMN between children with reading disability and children with attention deficit was not significantly different, whereas the new feature of MMN significantly discriminated the two groups of children. This is because the feature was derived from multi-domain information with significant reduction of the heterogeneous effect of datasets.

In mobile networks emergence of failures is caused by various breakdowns of hardware and software elements. One of the serious failures in radio networks is a Sleeping Cell. In our work one of the possible root causes for appearance of this network failure is simulated in a dynamic network simulator. The main aim of the research is to detect the presence of a Sleeping Cell in the network and to define its location. For this purpose Diffusion Maps data mining technique is employed. The developed fault identification framework is using the performance characteristics of the network, collected during its regular operation, and for that reason it can be implemented in real Long Term Evolution (LTE) networks within the Self-Organizing Networks (SON) concept.

Nonnegative Matrix/Tensor factorization (NMF/NTF) have been used in the study of EEG, and the fit (explained variation) is often used to evaluate the performance of a nonnegative decomposition algorithm. However, this parameter only reveals the information derived from the mathematical model and just exhibits the reliability of the algorithms, and the property of EEG can not be reflected. If fits of two algorithms are identical, it is necessary to examine whether the desired components extracted by them are identical too. In order to verify this doubt, we performed NMF and NTF on the same dataset of an auditory event-related potentials (ERPs), and found that the identical fits of NMF and NTF under the hierarchical alternating least squares algorithms corresponded to different desired ERPs extracted by NMF and NTF, moreover, NTF contributed the ERP with much better timing and spectral properties. Such analysis implies that to combine the fit and property of the desired ERP component together helps evaluate the performance of NMF and NTF algorithms in the study of ERPs.

Time-frequency analysis is critical to study event-related potentials (ERPs) now. ERPs are usually generated through averaging over a number of trials, and such averaging limits the application of a nonlinear time-frequency analysis method-Hilbert-Huang transformation (HHT). This is because HHT usually requires very long recordings to sufficiently decompose the complicated signal into oscillations and the averaged ERP trace tends to possess only hundreds of samples. Thus, this study designs the concatenated trial based HHT to release the limitation on the decomposition. Such a paradigm may reveal better temporal and spectral properties of an ERP than the conventional wavelet transformation does. Moreover, under the proposed method, it is found that the children with attention deficit hyperactivity disorders may have more temporally, spectrally and spatially distributed brain activities than the control children do.

This paper evaluates the performance of highly loaded High Speed Uplink Packet Access (HSUPA) network with and without network wide static Interference Coordination (IC). IC alternates the priorities for user transmission periods throughout the network to achieve reduced interference levels and higher performance. A large variety of combinations including, e.g., different schedulers, cell center/edge user definitions (user splits) and interference targets are investigated in this paper. Performance is analyzed using a quasi-static system level simulator which is also used to support Third Generation Partnership Project (3GPP) standardization work. The simulator contains detailed and commonly accepted models, for instance, for channel profiles, fading and propagation. The study indicated that IC can bring gain in certain scenarios. However, the gain comes at the expense of fairness leading to users that already have high throughput benefiting from the situation. Moreover, it was shown that the major part of the gain comes from splitting users to different groups and scheduling these groups without any network wide interference coordination.

Independent component analysis (ICA) does not follow the superposition rule. This motivates us to study a negative event-related potential - mismatch negativity (MMN) estimated by the single-trial based ICA (sICA) and averaged trace based ICA (aICA), respectively. To sICA, an optimal digital filter (ODF) was used to remove low-frequency noise. As a result, this study demonstrates that the performance of the sICA+ODF and aICA could be different. Moreover, MMN under sICA+ODF fits better with the theoretical expectation, i.e., larger deviant elicits larger MMN peak amplitude.

In this presentation two event-related potentials, mismatch negativity (MMN) and P3a, are extracted from EEG by non-negative matrix factorization (NMF) simultaneously. Typically MMN recordings show a mixture of MMN, P3a, and responses to repeated standard stimuli. NMF may release the source independence assumption and data length limitations required by fast independent component analysis (FastICA). Thus, in theory NMF could reach better separation of the responses. In the current experiment MMN was elicited by auditory duration deviations in 102 children. NMF was performed on the time-frequency representation of the raw data to estimate sources. Support to absence ratio (SAR) of the MMN component was utilized to evaluate the performance of NMF and FastICA. To the raw data, FastICA-MMN component, and NMF-MMN component, SARs were 31, 34 and 49 dB respectively. NMF outperformed FastICA by 15 dB. This study also demonstrates that children with reading disability have larger P3a than control children under NMF.

In this article, a method for separating linear subspaces of time-locked brain responses and other noise sources in multichannel electroencephalography data is proposed. The components related to time-locked and noise subspaces are distinguished by method based on different behavior they experience after traditional averaging. The actual separation of the two subspaces is performed without whitening by maximizing/minimizing the same criterion. The detailed derivation of the method is given, and the results of the method's application to simulated and real EEG datasets are studied. The possibilities of improving the results are also discussed.

In this paper, we present an enhanced VoIP scheduling for the high speed downlink packet access (HSDPA) in UMTS, which takes the age of the VoIP packet into account. The downlink capacity can be significantly improved by this way, especially for shorter uplink transmission delay. In order to quantify the achievable performance improvement, we present results obtained from extensive system-level uplink and downlink simulations. Inter alia, it is shown that using the proposed scheme can lead to an increase in the downlink cell capacity of up to 16%. By applying the proposed method, the downlink performance can be improved considerably while the uplink performance remains the same, which ensures the good end-to-end performance.

The aim of this paper is to evaluate the impact of mobility to the performance of voice over IP (VoIP) service in high speed uplink packet access (HSUPA) network. The focus in the paper is on the uplink performance of VoIP as the uplink is considered most often to limit the overall coverage. When considering user's mobility in the network, it is crucial to know how it impacts to overall performance, so that possible corrective measures can be taken. In this paper, the mobility is studied with detailed handover modeling including the effect of handover delays and active set sizes in different scenarios. The system performance is evaluated with dynamic system level tool in which mobility of users, radio resource management functionalities and the interactions between them are explicitly taken into account. The study indicated that the system performance is significantly affected by user mobility. Handover delay proved to have rather minor impact on the performance with low user velocities as performance loss remained within 10 % provided that the delay stayed within reasonable limits. Limiting the active set size was observed to reduce the VoIP capacity only slightly as long as at least two links were allowed in the active set. In addition, silence indication frames specified for VoIP caused only a slight additional performance loss in mobility scenarios.

In this paper we evaluate the performance of channel quality indicator (CQI) reporting schemes in 3GPP UTRAN long term evolution (LTE) downlink (DL). In LTE, time and frequency dependent CQI is needed for DL packet scheduling (PS) and fast link adaptation (LA). Studies have indicated that frequency domain PS (FD-PS) and LA are essential techniques in improving the LTE performance, giving e.g. both cell throughput and coverage gain of around 40 % over a distributed multiplexing scheme. However, there is a tradeoff with signaling overhead related to the CQI feedback and overall LA and PS performance, which is rather overlooked in the literature. We analyze four different CQI reporting schemes with respect to system spectral efficiency and conclude that the best-M average and threshold based CQI reporting schemes seem to be the most promising in terms of the compromise between system performance and signaling overhead.

This paper presents an evaluation of Voice over IP (VoIP) system level performance in High Speed Downlink Packet Access (HSDPA) network. The main purpose is to determine how VoIP over HSDPA performance is affected by HSDPA handover i.e. serving High Speed Downlink Shared Channel (HS-DSCH) cell change. This paper studies how sensitive the capacity of VoIP service is to handover delay and User Equipment (UE) velocity. Additionally, the reliability of handover related signaling in downlink is evaluated. The system level performance of VoIP in the mobility related scenarios is evaluated with fully dynamic system level tool in which mobility of users, radio resource management functionalities and the interactions between them are explicitly taken into account. The simulation results indicated moderate sensitivity of VoIP capacity to the UE velocity. High velocities lead to capacity loss for VoIP over HSDPA. Handover delays started to have significant effect only at UE velocity of 50 km/h or more. Reliability of handover signaling does not pose any problems even at high velocity and incurs only 1% resource overhead when transmitted over HSDPA.

The aim of this paper is to evaluate the system level performance of multimedia broadcast multicast service (MBMS) in WCDMA networks. To enhance coverage and data rates of MBMS users, two macro diversity concepts were introduced in 3GPP release 6: soft and selective combining. Prior work on the evaluation of MBMS with macro diversity schemes has been restricted to link level studies or to very limited scenarios in system level studies. This paper examines the concepts in more detail with dynamic system level tool in which e.g. mobility of users and interactions of the radio resource management functionalities are explicitly taken into account. The evaluation of combining set size and data rates with different thresholds and delays in the combining procedures are especially emphasized. Considering point-to-multipoint (p-t-m) mode of MBMS, the studies indicated clear performance improvements to be gained with macro diversity. In addition, the impact of delays in combining set update procedures was observed to be rather small.

In order to further improve the packet performance in the UMTS FDD system, uplink dedicated physical control channel (DPCCH) gating as a scheme is proposed in 3GPP under the work item "continuous connectivity for packet data users". In this paper, the uplink DPCCH gating concept is described and analyzed on both a qualitative and a quantitative level. And the system performance of different gating patterns used in the scheme was investigated by analytical prediction and system level simulations with different channel profiles. As shown, the selection of the gating pattern has a significant influence on the system performance, which is correlated with the uplink data transmission activity and the uplink power control. This contributes to a better understanding of the effects involved with uplink DPCCH gating where the biggest potential for performance optimizations can be found. In summary, the scheme of the uplink DPCCH gating is promising to improve the packet performance.

In this paper, the concept of DPCCH (dedicated physical control channel) gating for HSUPA (high speed uplink packet access) is analyzed. DPCCH gating technique has been recently under consideration within WCDMA to inactivate control channels during no data transmission periods, and hence, not to misuse capacity. In order to study the concept benefit, a concrete real-time service is selected in this paper: Voice over IP (VoIP) for mobile communications. It will be shown that VoIP would be highly beneficed by DPCCH gating inclusion in 3GPP specifications. Both analytical and simulation studies were run to confirm the gain expectations.

We propose using Independent Component Analysis (ICA) as an advanced pre-processing tool for blind suppression of interfering jammer signals in direct sequence spread spectrum communication systems utilizing antenna arrays. The role of ICA is to provide a jammer-mitigated signal to the conventional detection. If the jammer signal is weak or absent, preprocessing by ICA is not advisable. Therefore we also consider two possible switching schemes, called pre-switching and post-switching, which activate the ICA-based jammer canceller only when it is expected to improve conventional detection. ICA-RAKE pre-switching is less complex, while post-switching performs better, especially when the jammer is pulsed in nature. Simulations are given to illustrate the achieved performance gains for single- and multi-path channels.

More advanced receiver structures than the conventional single antenna Rake can be used to improve the signal-to-noise (SNR) ratios, which is especially beneficial in order to utilize the high bit rates provided by the HSDPA concept in WCDMA network. In WCDMA system, orthogonal Walsh-Hadamard sequences are used as channelization codes. In frequency-selective fading channels the orthogonality of channelization codes disappears and intra-cell multiple access interference (MAI) arises. In order to mitigate the effect of MAI, a chip-level equalization has shown to be a simple and effective solution. The effectiveness of chip equalization, however, degrades at the cell borders where the inter-cell interference dominates rather than MAI. Dual antenna is a straight-forward solution to mitigate that performance drop. In this paper we consider receivers equipped with dual antenna and linear minimum mean squared error (LMMSE) chip-level equalizers and present an analysts of the expected gains over conventional single and dual antenna rake receivers in realistic situations by using a dynamic WCDMA system-level tool

In this paper we consider the performance of a UTRA TDD system with various slot configurations. UTRA TDD supports flexible and dynamic channel allocations thus it can he configured to different service needs. Uplink and downlink transmissions have different characteristics and in this study we are concentrating on the requirements for symmetrical services in dedicated channels. Effect of number of beacon channels per radio frame and effect of different uplink and downlink partitioning to TDD system capacity are analyzed with dynamic system level simulator. Although this study is limited to circuit-switched data service these findings can be used as a starting point when building up mixed traffic scenarios with asymmetrical services.

In this paper we compare the performance of conventional nonlinear interference cancellation and blind source separation (BSS) techniques. Namely, serial interference cancellation (SIC). M-stage parallel interference cancellation (PIC) and BSS based on independent component analysis (ICA) are employed in the uplink of direct-sequence code division multiple access (DS-CDMA) system. Existing studies on BSS/ICA in DS- CDMA have mainly concerned blind interference cancellation, which have been argued by the ability of BSS to deal with unknown (interfering) signals while demodulating a specific user. The performance of BSS/ICA techniques compared to conventional single user detection and blind (linear) multiuser detectors are quite well understood. In this paper we highlight the effectiveness of BSS/ICA against conventional nonlinear interference cancellation schemes. The systems with and without additional external interference like adjacent channel interference or jamming, are considered. Numerical experiments are given to evaluate the performance in different settings. They indicate clear performance gains in comparison to SIC and PIC with just a few stages when no jamming is present. However, even with a moderate level of additional jamming, BSS/ICA can outperform both the nonlinear schemes. The results thus show how the blindness of BSS/ICA makes it a robust method in the presence of intentional/unintentional out-of-cell interference.

The use of spreading codes in a communication system provides inherent interference cancellation in the form of processing gain. This is not sufficient at times, since the quality of communications can degrade severely when the interfering signal has not been canceled effectively. Hence, additional interference mitigation schemes are necessary. In a cellular network employing direct sequence code division multiple access (DS-CDMA) system, interferences originate from various sources, including the neighboring cells. This is called adjacent cell interferences. Since this interference is independent of the source-of-interest, this paper uses this fact by applying independent component analysis (ICA) to cancel this interference. ICA is used as a filter to eliminate this interference after which any conventional detection scheme can be used. Numerical experiments indicate the performance gains achieved when using this structure. Additionally, we have simulated a soft lower bound that demonstrates the loss of performance due to this interference.

In this paper we consider blind interference mitigation in direct sequence spread spectrum communication system under intentional jamming. Belouchrani et al. (2000) present a general framework in which blind source separation (BSS) techniques are utilized to enhance conventional detection. However, only second order statistics were utilized which makes the scheme vulnerable to temporally uncorrelated information or jamming signals. In this paper we remove this disadvantage by utilizing BSS based on independent component analysis (ICA). Numerical examples are given to evaluate achieved performance gains.

This paper discusses downlink power control of dedicated channels in UTRA TDD. The current UTRA TDD downlink power control is similar to the one in UTRA FDD mode, that comprises of closed inner loop and quality based outer loop. However, due to the time division feature and associated flexibility with asymmetry of TDD, the inner loop can not operate as fast as in FDD and it is affected by rapid changes in environment. Therefore, the effect of downlink power control of dedicated channel inner loop algorithm to the performance of UTRA TDD network is studied in this paper. A dynamic system level simulator is used for this purpose.

We consider differentially non-coherent code tinting acquisition in direct-sequence code-division multiple access (DS-CDMA) communication system. An advanced method for differentially non-coherent acquisition, DC-MUSIC, was proposed and noticed to combine the benefits of both conventional differentially non-coherent matched filter and traditional MUSIC estimator in a meaningful way. We evaluate DC-MUSIC in the presence of tone jamming and data modulation. Especially, we study the effects of imperfect removal of data modulation. This is crucial for any differentially coherent/non-coherent acquisition schemes, since they exploit temporal correlation of signals. Simulation results are presented.

We consider pilot cancellation in a WCDMA network. Motivations for the cancellation of disturbing pilot signals can be argued both from link and system level points of view, since it enables both mitigation of additional multiple access interference and more interference-free handover processes. In addition, the transmission power in the network can be reduced. We present a study of the effects of pilot cancellation from the system performance point of view and draw conclusions about the achieved capacity gains.

In this paper we consider code timing acquisition in the DS-CDMA communication system. Recently, traditional MUSIC utilizing differential correlations, DC-MUSIC, was proposed. Here DC-MUSIC is applied in different jamming scenarios and several channel environments. Simulation results indicate a clear improvement in acquisition performance compared to traditional MUSIC, conventional and differentially coherent acquisition schemes. In addition, DC-MUSIC is found to be a robust scheme due to the possibility of coherent integration also in the case of jamming.

In this paper handover in UTRA TDD is discussed. A simple WCDMA handover, tailored to a TDD operation, is considered. This gives rise to careful setting of different handover parameters, and the evaluation of the effects on the system performance is worth an extensive study. A dynamic system level simulator, where all the relevant radio resource management procedures are modeled, is used for this purpose.

This paper discusses uplink power control in UTRA TDD, that is, the TDD (time division duplex) mode of the third generation mobile radio system UTRA (UMTS Terrestrial Radio Access), and presents system simulations made for it. The current UTRA TDD uplink power control contains several measurements that involve A considerably amount of error. Also THE power set by power control may involve error. In this paper the effect of power control error on the performance of the UTRA TDD network is studied by the means of a system level simulator.

We propose simple and efficient algorithms for the code timing acquisition in the direct-sequence code-division multiple-access communication system. The essential assumption is that a preamble or an unmodulated pilot channel is available for the desired user. Then the correlation matrix R(/spl tau/) of the sampled data, where /spl tau/ is suitably chosen time lag, contains the timing information only of desired user, while the contributions of uncorrelated interferers and noise are suppressed out. Hence, compared to the conventional approach, more interference suppression is achieved. Coarse delay estimates are then obtained by a matched filter (MF) or multiple signal classification-type approaches. In the latter case, only L eigenvectors are computed, where L is the number of resolvable paths. If only one path exists, an additional procedure is proposed to both approaches, by which the estimation accuracy is greatly improved with negligible increase in computation. More precisely, the chip timing offset due to chip-asynchronous sampling can be determined by solving a system of two second-order polynomials for each chip interval. Therefore, only at most 2C hypotheses are needed, where C is the processing gain. All the proposed methods are computationally quite simple, containing mainly MF-operations, or at most computation of only few eigenvectors. Mean acquisition time analysis is carried out semi-analytically. Numerical experiments speaks for the possibility of achieving significant performance gains compared to conventional acquisition, especially in the presence of strong multiple-access interference, making them attractive options to be attached for the next generation mobile receivers.

In this paper, we propose a method for estimating the fading channel coefficients of the desired user in a code-division multiple access (CDMA) system. The essential assumption made is that the transmission channel is Gaussian. An independent component analysis approach based on complexity minimization is used for separating the signal of interest. Both the time structure of the fading channel and the independence of different transmission paths are taken into account. Simulations with downlink data, propagated through a Rayleigh fading channel, shows noticeable performance gains compared to blind MMSE channel estimation.

In this paper, we propose a method for timing acquisition in a code division multiple access (CDMA) system. The assumptions made are that the data signals corresponding to different users are independent, and that a minor pilot sequence corresponding to the desired user is available. An independent component analysis (ICA) approach based on a fast fixed-point algorithm is used for simultaneous demodulation of the symbol sequence and finding of synchronisation information of the user of interest; no additional pilot signal is used for the timing acquisition. Our method shows better performance than conventional CMOE receiver and matched filter detector.

We study a multipath fading CDMA channel and introduce a fuzzy tracking method to estimate the complex channel coefficients in the downlink. The receiver is the conventional RAKE. The results given by the traditional alpha tracker, differential demodulation and known coefficients are compared to each other. In the presence of noisy conditions and many disturbing strong users, the fuzzy method seems promising.

We consider blind interference suppression in direct-sequence code division multiple access (DS-CDMA) communication system, and the means of a statistical technique called independent component analysis (ICA) are taken as a solution for the problem. ICA for complex valued signals has received special attention lately due to its applicability to many practical problems. We consider complex ICA especially via the known FastICA algorithm. A drawback of many ICA algorithms, including FastICA, in CDMA is that all the available a-prior information is not necessarily utilized. We propose a solution for this problem by considering ICA as a tuning element, which is attached to an existing receiver structure. Two types of receiver structures, RAKE-ICA and MMSE-ICA, are studied in a block fading CDMA downlink. Numerical studies indicate that the performance of the RAKE and subspace MMSE detector, respectively, can be greatly improved. This is mainly due to the fact that ICA is able to utilize also the independence of the original signals, and that ICA does not explicitly depend on the erroneous timing or channel estimation.

We propose a novel approach for code acquisition in a code-division multiple access (CDMA) communication system. The essential assumption is that the pilot signal of the desired user is available. If the codes are exactly orthogonal, the method can be derived from an optimization criterion. Using the pilot signal, the performance can be greatly improved without increasing the computational complexity. Simulations show that our method clearly outperforms the minimum variance method, eigenvector-based MUSIC, and matched filter.

CDMA (code division multiple access) is a multiple access technique based on spread spectrum methods. In a downlink signal processing, only the code of the mobile phone user is known, while the codes of the interfering users are unknown. Blind source separation or multiuser detection is an approach offering the solution to this problem. In this work, we compare novel and efficient learning blind source separation algorithms for estimating the symbols in AWGN channel. Simulations show that one can estimate the symbols without any knowledge of the chip sequences.