Time series forecasting is an important application in various domains such as energy management, traffic planning, financial markets, meteorology, and medicine. However, real-time series data often present intricate temporal variability and sharp fluctuations, which pose significant challenges for time series forecasting. Previous models that rely on 1D time series representations usually struggle with complex temporal variations. To address the limitations of 1D time series, this study introduces the Times2D method that transforms the 1D time series into 2D space. Times2D consists of three main parts: first, a Periodic Decomposition Block (PDB) that captures temporal variations within a period and between the same periods by converting the time series into a 2D tensor in the frequency domain. Second, the First and Second Derivative Heatmaps (FSDH) capture sharp changes and turning points, respectively. Finally, an Aggregation Forecasting Block (AFB) integrates the output tensors from PDB and FSDH for accurate forecasting. This 2D transformation enables the utilization of 2D convolutional operations to effectively capture long and short characteristics of the time series. Comprehensive experimental results across large-scale data in the literature demonstrate that the proposed Times2D model achieves state-of-the-art performance in both short-term and long-term forecasting.

Deep reinforcement learning (DRL) has empowered a variety of artificial intelligence fields, including pattern recognition, robotics, recommendation systems, and gaming. Similarly, graph neural networks (GNNs) have also demonstrated their superior performance in supervised learning for graph-structured data. In recent times, the fusion of GNN with DRL for graph-structured environments has attracted a lot of attention. This article provides a comprehensive review of these hybrid works. These works can be classified into two categories: 1) algorithmic contributions, where DRL and GNN complement each other with an objective of addressing each other's shortcomings and 2) application-specific contributions that leverage a combined GNN-DRL formulation to address problems specific to different applications. This fusion effectively addresses various complex problems in engineering and life sciences. Based on the review, we further analyze the applicability and benefits of fusing these two domains, especially in terms of increasing generalizability and reducing computational complexity. Finally, the key challenges in integrating DRL and GNN, and potential future research directions are highlighted, which will be of interest to the broader machine learning community.

State estimation is an essential tool for situational awareness and control to ensure safe operation. While current state-of-the-art techniques provide superior performance over conventional approaches, they have poor scalability and require large computational times. These limitations can be overcome by utilizing deep learning models such as deep neural networks (DNNs). However, DNNs are prone to over-fitting and cannot incorporate structural information of networks. Furthermore, current model-based approaches require detailed knowledge of network parameters that may be unavailable in large systems. Therefore, new models with comparable performance are desired that either do not require network parameters or that can work using partial knowledge of these parameters. Recently, graph neural networks (GNNs) have become popular deep learning models that extend neural models to graph structures and incorporate structural information of the networks through graph structures. Therefore, this article proposes GNN-based state estimators by modeling the state estimation problem in distribution systems as node-level prediction problems on their graph representations with state measurement matrices and tensors as input features. Feature scaling and pseudo-measurement generation phases are introduced to enhance their performance. These approaches are evaluated on the IEEE 33, 37-node systems, and an unbalanced three-phase 559-node system. The proposed approaches provide comparable performance to sparsity-aware state estimators without requiring prior knowledge of the network parameters while using significantly lower computational times.

Identifying critical nodes and links in graphs is a crucial task. These nodes/links typically represent critical elements/communication links that play a key role in a system's performance. However, a majority of the methods available in the literature on the identification of critical nodes/links are based on an iterative approach that explores each node/link of a graph at a time, repeating for all nodes/links in the graph. Such methods suffer from high computational complexity and the resulting analysis is also network-specific. To overcome these challenges, this article proposes a scalable and generic graph neural network (GNN) based framework for identifying critical nodes/links in large complex networks. The proposed framework defines a GNN based model that learns the node/link criticality score on a small representative subset of nodes/links. An appropriately trained model can be employed to predict the scores of unseen nodes/links in large graphs and consequently identify the most critical ones. The scalability of the framework is demonstrated through prediction of nodes/links scores in large scale synthetic and real-world networks. The proposed approach is fairly accurate in approximating the criticality scores and offers a significant computational advantage over conventional approaches.

The power distribution network is undergoing tremendous transformation due to an increase in the penetration of renewable energy resources and electric vehicles. These changes have resulted in greater uncertainty and dynamics in the distribution grid states. Therefore, the ability to track and monitor system states has become a critical need for accurate and timely control actions. In this article, we propose two dynamic sparsity-based state estimation approaches for distribution systems: 1) locally weighted matrix completion (LW-MC), and 2) Bayesian matrix completion with Kalman filter prediction (BMC-KF). The performance of the proposed dynamic state estimation strategies is compared with the classic/static matrix completion (static-MC) approach using the IEEE 37 and IEEE 123 bus test systems. Results indicate that BMC-KF approach outperforms both LW-MC as well as static-MC even when 30% of the measurement data is available. Computational complexity associated with both approaches is quantified.

In the quest to improve efficiency, interdependence and complexity are becoming defining characteristics of modern complex networks representing engineered and natural systems. Graph theory is a widely used framework for modeling such complex networks and to evaluate their robustness to disruptions. Particularly, identification of critical nodes/links in a graph can facilitate the enhancement of graph (system) robustness and characterize crucial factors of system performance. Most existing methods of critical node identification are based on an iterative approach that explores each node/link of a graph. These methods suffer from high computational complexity and the resulting analysis is network specific. Additionally, uncertainty associated with the underlying graphical model further limits the potential value of these traditional approaches. To overcome these challenges, we propose a Bayesian graph neural network based node classification framework that is computationally efficient and systematically incorporates uncertainties. Instead of utilizing the observed graph for training the model, a MAP estimate of the graph is computed based on the observed topology and node target labels. Further, a Monte-Carlo (MC) dropout algorithm is incorporated to account for the epistemic uncertainty. The fidelity and the gain in computational complexity offered by the Bayesian framework is illustrated using simulation results.

Grid state estimation is essential for effective control and management of distribution systems. While weighted least squares has been the conventional method for state estimation, sparsity-aware methods have become popular due to their superior performance with limited data. Matrix completion and compressed sensing-based state estimation approaches exploit the underlying smoothness in the state variables. However, classic matrix completion methods do not take into account the temporal correlation of system states. Compressed sensing methods, on the other hand, require an appropriate choice of sparsifying basis that may not be easy to identify. This article proposes a block tensor completion based framework which uses an alternative approach to estimate voltage phasor, power injections, and branch currents. This approach utilizes the temporal correlation of the system states in a tensor trace-norm minimization formulation with power flow equations as constraints. Feature scaling is introduced in the problem formulation to benefit from the improved sensitivity of the tensor trace norm to the matrix columns in the scaled unfoldings of the tensor. Weighted tensor norm is utilized to exploit the structures of the different unfoldings of the state measurement tensor to improve the voltage estimation. The estimation accuracy is further improved by alternatively estimating the tensor columns and increasing the available data at each stage in the tensor completion process. The proposed methods are evaluated on the IEEE-33, 37 test systems, and a 100-node test system. The proposed methods are shown to provide significant performance gains relative to the classic matrix and tensor completion based approaches.

Ultra-low latency and high reliability communications are the two defining characteristics of tactile Internet (TI). Nevertheless, some TI applications would also require high data-rate transfer of audiovisual information to complement the haptic data. Using millimeter Wave (mmWave) communications is an attractive choice for high data-rate TI applications due to the availability of large bandwidth in the mmWave bands. Moreover, mmWave radio access is also advantageous to attain the air-interface-diversity required for high reliability in TI systems as mmWave signal propagation significantly differs to sub-6 GHz propagation. However, the use of narrow beamwidth in mmWave systems makes them susceptible to link misalignment-induced unreliability and high access latency. In this article, we analyze the tradeoffs between high gain of narrow beamwidth antennas and corresponding susceptibility to misalignment in mmWave links. To alleviate the effects of random antenna misalignment, we propose a beamwidth-adaptation scheme that significantly stabilize the link throughput performance.

Future smart power distribution system with multiple active consumers and aggregators exchanging real-time electricity pricing and electricity consumption information over a communication network is prone to cyberattack. The goal of this paper is to understand the impact of attacks on pricing/load signals on the physical grid within a transactive energy market framework. First, this paper models the interaction between real-time electricity price and total energy demand in the form of a discrete time nonlinear autonomous dynamical system. Second, equilibrium electricity price and energy demand associated with this coupled dynamical system is derived and conditions for bounded input bounded output (BIBO) stability are identified. Third, a BIBO stable algorithm to design real-time electricity pricing scheme from a techno-economic perspective is developed. Finally, the impact of various levels of false data injection (FDI) attack on price of electricity, demand, and distribution system voltage is investigated. The proposed model is analyzed using simulations on the IEEE 69-bus test system and the impact of FDI attack on both electricity price/demand and distribution grid voltage is quantified. This paper shows that impact of FDI attack on electricity prices is more severe than an attack on electricity demand.

The two-way communication of information between agents in the smart grid, while making way for better monitoring and control, comes at the cost of elevated communication traffic. Compressive sensing is a technique that exploits sparsity of power consumption data (in the Haar basis) and achieves sub-Nyquist compression. Household power consumption data, however, have varying sparseness due to, for example, multistate appliances. Compressing this data with a fixed ratio can lead to nonoptimal results (less compression or large reconstruction error). In this regard, a dynamic compression scheme that estimates a signal's sparsity and decides the amount of compression is desirable. We demonstrate that this approach, when applied with existing estimators of sparsity, has its limitations in overemphasizing one objective compared to the other. We propose a new measure derived from coefficient of variation and demonstrate that it achieves a better tradeoff between reconstruction performance and compression ratio. In addition, we employ a dynamic spatial compression scheme to account for spatial correlation between data of neighboring nodes and present a framework that incorporates dynamic temporal and spatial compression. We present the results on three publicly available datasets at different sampling rates and outline key findings of the study.

Fault detection and identification (FDI) of electrical motors is crucial to ensuring smooth operation of several industrial systems. Detection and diagnosis of faults at the incipient stage allows corrective actions to be adopted so as to curb the severity of faults. However, FDI of incipient faults has proved to be elusive to traditional methods of fault diagnosis. With recent developments in statistical machine learning, new methods are proposed that can be used for FDI. In this paper, we adopt three tools (support vector machine, convolutional, and recurrent networks) from machine learning to address the challenge of FDI of incipient faults. We perform FDI of a dc motor with the most commonly and readily measured current data. Results from experimental data reveal that the convolutional network performs the best of the three methods. A comparative study of the performance of the three methods under different types of operating conditions is provided. Sensitivity of the techniques to noise in measurements is also studied. The proposed approach serves as a reliable tool for FDI of dc motor under different types of loading conditions.

It is well known that the performance of OFDM- based Powerline Communication (PLC) systems is impacted by impulsive noise. In this work, we propose a practical blind adaptive analog nonlinear filter to efficiently detect and mitigate impulsive noise. Specially, we design an Adaptive Canonical Differential Limiter (ACDL) which is constructed from a Clipped Mean Tracking Filter (CMTF) and Quartile Tracking Filters (QTFs). The QTFs help to determine a real-time range that excludes outliers. This range is fed into the CMTF which is responsible for mitigating impulsive noise. The CMTF is a nonlinear analog filter and its nonlinearity is controlled by the aforementioned range. Proper selection of this range ensures the improvement of the desired signal quality in impulsive environment. It is important to note that the proposed ACDL behaves like a linear filter in case of no impulsive noise. In this context, the traditional matched filter construction is modified to ensure distortionless processing of the desired signal. The performance improvement of the proposed ACDL is due to the fact that unlike other nonlinear methods, the ACDL is implemented in the analog domain where the outliers are still broadband and distinguishable. Simulation results in PRIME (OFDM-based narrowband PLC system) demonstrate the superior BER performance of ACDL relative to other nonlinear approaches such as blanking and clipping in impulsive noise environments.

Fifth generation (5G) wireless technology is expected to utilize highly directive antennas at millimeter wave (mmWave) spectrum to offer higher data rates. However, given the high directivity of antennas and adverse propagation characteristics at mmWave frequencies, these signals are very susceptible to obstacles. One of the important factors that are highly impacted is interference behavior. In fact, signals received from other terminals can be easily blocked or attenuated at the receiver. In addition, higher number of terminals can transmit signals without introducing much interference and hence the traffic behavior, maintained by medium access control (MAC) layer, may change. In this paper, we provide an interference model to evaluate the interference power received at the physical layer of the receiving terminal, considering antenna directivity, effect of obstacles and MAC layer constraints that control the number of terminals transmitting simultaneously. We first develop a blockage model and then derive the Laplace transform of the interference power received at a typical receiving node. Subsequently, using the derived Laplace transform, we evaluate the network error performance using average bit-error-rate (BER). Analytical results are validated via Monte-Carlo simulations.

Daily assessment of sleep patterns with traditional sensor systems such as polysomnography (PSG) and electrocardiograms (ECG) is not feasible for certain populations such as severely disabled autistic children. Nocturnal movement/disturbance analysis is an important diagnostic tool for assessing sleep issues. The objective of this paper is to describe a motion detection algorithm to quantify restlessness during sleep via an unobtrusive contactless electromechanical film based ballistocardiogram (BCG) sensor integrated into a smart bed system. Two methods are proposed for motion artifact detection. One involves formulating Neyman-Pearson detection test based on signal variance in time domain and the second approach is based on a sequential detection algorithm. Test results demonstrate that Neyman- Pearson and sequential detection algorithms are very effective in identifying movements with 95% and 96% probability of detection and 94% and 95.2% accuracy in sleep and restlessness state identification, respectively.

Smart metering infrastructure and sensor deployment in the distribution grid is an integral part of the smart grid transition. These smart meters/sensors generate large volumes of data whose aggregation presents a significant challenge from a communication network standpoint. Additionally, the lack of reliability in aggregating this data prevents its use for real time operations such as state estimation and control. In this paper, we propose for the first time, a 2D compressive sensing based strategy to efficiently aggregate power measurements across a three phase unbalanced distribution grid and use it for voltage state estimation. By exploiting the underlying sparsity that results due to spatio temporal correlation of distributed generation and loads, we show that high fidelity estimation of voltage states is feasible even at 50% compression ratios. Using the IEEE 34 node test feeder system as an example, we also demonstrate the impact of the choice of the projection matrix and sparsity basis on reconstruction accuracy.

The potential of the millimeter wave (mmWave) band in meeting the ever growing demand for high data rate and capacity in emerging fifth-generation (5G) wireless networks is well-established. Since mmWave systems are expected to use highly directional antennas with very focused beams to overcome severe pathloss and shadowing in this band, the nature of signal propagation in mmWave wireless networks may differ from current networks. One factor that is influenced by such propagation characteristics is the interference behavior, which is also impacted by the simultaneous use of the unlicensed portion of the spectrum by multiple users. Therefore, considering the propagation characteristics in the mmWave band, we propose a spatial-spectral interference model for 5G mmWave applications, in the presence of Poisson field of blockages and interferers operating in licensed and unlicensed mmWave spectrum. Consequently, the average bit error rate of the network is calculated. Simulation is also carried out to verify the outcomes of the paper.

This paper provides a tractable framework to understand the performance of Device-to-device (D2D) underlay cellular networks. D2D communication can improve range, data rates and energy efficiency of cellular networks. However, analyzing performance of D2D techniques is non-trivial. In this paper, ergodic capacity of D2D underlay cellular networks is investigated using Poisson point process (PPP) models. Line-of-sight (LoS) components in D2D direct links are characterized via a Rician fading model. In addition, locations of D2D receiving users (DRUs) are assumed to be random with varying distribution. Specifically, we investigate two different cases of DRUs: (1) distance between a D2D user (DU) pair follows a uniform distribution and (2) a DRU is distributed uniformly in the circular area around its serving D2D transmit user (DTU). We derive, for the first time, closed-form results for ergodic capacity of D2D communications for a practical case with path loss exponent of 4. We demonstrate that our new analytical results better approximate the actual capacity relative to prior approaches.

Cooperative multiple input multiple output (Co-MIMO) strategies represent one approach to meet the growing requirements (i.e., higher throughput, enhanced coverage, low latencies, and reduced cost) of wireless communication services. In Co-MIMO networks, low-power relay nodes (RNs) are recruited by mobile users to cooperate as virtual antenna arrays. Although Co-MIMO architectures can offer significant improvement in both the performance and security of wireless networks, they are susceptible to attacks. In this paper, we propose a novel node reliability evaluation scheme to enhance the security of Co-MIMO networks. Leveraging the probe signal transmissions involved in physical-layer secret key generation schemes, two distributed node level reliability detection methods (one-shot and dynamic) are proposed to detect RNs that are noncooperative. Based on the fusion of information from the RNs, an overall reliability evaluation can be accomplished at a central server. Mobile users interested in collaboration can access this central server to determine which nodes to recruit for cooperation. Both the theoretical analysis and the simulation results are presented to illustrate the proposed node reliability evaluation schemes.

In this paper, we analyze dynamic switching networks, wherein the networks switch arbitrarily among a set of topologies. For this class of dynamic networks, we derive an epidemic threshold, considering the susceptible-infected-susceptible epidemic model. First, an epidemic probabilistic model is developed assuming independence between states of nodes. We identify the conditions under which the epidemic dies out by linearizing the underlying dynamical system and analyzing its asymptotic stability around the origin. The concept of joint spectral radius is then used to derive the epidemic threshold, which is later validated using several networks (Watts-Strogatz, Barabasi-Albert, MIT reality mining graphs, Regular, and Gilbert). A simplified version of the epidemic threshold is proposed for undirected networks. Moreover, in the case of static networks, the derived epidemic threshold is shown to match conventional analytical results. Then, analytical results for the epidemic threshold of dynamic networks are proved to be applicable to periodic networks. For dynamic regular networks, we demonstrate that the epidemic threshold is identical to the epidemic threshold for static regular networks. An upper bound for the epidemic spread probability in dynamic Gilbert networks is also derived and verified using simulation.

We consider a distributed agent based state estimation problem where each agent has a local underdetermined observation space and is interested in a subset of states. At a particular time instant, each agent predicts its state and makes intermediate correction based on its local measurements. Information about the corrected state elements are then exchanged among neighboring (i.e., agents who share at least one state element) agents. Based on the final processing of the exchanged information, an agent based optimally weighted Kalman consensus Filter is formed in the light of well-established theory of distributed Kalman filtering. The optimal weighting of consensus is derived through Lyapunov function based stability analysis of corresponding estimation error. The effect of communication is also investigated by introducing random failures in the communication link among neighboring agents. The corresponding bounds on the degree of consensus and inter-agent link failure rate are also derived for stable implementation of the agent based dynamic estimation. The proposed filter is applied to a custom built 2- agent system to conform the desired optimal limit for the degree of consensus based information exchange both under perfect and lossy communication network.

Long-term evolution-advanced (LTE-A) networks exploit low-power relay nodes, picocells and femtocells to boost throughput, enhance coverage, decrease latency, and reduce cost. End users in a relay-based LTE-A network can recruit relay nodes to cooperate as virtual antenna arrays, thereby reaping the benefits offered by multiple input single output (MIMO) techniques. Although the relay-based cooperative MIMO (coop MIMO) implementation in LTE-A networks improves performance, security issues are often overlooked. This paper introduces a physical layer security scheme for point-to-point networks, and extends this scheme to MIMO networks. Two practical relay-based coop MIMO architectures and corresponding secret key generation (SKG) schemes are presented. For both the MIMO and coop MIMO networks, the impact of proposed power allocation on SKG rate (SKGR) is quantified via the theoretical and numerical analysis. Results indicate that the proposed power allocation scheme can offer 15%-30% increase in SKGR relative to MIMO/coop MIMO networks with equal power allocation at low-power region, thereby improving network security.

Simultaneous information and energy transfer (SIET) is attracting much attention as an effective method to provide green energy supply for mobiles. However, low power level of harvested energy from RF spectrum limits application of this technique. Thanks to improvement of sensitivity and efficiency of RF energy harvesting circuit as well as dense deployment of small cell base stations, SIET becomes more practical. In this paper, we propose a unified receiver model for SIET in LTE-A small cell base station networks, formulate a feasibility problem with Poisson point process model and analyze the feasibility for a special and practical scenario. The results show that it is feasible for mobiles to charge the secondary battery with harvested energy from BSs, but it is still impractical to directly charge the primary battery or operate without any battery at all.

Several applications within a shipboard system involve the integration of physical systems (e.g., power systems) and cyber computing platforms such as command and control, communications, and sensing networks, and require real-time quality of service (QoS) guarantees. In this paper, the distribution of total (end-to-end) delay associated with fault diagnosis and reconfiguration of shipboard power system (SPS) is investigated from a cyber-physical systems (CPS) perspective. Specifically, a cross-layer end-to-end delay analysis framework is introduced for SPS reconfiguration. The proposed framework stochastically models the heterogeneity of actions of various subsystems involved in the reconfiguration tasks viz., generation of fault information by sensor nodes associated to the power system, processing of actions at control center to resolve fault locations and reconfiguration, and flow of information through communication network to perform necessary actions. The proposed framework then combines appropriately the output delay distributions from each subsystem to: 1) analytically predict the distribution of end-to-end delay in SPS reconfiguration after the occurrence of faults and 2) analyze and design real-time reconfiguration solutions for shipboard CPS, that meet total delay requirements. Simulations using various topological scenarios demonstrate that the proposed analytical framework closely predict the total delay associated with SPS reconfiguration.

This paper presents a design strategy for base station (BS) coordinated multi cell joint beamforming. Our objective is to maximize the average cell throughput while maintaining quality of service (QoS) for individual users. The QoS in our analysis relates to minimum signal to interference plus noise ratio (SINR) achieved by every user. Since, throughput maximization is a non convex problem, an optimal solution can not be obtained using polynomial order algorithms. However, it can be observed that existence of inter cell interference (ICI) terms are the cause of non convexity. With this knowledge, we propose two decentralized approaches based on primal and dual decomposition of the throughput maximization problem. As ICI terms are the shared variables between the base stations (BSs), these terms are exchanged between the neighboring BSs and kept fixed during an iteration. This fixes the non convex (ICI) terms, resulting in convex subproblems. We demonstrate the quality of our sub optimal decentralized solution by simulations and comparing it with optimal exhaustive search algorithms (Genie-aided algorithm). A comparison analysis is also presented for both primal and dual solutions.

We analyze the data throughput maximization problem over fading channels for a energy harvesting wireless sensor network. The effective use of energy harvesting wireless sensor networks requires an apt understanding of the underlying environmental processes. Energy as a constrained resource must be carefully utilized so as to enable good performance through a horizon of epochs, trading off sensing performance and energy usage. We develop an algorithm for the allocation of power across sensors and time which is based on observations of the underlying structure of the optimization problem. We provide an analysis of particular features of the problem and present a maximum sum rate algorithm for generalized energy flow constraints, which proves to be optimal given known energy availability and fading coefficients. We present simulation results to validate the theory and algorithm.

In this paper, we analyze Kalman filter (KF) based state estimation in spatially distributed cyber-physical systems. We consider a practical scenario, where sensors are arbitrarily deployed over an area to jointly sense a linear dynamical system. The sensors directly communicate their observations to a central state estimation unit over a lossy network resulting in: (1) random measurement losses, (2) partial observation updates in the Kalman filter. We analyze the stability of the state estimation process in this scenario, by establishing the conditions under which its steady state error covariance matrix is bounded. In contrast to previous pioneering work with intermittent observations Sinopoli:KFIntObs, we consider a dispersed measurement scenario and establish bounds on critical measurement loss rates of individual sensor communication links. The analysis illustrates the trade-off between state estimation accuracy and the quality of underlying communication network. Our analysis is critical to quantify the stability of control operation for safe and efficient operation of a cyber-physical system.

In this paper, we propose a novel design of concatenated Space-Time Block Codes (STBCs) using Systematic Redundant Residue Number System (RRNS) codes. The link between residues and complex signal constellations is optimized by incorporating the "Direct Mapping", "Prior probability based Distance Aware Mapping" and "Indirect Mapping" schemes. We analytically quantify the upper bounds on the codeword and bit error probabilities of Systematic RRNS-STBC and characterize the achievable coding and diversity gains assuming maximum likelihood decoding (MLD). We propose an Adaptive M-ary Demapping scheme for ML decoding, which exploits the RRNS code structure to decrease complexity and provide further performance improvement over the Distance Aware Mapping scheme. It is shown that Indirect Mapping, when used for RRNS coding schemes can offer more efficient transmission and performance than naive M-ary mapping. It is also seen that Systematic RRNS-STBC codes provide superior performance compared to Non-systematic RRNS-STBC, for the same code parameters, owing to more efficient binary to residue mapping.

Femtocells are perceived as having a strong potential to provide high data-rate services with increased coverage at low cost. In this paper, we consider a WiMAX network with both macro base stations (BS) and low-power, low-cost Femto Access Points (APs). We assume that Femto-APs share the same bandwidth as macro BS and Femto-APs are connected to the macro BS by wired backhaul such as cable or DSL. One of the important technical challenges in this framework is to mitigate the interference from macro users by avoiding their spectrum. Robust and dynamic spectrum sensing for channel occupancy by macro users is therefore necessary to schedule resources for femto users. An efficient cyclostationary-based spectrum sensing method is presented to facilitate cognitive radio (CR) aided resource allocation in femtocell networks for maximizing system throughput while minimizing inter-tier interference. Contrary to the classical energy detection (ED) approach, a cyclostationary-based spectrum sensing is more effective in dealing with imperfect knowledge of in-band noise variance in practical radio signals. Also, this method is more efficient than ED in low signal-to-noise ratio which is a very important requirement in WiMAX based macrocell-femtocell overlay network.

In two tier cellular networks with femtocells suited within a macrocell, interference limits the throughput per link. In this paper we study the effect of using sectorized antennas for femtocell BSs to decrease the interference power resulting from transmitter power of femtocell BSs and increase the minimum achievable SINR in femtocells subject to a constraint on the minimum SINR required for macrocell connections.

In this paper, we employ an analytical method to evaluate the route bit-error-rate (BER) performance of a wireless ad hoc network which exploits minimum distance routing. First, we estimate the average source-destination distance. Then, we derive an analytical formula to estimate the average number of hops in routes. Finally, we derive the average Route BER based on (1) the transmit power of nodes, (2) the network bandwidth, and (3) the angular view of minimum distance routing. We show that increasing node density leads to a reduction in average route BER until the average interference power overpowers the average received power. The analytical derivations have been validated using simulation studies.

We consider a wireless sensor network engaged in the task of distributed tracking. Here, multiple remote sensor nodes estimate a physical process (viz., a moving object) and transmit quantized estimates to a fusion center for processing. At the fusion node a BLUE (Best Linear Unbiased Estimation) approach is used to combine the sensor estimates and create a final estimate of the state. In this framework, the uncertainty of the overall estimate is derived and shown to depend on the individual sensor transmit energy and quantization levels. Since power and bandwidth are critically constrained resources in battery operated sensor nodes, we attempt to quantify the tradeoff between the lifetime of the network and the estimation quality over time. A unique feature of this work is that instead of merely allowing a greedy minimization of uncertainty in each time instance, the lifetime of the wireless sensor network is improved by incorporating a heuristic scaling on the operating capability of each node. This heuristic in turn depends on the remaining energy, equivalent to the past history of power and quantization decisions. Simulation results demonstrate the quality of the state estimate as well as the extended lifetime of the network when power and quantization levels are dynamically updated.

In sensor network-based detection/surveillance, one of the first challenges to address is the optimal deployment of sensors such that detection requirements are satisfied in a given area. Specifically, we pose the following question: Given a finite number of sensors, what is the best way to deploy these sensors in order to minimize the squared difference between achieved and required detection/miss probabilities? In this paper, we develop a novel optimal control theory based formulation of this sensor deployment problem. Exploiting similarities between the problem at hand and the linear quadratic regulator, an analytical solution is derived and tested. Unlike prior efforts that rely purely on heuristics, the proposed optimal control framework provides a theoretical basis for the resulting solution. As the complexity of the optimal control based solution is high, we develop a low-complexity approximation called Max_Deficiency algorithm. Using simulation results, we show that the proposed algorithms outperform existing methods by using 10% to 30% fewer number of sensors to satisfy detection requirements.

Abstract In this paper, we propose a trellis exploration algorithm based preprocessing strategy to lower the peak‐to‐average power ratio (PAPR) of precoded MIMO‐OFDM. We first illustrate the degradation in PAPR due to optimal linear precoding in MIMO‐OFDM systems. Then we propose two forms of multi‐layer precoding (MLP) schemes to reduce PAPR. In both schemes, the inner‐layer precoder is designed to optimize system capacity/BER performance. In the first MLP scheme (MLP‐I), a common outer‐layer polyphase precoding matrix is employed. In the second MLP scheme (MLP‐II), data stream corresponding to every transmit antenna is precoded with a different outer‐layer polyphase precoding matrix. Both outer‐layer precoders are custom designed using the trellis exploration algorithm by applying the aperiodic autocorrelation of OFDM data symbols as the metric to minimize. Simulation results indicate that both MLP schemes show superior PAPR performance over conventional MIMO‐OFDM with and without precoding. In addition, MLP better exploits frequency diversity resulting in BER performance gains in multi‐path environments. Copyright © 2010 John Wiley & Sons, Ltd.

Assuming that decision fusion is employed for detection in a distributed sensor network (DSN), what is the best sensor deployment strategy to satisfy specific detection and false alarm rate requirements? This is the fundamental question we address in this paper. Towards this end, we propose a novel treatment of the deployment problem in the context of optimal control theory. Specifically, we model the deployment problem as a linear quadratic regulator problem, with the deployment locations serving as control parameters. Within this framework, we propose a sequential sensor deployment algorithm. We compare the performance of the proposed algorithm against that of a greedy algorithm. Simulation results, show that the proposed algorithm can save up to 40% in the number of sensors used relative to the greedy algorithm.

We analyze the characteristics and performance of a wireless ad hoc network where nodes are connected via random channels and information is transported in the network in a cooperative multihop fashion using amplify and forward relay strategy. We characterize the network by studying important parameters such as: (1) SNR degradation with hop, (2) outage probability, (3) maximum permissible number of hops, and (4) maximum permissible number of simultaneous transmissions. We then devise a method for node selection and transmission of information across the network over disjoint routes between source and destination nodes by employing standard constructs from graph theory. Based on the above results, we evaluate the throughput achievable in the network and its asymptotic scaling as function of the number of nodes in the network for a given channel distribution.

It is well known that in practice, MIMO channels usually exhibit strong line-of-sight (LoS) components. However, common codebook designs in precoded MIMO rely heavily on the assumption of i.i.d Rayleigh fading. Therefore, designing practical unitary precoders that are suited for realistic channel condition (such as Rician MIMO channel) becomes an interesting topic to investigate. In this paper, we propose to use trellis exploration algorithm to accomplish this objective by adapting each entry of the precoding matrix to instantaneous channel realizations. Since trellis search is an on-line learning approach, our proposed precoding scheme is more adaptive to variations in channel propagations than off-line designed codebooks. Specifically, we use simulation results to show that in Rician MIMO channel, our proposed approach results in better performance than Grassmannian line/space packing and typical DFT codebook design in terms of ergodic capacity, under the same amount of feedback bits.

In many wireless sensor networks based detection and surveillance applications, the goal is to deploy sensors in an an area of interest such that certain false alarm and detection requirements are satisfied. Additionally, data fusion methods can be used to combine information from multiple sensors in order to enhance the ability of the network to meet the detection/false alarm requirements. In this paper, we pose the following question: Given a finite number of sensors that have the ability to cooperate via data fusion, what is the best way to deploy the sensors in order to meet the detection requirements in a mean squared sense, while maintaining a specified false alarm probability. Unlike prior efforts that rely on heuristics to address the deployment question, we present an optimal control theory based sensor deployment approach. Here, we model the system as a linear quadratic regulator with the deployment locations serving as control parameters. We quantify the effect of placing a sensor (and its ability to cooperate with other sensors) on the overall detection probability in order to develop an analytical solution. Using simulation results, we illustrate that our proposed approach is far superior in performance relative to existing methods in terms of minimum number of sensors needed to satisfy detection and false alarm requirements.

Wireless sensor networks are widely used for detection and surveillance applications. In this paper, we develop a novel sensor deployment algorithm by formulating the deployment problem as an optimal control theory problem. Specifically, the problem at hand is modeled as a linear quadratic regulator. In contrast to prior efforts, that mainly rely on heuristics, our formulation offers a complete theoretical framework for deployment. As the complexity of the optimal control based solution is high, we develop a low complexity approximation called max_deficiency algorithm. Using simulation results, we show that the proposed algorithms outperform existing methods by using 10% to 30% fewer number of sensors to satisfy detection requirements.

In this paper, we quantify and compare the energy efficiency of amplify-and-forward (AF) and decode-and- forward (DF) schemes on a basic three node cooperative network. Total energy is calculated considering circuit energy as well as transmission energy. For AF and DF schemes, we set a target symbol error probability (SEP) and evaluate the minimum transmission energy for achieving the target SEP. In this process we first derive an approximation for SEP at high SNR for DF with possible error in relays. Next, we formulate energy calculation as a convex optimization problem and present the theoretical solution. The result is used to compare the total energy consumption of AF and DF for various relay positions: relay near source and relay near destination. The results illustrate significant dependence on (1) circuit energy for short distances where DF has lower energy efficiency and, (2) transmission energy at larger distances (between source and destination) where AF has lower efficiency.

In this paper, we propose a multi-layer preceding scheme to reduce PAPR in MIMO-OFDM systems. Specifically, an outer-layer polyphase preceding matrix P is custom designed using the trellis exploration algorithm that was previously applied to the design of spreading sequences for CDMA systems. The algorithm determines the P matrix that results in the minimum aperiodic autocorrelation (and therefore minimum PAPR) among transmitted OFDM data symbols. Simulation results illustrate that the proposed multi-layer precoding scheme can not only significantly lower the PAPR in a MIMO-OFDM, but also well preserve the performance gains that are obtained by inner-layer precoding.

-In this paper, we consider an Erlangian process model for secondary user (SU) traffic in a cognitive radio network (CRN). Unlike prior efforts that focused on Poisson traffic models, the model captures bulk arrival and departure scenarios in a frequency band that is open for opportunistic use by multiple secondary users. Specifically, we develop a Kalman filter based estimation and forecasting strategy for the number of secondary users. Using simulated data, we demonstrate that the proposed approach provides a robust upper bound prediction on the number of secondary users.

In this paper, we propose an RNS arithmetic based FH pattern design approach that is well suited and easy to implement for practical OFDMA systems. The proposed RNS aided FH scheme not only guarantees orthogonality among intra-cell users but also randomizes inter-cell interference. We present detailed construction procedures and performance analysis for both independent and cluster hopping scenarios. We also demonstrate the gains due to frequency diversity and intercell interference diversity on the system bit error rate (BER) performance via simulations. The proposed scheme offers BER performance gain that is consistent across all cells unlike other FH pattern design schemes such as the Latin Squares (LS) based FH pattern design, where wide performance variations are observed across cells.

In this letter, we present a novel approach to determine limited feedback-based unitary precoder for maximizing single user (SU)-MIMO capacity. Specifically, a polyphase unitary precoding matrix is custom designed via the trellis exploration algorithm employing system capacity as the metric to maximize. Since the trellis exploration algorithm operates on a finite set of phases, information regarding the optimal precoder is fed back to the transmitter using a limited number of bits.

Consider a scenario where a subset of sensors has been selected for target tracking in an energy-constrained wireless sensor network. They communicate their quantized state measurements over noisy Rayleigh fading channels to a fusion center, where centralized estimation of target state is performed. In this context, we consider the problem of determining optimum quantization bits and transmission power per bit for all sensors such that both resource utilization and overall estimation error at the fusion center are minimized. Unlike prior efforts, we incorporate the operating state (characterized by the amount of residual battery power) of the sensors in the optimization framework. Analytical solutions for optimum power and number of quantization bits are derived and validated with numerical examples through simulations. We study the effect of channel quality, local measurement noise, and operating states of the sensors on their optimum choice for quantization bits and transmit power per bit.

In this work, we present novel methods for detecting the presence of motion artifact in photoplethysmographic (PPG) measurements based on higher order statistical information present in the data. We analyze both clean and corrupt PPG data in the time and frequency domains. In the time domain, skew and kurtosis measures of the signal are used as distinguishing metrics between clean and motion- corrupted data. In the frequency domain, the presence of random components due to motion artifact is analyzed using a frequency domain kurtosis measure. Additionally, bispectral analysis of PPG data indicates the presence of strong quadratic phase coupling (QPC) and more specifically self coupling in the case of clean PPG data. Though quadratic phase coupling is found in data corrupted by motion artifact, the self coupling feature is absent. A Neyman-Pearson (NP) detection rule is formulated for each of the measures. Additionally, treating each of the measures as observations from independent sensors, the Varshney-Chair rule is used to fuse individual decisions to form a global system decision.

In this paper we present and analyze an improved decorrelator MUD for asynchronous multi-carrier code division multiple access (MC-CDMA) systems with extended observation window. Specifically, we extend the receivers observation window from one symbol duration to two symbol durations enabling the receiver to completely harness the energy of the data symbol in the ith block in one shot. Hence, unlike previously proposed MUDs for asynchronous MC-CDMA, our approach remove the need to resynchronize to each users' symbol to detect all users' data. Furthermore, we analyze the decorrelator MUD in detail and propose several methods to improve its bit-error- rate performance resulting in gain of upto 2 dB relative to the traditional decorrelator MUD proposed in ([1]-[3]).

This paper addresses the optimal power allocation issue in a distributed sensor network assuming imperfect communication between the fusion center and the sensors. Noisy orthogonal channels are considered and optimal power allocation is achieved by maximizing statistical distance measures between the probability densities of the received signal under each of two hypotheses. While J-divergence measure has been considered in [1], its lack of closed form expression and complexity in calculation motivates the need to investigate other measures for mixture densities. We evaluate two statistical distance measures with closed form expressions and compare their performance with that of J-divergence for the system model introduced in [1]. Specifically, we consider elemental J-divergence and elemental L2 distance as candidate performance metrics. We illustrate that power allocation using the proposed measures is less complex relative to power allocation considering J-divergence as given in [1].

Prior research efforts on cognitive radio networks have mainly focused on effective sensing of primary users to determine the availability of a spectrum band for opportunistic use. However, when multiple secondary users compete for a limited amount of spectrum, quality of service (QoS) for these users could degrade due to interference. In this paper, we present an integrated modeling and forecasting framework characterizing spectrum use by secondary users in a cognitive radio network (CRN). In our proposed method, each secondary user in a CRN, first enters a learning and modeling phase where it attempts to estimate the traffic parameters of other secondary users. Following this phase, the secondary user actively participates in opportunistic spectrum use. Assuming a continuous time Markov chain model for secondary user activity, we develop a Kalman filter based approach for estimating the number of secondary users. This estimate is in turn used to predict the number of secondary users in a future time instant. Experimental results show that our proposed forecasting technique provides a good upper bound prediction for the number of secondary users.

Corruption of photopleythsmograms by motion artifacts has been a serious obstacle to the reliable use of pulse oximeters for real-time, continuous state-of-health monitoring. In this work, we propose a motion artifact reduction methodology that is effective even in the case of severe subject movement. The methodology involves an enhanced preprocessing unit consisting of a motion detection unit (MDU), period estimation unit, and a Fourier series reconstruction unit. The MDU aids in identifying clean data frames versus those corrupted with motion artifacts. The period detection unit is used to determine the fundamental frequency of a corrupt frame. The Fourier series reconstruction unit reconstructs the final preprocessed signal. The reconstruction process primarily utilizes the spectrum variability of the pulse waveform. Preprocessed data are then fed to a magnitude- based frequency domain independent component analysis (FD-ICA) unit. This helps reduce motion artifacts present at the frequency components chosen for reconstruction. Experimental results are presented to demonstrate the effectiveness of the proposed motion artifact reduction method. The efficacy of the technique is compared with time domain ICA and complex frequency domain ICA methods.

Forecasting of network traffic plays a very important role in many domains such as congestion control, adaptive applications, network management and traffic engineering. A good traffic model should have the ability to capture prominent traffic characteristics, such as long-range dependence (LRD), self-similarity and heavy-tailed distributions. In this paper, we propose a non-linear time series model, Generalized AutoRegressive Conditional Heteroskedasticity (GARCH), with innovation process generalized to the class of heavy-tailed distributions. Our model is fitted on real data and our results confirms the goodness of fit of our model. We then evaluate a forecasting scheme based on our model. Comparative study with other generic models shows that our model have a better prediction accuracy. In addition, the parameter estimation is less complex than the other models used so far in modeling Internet traffic data.

In this article we propose a novel Dynamic Spectrum Access (DSA) paradigm to combine the benefits of UWB transmission and cognitive radio technology to optimize the efficiency of wireless spectrum usage and maximize the channel capacity. Specifically, this work presents a multi-carrier platform not only transmits over unused spectrum "holes", but transmits over underused spectrum blocks as well, while minimizing interference to existing primary narrowband transmissions. It is also shown that current cognitive radio and UWB transmission are two special cases of the proposed general paradigm. As a direct result, this paradigm offers a high degree of flexibility of spectrum use for next generation wireless communication systems.

We present two new low-complexity Bayesian-fusion linear receiver structures for synchronous non-orthogonal DS-CDMA wireless sensor networks. The first receiver treats the underlying hypothesis as a system parameter and estimates it to arrive at a global decision. The second receiver is similar to the MMSE multiuser detector, except that it accounts for the covariance between the sensors' transmitted bits in the system model. Both receivers correctly exploit the covariance among the local decisions to achieve improved performance. We evaluate the receivers' performance in the presence of local-sensor and channel errors due to multiple-access interference (MAI), Rayleigh fading, and noise and show that their performance exceeds that of all of the suboptimal receivers proposed in S.K. Jayaweera (Jan 2005).

This paper analyzes optimized short block length low-density parity-check (LDPC) codes in spread OFDM systems that operate in frequency correlated Rayleigh fading channels. Extended irregular repeat-accumulate (eIRA) codes and codes constructed with progressive edge-growth (PEG) algorithm are optimized using density evolution and differential evolution techniques and any error floors are improved using girth conditioning. It is shown here that error floors in LDPC coded OFDM is lowered by introducing spreading on top of channel coding. It is also demonstrated that by using the distribution of the number of faded bits per OFDM symbol, the similarity in performance between codes in uncorrelated and correlated Rayleigh fading channels can be determined a priori.

This paper presents a novel deterministic technique for the generation of DS-CDMA signature sequences with a wide range of correlation properties that is based on a trellis structure and a trellis-exploration algorithm. A low-complexity implementation of the algorithm is then derived by decomposing the calculation of the metric used to generate the codeset. This approach provides a fast method of generating binary or polyphase codes with no inherent constraints on code length or size of the codeset. In addition, the algorithm is iterative and provides increasingly better codes in terms of average mean-square aperiodic correlation properties as the number of iterations increases. The algorithm is shown to outperform existing complex-code design techniques and provides codes with similar correlation properties to well-known codesets while using significantly fewer unique phases

In this paper, we propose a Parallel Genetic Algo- rithm (PGA) for optimizing the performance of a parallel decen- tralized sensor network. The metric used for the optimization is the probability of global detection error. The Parallel Genetic Algorithm simultaneously optimizes both the fusion rule and the local decision rules. We show that our approach provides results comparable to those obtained by using a GA and gradient- based algorithm from previous work by Aldosari and Moura, with reduced complexity. We consider both the cases of identical (homogeneous) and non-identical (heterogeneous) sensors and demonstrate that our algorithm converges to the same optimal solution in both cases. We also discuss the effect of the quality of the initial solution on the convergence of the PGA.

In this paper, we propose asynchronous non-orthogonal communication between distributed sensors and a data fusion center via asynchronous direct-sequence code-division multiple access (DS-CDMA). Furthermore, we evaluate the performance of such a system in the presence of local-sensor and channel errors due to multiple-access interference (MAI) and noise. We derive the optimal Bayesian receiver for this system operating over both a simple AWGN channel and a Rayleigh-fading channel, assuming full knowledge of the channel fading coefficients. The optimal receiver is shown to have exponential complexity as a function of the number of sensors. A set of low-complexity partitioned receivers are then proposed and analyzed in relation to the optimal one. These partitioned receivers are based on well-known linear and nonlinear multiuser detectors for DS-CDMA. The synchronous DS-CDMA case was studied previously, and it is shown here that the degradation induced by moving to an asynchronous model is relatively small for the optimal receiver. Moreover, we provide compelling evidence that the quality of the local-sensor decisions is the limiting factor in fusion performance for fading channels with high signal-to-noise ratio.

In this paper, we analyze the impact of the local decision rules on the performance of a distributed sensor network. We do this by fixing the fusion rule to be one of the three common binary decision rules: majority voting, AND and OR rules, and optimizing the local decision rules with respect to the probability of error at the fusion center. We assume that the sensor observations are correlated and we consider the general case of heterogeneous (non-identical) sensors. We show that systems having different fusion rules can all provide similar performance if the local decision rules are chosen appropriately

This paper explores the application of the ant colony algorithm for an NP-complete problem in the area of wireless communications. The specific problem is one of detecting users (binary vectors) in a multi-user environment in synchronous MC-CDMA (multi-carrier Code Division Multiple Access) systems, such that the total interference noise in minimized. This approach is particularly attractive as ACO is well suited for physically realizable, real-time use, where fast convergence is absolutely necessary. Results suggest that the algorithm reduces the computer time requirement by as much as 98% over an exhaustive search method.

This paper proposes a new algorithm based on the clonal selection principle for the design of spreading codes for DS-CDMA. The algorithm follows a multi-objective approach, generating complex spreading codes with good autocorrelation as well as good cross-correlation properties. It also enables spreading code design with no restrictions on the number of users or code length. The algorithm maintains a repertoire of codes that are subject to cloning and undergo a process of affinity maturation to obtain better codes. Results indicate that the produces code sets that lie very close to the theoretical Pareto front.

This paper presents a novel deterministic technique for the generation of DS-CDMA signature sequences with a wide range of correlation properties that is based on the well-known Viterbi search algorithm. This approach provides a fast, low-complexity method of generating binary or polyphase codes with no constraints on code length or size of the codeset. In addition, the algorithm is iterative and provides increasingly better codes in terms of average mean-square aperiodic correlation properties as the number of iterations increase. The algorithm is shown to outperform existing complex-code design techniques and provides codes with similar correlation properties to well-known codesets while using significantly fewer unique phases.

This paper analyzes the performance of carrier interferometry/orthogonal frequency division multiplexing (CI/OFDM) in the presence of multipath fading as well as jamming. Specifically, the effect of jamming power and jammer bandwidth on the performance of CI/OFDM and OFDM is investigated. While CI/OFDM significantly outperforms traditional OFDM in a fading environment, we demonstrate that OFDM is more resistant to single-tone jamming than CI/OFDM. Furthermore, OFDM is more robust when jamming power increases although CI/OFDM is more robust when the bandwidth of the jammer increases. We also show that by employing frequency-domain anti-jamming techniques, it is possible to restore the performance gains offered by CI/OFDM relative to traditional OFDM.

In this paper, we present a novel multiuser detection (MUD) technique based on ant colony optimization (ACO), for synchronous multi-carrier code division multiple access (MC-CDMA) systems. ACO algorithms are based on the cooperative foraging strategy of real ants. While an optimal MUD design using an exhaustive search method is prohibitively complex, we show that the ACO based MUD converges to the optimal MUD BER performance in relatively few iterations providing 98% saving in computational complexity.

In this paper, we present a novel multiuser detection (MUD) technique based on ant colony optimisation (ACO), for synchronous direct sequence code division multiple access systems. ACO algorithms are based on the cooperative foraging strategy of real ants. While an optimal MUD design using an exhaustive search method is prohibitively complex, we show that the ACO-based MUD converges to the optimal bit-error-rate performance in relatively few iterations providing 95% savings in computational complexity. This reduction in complexity is retained even when considering users with unequal received powers.

This paper proposes a novel evolutionary approach to spreading code design in direct sequence code division multiple access (DS-CDMA). Specifically, a multiobjective evolutionary algorithm (EA) is used to generate complex spreading sequences that are optimized with respect to the average mean-square cross- and/or autocorrelation (CC and/or AC) properties. A theoretical model is developed in order to demonstrate the optimality of the generated codes. The proposed algorithm enables spreading code design with no constraints on the code length. Furthermore, it is possible to generate K/spl ges/N codes of length N with very little cost in correlation properties. This results in significant capacity enhancement in DS-CDMA systems.

The paper proposes a novel evolutionary approach to spreading code design in DS-CDMA. Specifically, a multiobjective evolutionary algorithm is used to generate complex spreading sequences that are optimized with respect to average mean square cross correlation and/or autocorrelation properties. A theoretical model is developed in order to demonstrate the optimality of the generated codes. The proposed algorithm enables spreading code design with no constraints on the code length. Furthermore, it is possible to generate K/spl ges/N codes of length N with very little cost in correlation properties. This results in significant capacity enhancement in DS-CDMA systems.

In this work, we propose a new multicarrier platform to optimize the efficiency of wireless operators' licensed bands and to enable flexible sharing of licensed and unlicensed bands (in different spectral regions). This research supports a recent FCC proposal which suggests innovative spectrum management regulations to improve spectral efficiency. Specifically, this work presents a multicarrier platform capable of (1) achieving high spectral efficiency by application of "narrow" orthogonal carriers; and (2) enables flexible spectral sharing across different licensed and unlicensed bands via operator borrowing/ lending.

In traditional binary frequency-shift keying (BFSK), two distinct carrier frequencies are used to represent binary data. In this letter, we propose transmitting multiple orthogonal, in-phase subcarriers around two distinct carrier frequencies to represent binary information. The envelope of the resulting FSK signal is a carrier interferometry pattern, and hence, the name carrier interferometry (CI/) FSK. We demonstrate that this technique is spectrally efficient when compared to traditional BFSK. Performance and data-rate benefits are also demonstrated with the use of a novel coherent reception technique. Moreover, while it is difficult to employ traditional BFSK in multipath channels, we show how the new CI/FSK scheme exploits frequency-diversity benefits when used in such channels.

In this letter, a large set of spreading codes that doubles capacity in multicarrier code-division multiple-access (MC-CDMA) systems without any cost in bandwidth and with negligible cost in performance is introduced. This large set is comprised of: 1) complex spreading codes instead of conventional real-valued spreading codes and 2) two sets, each made up of orthogonal complex spreading codes, with minimum cross correlation between sets. Simulations performed over Rayleigh fading channels demonstrate 100% gains in terms of MC-CDMA capacity with negligible loss in performance.

This letter provides a novel method for doubling throughput in time division multiple access (TDMA) systems. Specifically, with pseudo-orthogonal positioning of carrier interferometry pulse shapes, a TDMA system is designed to support twice as many real symbols in a slot, without increasing bandwidth or slot duration. With regard to performance, the doubling of throughput comes at the cost of only 1-2 dB performance degradations at probability of error of 10/sup -2/.

In traditional BFSK, two distinct carrier frequencies are used to represent binary '1' and binary '0'. In this paper, we propose transmitting multiple, orthogonal, in-phase sub-carriers around two carrier frequencies to represent the binary information. We demonstrate that this technique is spectrally efficient when compared to traditional BFSK. Performance and data rate benefits are also demonstrated in both non-coherent and novel coherent reception techniques. Moreover, while traditional BFSK is not employed in channels that are frequency selective, we show that with coherent reception, the new FSK scheme can be used in frequency selective channels to exploit frequency diversity benefits.

This paper proposes a novel scheme which doubles capacity in MC-CDMA systems without any cost in bandwidth and with negligible cost in performance. Specifically: (1) complex spreading codes are used instead of conventional real value spreading codes; and (2) two groups of orthogonal complex spreading codes are used simultaneously, where minimum cross-correlation exists between the two groups. Simulations performed over Rayleigh fading channels using these novel codes demonstrate that the proposed scheme enables gain of 100% in terms of MC-CDMA capacity with negligible performance losses.

Abstract Multi‐carrier technologies in general, and OFDM and MC‐CDMA in particular, are quickly becoming an integral part of the wireless landscape. In this first of a two‐part survey, the authors present the innovative transmit/receive multi‐carrier implementation of TDMA and DS‐CDMA systems. Specifically, at the transmit side, the pulse shape (in TDMA) and the chip shape (in DS‐CDMA) corresponds to a linear combining of in‐phase harmonics (called a CI signal). At the receiver side, traditional time‐domain processing (equalization in TDMA and RAKE reception in DS‐CDMA) is replaced by innovative frequency based processing. Here, receivers decompose pulse (or chip) shapes into carrier subcomponents and recombine these in a manner achieving both high frequency diversity gain and low MAI. The resulting system outperforms traditional TDMA and DS‐CDMA systems by 10–14 dB at typical BERs, and, by application of pseudo‐orthogonal pulse shapes (chip shapes), is able to double system throughput while maintaining performance gains of up to 8 dB. Copyright © 2002 John Wiley & Sons, Ltd.

Abstract Multi‐carrier technologies in general, and OFDM and MC‐CDMA in particular, are an integral part of the wireless landscape. In this second part of a two‐part survey, the authors present an innovative set of spreading codes known as CI codes, and demonstrate how these significantly increase performance and capacity in OFDM and MC‐CDMA systems, all the while eliminating PAPR concerns. Regarding OFDM: the spreading of each symbol over all N carriers using CI spreading codes (replacing the current one symbol per carrier strategy) are presented. CI codes are ideally suited for spreading OFDM since, when compared to traditional OFDM, CI‐based OFDM systems achieve the performance of coded OFDM (COFDM) while maintaining the throughput of uncoded OFDM, and, at the same time, eliminate PAPR concerns. When applied to MC‐CDMA, CI codes provide a simple means of supporting 2N users on N carriers while maintaining the performance of an N‐user Hadamard Walsh code MC‐CDMA system, i.e., CI codes double MC‐CDMA network capacity without loss in performance. The CI codes used in OFDM and MC‐CDMA systems are directly related to the CI pulse (chip) shapes used to enhance TDMA and DS‐CDMA (see part 1): hence, the CI approach provides a common hardware platform for today's multi‐carrier/multiple‐access technologies, enabling software radio applications. Copyright © 2002 John Wiley & Sons, Ltd.