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The interaction between technology and healthcare has a long history. However, recent years have witnessed the rapid growth and adoption of the Internet of Things (IoT) paradigm, the advent of miniature wearable biosensors, and research advances in big data techniques for effective manipulation of large, multiscale, multimodal, distributed, and heterogeneous data sets. These advances have generated new opportunities for personalized precision eHealth and mHealth services. IoT heralds a paradigm shift in the healthcare horizon by providing many advantages, including availability and accessibility, ability to personalize and tailor content, and cost-effective delivery. Although IoT eHealth has vastly expanded the possibilities to fulfill a number of existing healthcare needs, many challenges must still be addressed in order to develop consistent, suitable, safe, flexible, and power-efficient systems that are suitable fit for medical needs. To enable this transformation, it is necessary for a large number of significant technological advancements in the hardware and software communities to come together. This keynote paper addresses all these important aspects of novel IoT technologies for smart healthcare-wearable sensors, body area sensors, advanced pervasive healthcare systems, and big data analytics. It identifies new perspectives and highlights compelling research issues and challenges, such as scalability, interoperability, device-network-human interfaces, and security, with various case studies. In addition, with the help of examples, we show how knowledge from CAD areas, such as large scale analysis and optimization techniques can be applied to the important problems of eHealth. [ABSTRACT FROM AUTHOR]

In this paper, we investigate a very challenging task of automatically generating presentation slides for academic papers. The generated presentation slides can be used as drafts to help the presenters prepare their formal slides in a quicker way. A novel system called PPSGen is proposed to address this task. It first employs the regression method to learn the importance scores of the sentences in an academic paper, and then exploits the integer linear programming (ILP) method to generate well-structured slides by selecting and aligning key phrases and sentences. Evaluation results on a test set of 200 pairs of papers and slides collected on the web demonstrate that our proposed PPSGen system can generate slides with better quality. A user study is also illustrated to show that PPSGen has a few evident advantages over baseline methods. [ABSTRACT FROM PUBLISHER]

This paper studies transmission strategies for the downlink of a cloud radio access network, in which the base stations are connected to a centralized cloud computing-based processor with digital fronthaul or backhaul links. We provide a system-level performance comparison of two fundamentally different strategies, namely, the data-sharing strategy and the compression strategy, which differ in the way the fronthaul/backhaul is utilized. It is observed that the performance of both strategies depends crucially on the available fronthaul or backhaul capacity. When the fronthaul/backhaul capacity is low, the data-sharing strategy performs better, while under moderate-to-high fronthaul/backhaul capacity, the compression strategy is superior. Using insights from such a comparison, we propose a novel hybrid strategy, combining the data-sharing and compression strategies, which allows for better control over the fronthaul/backhaul capacity utilization. An optimization framework for the hybrid strategy is proposed. Numerical evidence demonstrates the performance gain of the hybrid strategy. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new method for solving multiobjective optimization problems with a large number of decision variables. The proposed method called weighted optimization framework is intended to serve as a generic method that can be used with any population-based metaheuristic algorithm. After explaining some general issues of large-scale optimization, we introduce a problem transformation scheme that is used to reduce the dimensionality of the search space and search for improved solutions in the reduced subspace. This involves so-called weights that are applied to alter the decision variables and are also subject to optimization. Our method relies on grouping mechanisms and employs a population-based algorithm as an optimizer for both original variables and weight variables. Different grouping mechanisms and transformation functions within the framework are explained and their advantages and disadvantages are examined. Our experiments use test problems with 2–3 objectives 40–5000 variables. Using our approach on three well-known algorithms and comparing its performance with other large-scale optimizers, we show that our method can significantly outperform most existing methods in terms of solution quality as well as convergence rate on almost all tested problems for many-variable instances. [ABSTRACT FROM PUBLISHER]

Few of the challenges with development of a single on–board motor for direct–drive electric vehicles include high torque density and low torque ripple. Therefore, in this paper, a 36–slot, 34–pole consequent pole six–phase permanent magnet synchronous machine (PMSM) has been optimized to address the aforementioned challenges for direct–drive application. Existing literature on optimization processes that rely solely on finite element models are restricted to three–phase machines only and also take longer computation time. Therefore, this paper proposes a novel optimization approach based on supervised machine learning for six–phase PMSM. In this approach, a non–conventional extended dual dq–frame model that accounts for higher order space harmonics in inductances and flux linkages has been developed and used for accurate computation of average torque and torque ripple of six–phase PMSM. Using the performance characteristics obtained from the extended dual dq–frame model for a set of initial design candidates, support vector regression algorithm is employed for supervised machine learning and increasing solutions in the design space. Furthermore, pareto front is used for selecting optimal machine models with maximum torque density and reduced torque ripple. Multi–objective trade–offs and comparison of initial and optimized designs based on average torque, torque ripple, efficiency and cost are performed. [ABSTRACT FROM AUTHOR]

The age of information (AoI) is a key performance metric for data freshness in real-time systems, measuring the time elapsed between status updates received at a remote destination. This paper addresses the analysis of both the AoI and the delay for a system demanding timely status updates, consisting of an internet of things (IoT) community, and a multi-core edge computing node (EN). The IoT devices perform channel contention according to a slotted aloha non-orthogonal multiple access scheme with two transmission power levels to access the EN. The objective of the paper is the dynamic selection of both the EN computation capabilities and the suitable access probability for the two power levels, aiming at maximizing the time-average system utility, while guaranteeing the queues stability. The device end-to-end delay is analyzed by resorting to the martingale theoretical approximation under the condition that the Lyapunov optimization framework is performed on a time slot basis, hence leading to a novel instantaneous mixed integer problem formulation. Furthermore, the generalized benders decomposition method is employed to provide a suboptimal solution for the formulated problem. Finally, the accuracy of the proposed framework is validated by comparing the obtained analytical predictions with simulation results. [ABSTRACT FROM AUTHOR]

This paper studies instance-dependent Positive and Unlabeled (PU) classification, where whether a positive example will be labeled (indicated by $s$ s ) is not only related to the class label $y$ y , but also depends on the observation $\mathbf {x}$ x . Therefore, the labeling probability on positive examples is not uniform as previous works assumed, but is biased to some simple or critical data points. To depict the above dependency relationship, a graphical model is built in this paper which further leads to a maximization problem on the induced likelihood function regarding $P(s,y|\mathbf {x})$ P (s , y | x) . By utilizing the well-known EM and Adam optimization techniques, the labeling probability of any positive example $P(s=1|y=1,\mathbf {x})$ P (s = 1 | y = 1 , x) as well as the classifier induced by $P(y|\mathbf {x})$ P (y | x) can be acquired. Theoretically, we prove that the critical solution always exists, and is locally unique for linear model if some sufficient conditions are met. Moreover, we upper bound the generalization error for both linear logistic and non-linear network instantiations of our algorithm, with the convergence rate of expected risk to empirical risk as $\mathcal {O}(1/\sqrt{k}+1/\sqrt{n-k}+1/\sqrt{n})$ O (1 / k + 1 / n - k + 1 / n) ($k$ k and $n$ n are the sizes of positive set and the entire training set, respectively). Empirically, we compare our method with state-of-the-art instance-independent and instance-dependent PU algorithms on a wide range of synthetic, benchmark and real-world datasets, and the experimental results firmly demonstrate the advantage of the proposed method over the existing PU approaches. [ABSTRACT FROM AUTHOR]

Compositional Optimization (CompOpt) was recently proposed for optimization of discrete-event systems of systems. A modular optimization model allows CompOpt to divide the optimization into separate sub-problems, mitigating the state space explosion problem. This paper presents the Modular Optimization Learner (MOL), a method that interacts with a simulation of a system to automatically learn these modular optimization models. MOL uses a modular learning that takes as input a hypothesis structure of the system and uses the provided structural information to split the acquired learning into a set of modules, and to prune parts of the search space. Experiments show that modular learning reduces the state space by many orders of magnitude compared to a monolithic learning, which enables learning of much larger systems. Furthermore, an integrated greedy search heuristic allows MOL to remove many sub-optimal paths in the individual modules, speeding up the subsequent optimization. Note to Practitioners—Automation systems are becoming increasingly large and complex and the automation of more and more advanced tasks often requires the coordination of multiple subsystems. Optimization can have a great impact on the efficiency of these systems in terms of cost and operation speed. Finding optimal solutions is, however, a difficult task. As the number of tasks and subsystems of the automation system increases, the search space of the optimization problems tends to grow exponentially. Compositional Optimization (CompOpt) is a method specifically designed for the optimization of large-scale automation systems. A challenge with the application of CompOpt is that it takes as input a specific type of optimization model that divides the system into subsystems; like machines, vehicles, etc. Formulating these models requires a high level of expertise and system knowledge. This paper addresses this challenge with an algorithm that learns these models from a simulation of the system. The simulation can be implemented in any software as long as a suitable interface exists or can be constructed. To divide the learning into subsystems, the algorithm uses an initial plant structure hypothesis (PSH). This can be viewed as a meta-model that includes known structural information, such as the number of subsystems and which actions that affect each subsystem. The more structural information that is added to PSH, the more efficient the learning and subsequent optimization will be. The purpose of this is to reduce the level of expertise and system knowledge needed in the application of optimization, to simplify the transition to Industry 4.0. [ABSTRACT FROM AUTHOR]

Economic dispatch with valve-point effect (EDVPE) considered is presented as a more accurate model of the real problem compared to the conventional economic dispatch model. It is basically a non-convex, non-differentiable, and multi-modal optimization model with many local minima. Part I of the paper focuses on the local minimum analysis of the EDVPE. The analysis indicates that a local minimum consists of the singular points, the small convex regions, and the output of a slack unit that is dispatched to balance the load demand. Two types of local minima are identified and the second type could be ignored. To verify the rationality of the analyses, a traverse search has been performed to solve the EDVPE with and without considering the transmission loss on different test systems. All the simulation results support the analysis given in the paper. To effectively solve the EDVPE on a large-scale power system, based on the analysis presented in this paper, a new method, dimensional steepest decline method, is proposed in Part II of the paper. [ABSTRACT FROM PUBLISHER]

While much attention was given in the literature to improve the fault ride through (FRT) capability of a doubly-fed induction generator (DFIG)-based wind energy conversion system (WECS) during various faults at the grid side, not much attention was given to investigate and improve the performance of the DFIG-WECS under ferroresonance conditions. This paper investigates the impact of ferroresonance on the overall performance of a DFIG-WECS. The paper also presents a new technique to improve the high voltage ride through capability of the investigated system under such condition. The proposed technique relies on incorporating a superconducting coil within the dc-link of the DFIG grid-side and rotor-side converters. Energy exchange between the coil and the system is controlled through regulating the duty cycle of the dc-chopper interfacing the coil with the dc-link. In this regard, a simple but yet effective proportional-integral (PI) controller is employed to regulate the dc-chopper duty cycle. Parameters of the PI controller are optimized using elephant herding optimization algorithm. Simulation results verify the effectiveness of the proposed controller in improving the overall performance of a DFIG-WECS during ferroresonance events. [ABSTRACT FROM AUTHOR]

A substantial body of literature on global design optimization techniques is based on an implicit assumption that a system under optimization is designed by a perfectly centralized design team with all team members committed to a global optimum solution. This assumption enables the system engineers to minimize all the objective functions simultaneously while controlling all design decisions at the same time. Recent research suggested that this inherent assumption may need to be revisited. This paper proposes a novel framework for design optimization using game theory, which represents a design process as a normal-form game and unifies two fundamental decisions of discipline designers, namely a system-global or a discipline-local optimum solution. This paper provides a general mathematical formulation of the framework for a design process with any number of discipline designers. The framework introduces the notion of the Nash front, which is a set of design solutions consisting of the Nash equilibria derived through the reallocation of design authority between discipline designers. This paper then tests the framework on a numerical optimization case study of a Stirling micro combined heat and power plant with two discipline designers. The result indicates that for the system capital cost and the system power output the difference between global Pareto-optimum designs and the Nash front designs on average across five different scenarios of design authority allocation was 22% and 16%, correspondingly. The application of the novel framework in different fields of engineering could help to build models that are more realistic and prevent impractical technical objectives. [ABSTRACT FROM AUTHOR]

By triggering the switches in sequence and controlling the brick charging voltage independently, a pulsed power system can generate flexible current waveform in the load, which could allow high-accurate magnetic pressure in experimental materials in the study of EOS data and dynamic response of material characterization. Currently, a 1.5-MA driver with 20 independent bricks has been developed, and a programmable 256-brick parallel discharge driver which is able to generate shaped current greater than 6.5 MA is under construction in the China Academy of Engineering Physics. In this paper, for these two multibrick parallel discharge drivers, the method of optimizing the output current waveform of the driver is studied by using the genetic algorithm and the corresponding optimization results of different target current waveforms are obtained. An experimental platform is set up for studying the gas switch breakdown characteristics. The self-breakdown and triggering breakdown experiments of the switch are conducted, and its breakdown characteristics are researched. Meanwhile, the effect of breakdown delay and jitter on the output current waveform is studied by simulation. This paper is of great significance to the actual operation of the multibrick parallel discharge drivers. [ABSTRACT FROM AUTHOR]

This paper investigates the dynamic access point (AP) clustering and precoding problem in the downlink of user-centric virtual cell networks. The goal is to maximize the weighted sum spectral efficiency (SE) while satisfying the power constraints and AP clustering constraints in adjacent time slots (TSs). By adopting the random walk mobility to model the mobile user equipments’ movement behaviors, we consider dynamic and time-varying channel conditions. Therefore, the weighted sum SE maximization programming takes the form of discrete-time sequence of mixed-integer non-convex optimization problems. In this paper, we propose to solve this sequential problem in two stages. In the first stage, a dynamic AP clustering approach based on discrete particle swarm optimization is developed. This approach takes the advantage of the channel correlation by exploiting the relationship between AP clustering solutions in adjacent TSs to improve the SE performance and reduce complexity. In the second stage, given the AP clustering solution obtained in the first stage, a distributed precoding algorithm is devised via applying the weighted minimum mean square error method. By combining these two stages, we propose a novel dynamic AP clustering and precoding algorithm (DAPC-Pre). The effectiveness of the proposed DAPC-Pre algorithm is verified by the simulation results. In particular, the proposed algorithm converges fast and significantly outperforms benchmark algorithms in terms of sum SE under different dynamic environments. [ABSTRACT FROM AUTHOR]

This paper studies the composition in portrait paintings and develops an algorithm to improve the composition of portrait photographs based on example portrait paintings. A study of portrait paintings shows that the placement of the face and the figure is pose-related. Based on this observation, this paper develops an algorithm to improve the composition of a portrait photograph by learning the placement of the face and the figure from an example portrait painting. This example portrait painting is selected based on the similarity of its figure pose to that of the input photograph. This similarity measure is modeled as a graph matching problem. Finally, space cropping is performed using an optimization function to assign a similar location for each body part of the figure in the photograph with that of the figure in the example portrait painting. The experimental results demonstrate the effectiveness of the proposed method. A user study shows that the proposed pose-based composition improvement is preferred more than rule-based methods and learning-based methods. [ABSTRACT FROM AUTHOR]

Spatially-coupled (SC) codes are a family of graph-based codes that have attracted significant attention, thanks to their capacity approaching performance and low decoding latency. An SC code is constructed by partitioning an underlying block code into a number of components and coupling their copies together. In this paper, we first introduce a general approach for the enumeration of detrimental combinatorial objects in the graph of finite-length SC codes. Our approach is general in the sense that it effectively works for SC codes with various partitioning schemes, column weights, and memories. Next, we present a two-stage framework for the construction of high performance binary SC codes optimized for the additive white Gaussian noise channels; we aim at minimizing the number of detrimental combinatorial objects in the error floor region. In the first stage, we deploy a novel partitioning scheme, called the optimal overlap partitioning, to produce the optimal partitioning corresponding to the smallest number of detrimental objects. In the second stage, we apply a new circulant power optimizer to further reduce the number of detrimental objects in the lifted graph. SC codes constructed by our new framework have up to two orders of magnitude error floor performance improvement and up to 0.6 dB SNR gain compared to prior state-of-the-art SC codes. [ABSTRACT FROM AUTHOR]

This paper treats the problem of optimal resource allocation over time in a finite-horizon setting, in which the resource become available only sequentially and in incremental values, and the utility function is concave and can freely vary over time. Such resource allocation problems have direct applications in data communication networks (e.g., energy harvesting systems). This problem is studied extensively for special choices of the concave utility function (time invariant and logarithmic) in which case the optimal resource allocation policies are well-understood. This paper treats this problem in its general form and analytically characterizes the structure of the optimal resource allocation policy and devises an algorithm for computing the exact solutions analytically. An observation instrumental to devising the provided algorithm is that there exist time instances at which the available resources are exhausted, with no carryover to future. This algorithm identifies all such instances, which in turn, facilitates breaking the original problem into multiple problems with significantly reduced dimensions. Furthermore, some widely used special cases in which the algorithm takes simpler structures are characterized, and the application to the energy harvesting systems is discussed. Numerical evaluations are provided to assess the key properties of the optimal resource allocation structure and to compare the performance with the generic convex optimization algorithms. [ABSTRACT FROM AUTHOR]

In this paper, a novel dual-rotor axial-gap flux-switching permanent-magnet machine with dc excitation winding is proposed. First, the topology and operation principles of the proposed topology are clarified. Basic design equations and parameters are provided, and the electromagnetic analyses of the proposed flux-switching machine with 3-D FEA are performed. Parametric optimizations using 3-D FEA are completed for different dc excitations. A prototype machine is manufactured, tested, and validated. It is shown that the air-gap flux of the proposed axial-gap machine can be controlled in a wide range compared to conventional surface magnet machines. [ABSTRACT FROM AUTHOR]

In the last decade, machine-learning-based compilation has moved from an obscure research niche to a mainstream activity. In this paper, we describe the relationship between machine learning and compiler optimization and introduce the main concepts of features, models, training, and deployment. We then provide a comprehensive survey and provide a road map for the wide variety of different research areas. We conclude with a discussion on open issues in the area and potential research directions. This paper provides both an accessible introduction to the fast moving area of machine-learning-based compilation and a detailed bibliography of its main achievements. [ABSTRACT FROM AUTHOR]

This paper presents the implementation of Luneburg lens antenna based on a 2-D parallel-plate structure for broadband multibeam radiation at millimeter-wave frequencies. In particular, each beam pattern of this 2-D Luneburg lens antenna has a narrow beamwidth along the multibeam dimension, and exhibits a broad beamwidth along the other cross section, analogous to the radiation from a 1-D array of antennas embedded inside the parallel-plate structure. Furthermore, the isolation to external structures by the top and bottom metal parallel plates may reduce the beam distortion from external interferences, and therefore enhance its compatibility to integrate with other radio frequency (RF) components to form an RF subsystem module. In addition, a theoretic model to realize the dielectric variation of Luneburg lens is developed by using stacked multiple planar layered dielectric materials for a cheap manufacture. In this paper, numerical simulation to validate the developed model of 2-D Luneburg lens design is first presented. Afterward, simulation and experimental measurement results of a practical design are both presented to validate the design concept. [ABSTRACT FROM AUTHOR]

This paper considers the scheduling of stochastic customer orders to minimize expected cycle time. Customer orders dynamically arrive at a machine station, and each order consists of multiple product types. Random workloads are required by each product type, and the workloads are assigned to a set of unrelated parallel machines in the station to be processed. The objective is to obtain the minimal long-run expected order cycle time through proper workload assignments. In view of the difficulty in evaluating the objective function, this paper models the targeted problem as a simulation optimization problem and proposes to solve it under the multifidelity model framework. To improve the efficiency in evaluating candidate solutions, a low-fidelity model is constructed to select solutions with better performances for high-fidelity simulations. The effectiveness of this low-fidelity model is demonstrated through a series of theoretical evidences. A simulation optimization algorithm, named Bound-Multi-fidelity Optimization with Ordinal Transformation and Optimal Sampling Bound-(MO2TOS), is developed by taking advantage of the properties of the low-fidelity model. Numerical experiments are conducted to evaluate the performance of the proposed algorithm against three other well-known simulation optimization algorithms in the literature. Results indicate that the Bound-MO2TOS outperforms all the other tested algorithms, and its performance is robust against changes in problem scale. Note to Practitioners—Customer order scheduling models have wide applications in industries where every single order may contain multiple product types and the entire order requires one shipment delivery. In many applications, such as pigments dyes manufacturing, auto repair, and grocery, consideration of customer orders rather than individual jobs is often preferred. Given the stochastic nature and the synchronization constraints of the problem, a multifidelity simulation algorithm named Bound-Multi-fidelity Optimization with Ordinal Transformation and Optimal Sampling Bound-(MO2TOS) is proposed. Experimental results suggest that the proposed algorithm outperforms three other popular simulation optimization algorithms under a variety of scenarios. It is admitted that further improvement can be made in refining the computation resource distribution process of Bound-MO2TOS. In the future research, such an improvement will be more thoroughly elaborated so that practitioners can utilize it to coordinate their productions better. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surface (RIS) has emerged as a promising technique for future wireless communication networks. How to reliably transmit information in a RIS-based communication system arouses much interest. This paper proposes a reflecting modulation (RM) scheme for RIS-based communications, where both the reflecting patterns and transmit signals can carry information. Depending on that the transmitter and RIS jointly or independently deliver information, RM is further classified into two categories: jointly mapped RM (JRM) and separately mapped RM (SRM). JRM and SRM are naturally superior to existing schemes, because the transmit signal vectors, reflecting patterns, and bit mapping methods of JRM and SRM are more flexibly designed. To enhance transmission reliability, this paper proposes a discrete optimization-based joint signal mapping, shaping, and reflecting (DJMSR) design for JRM and SRM to minimize the bit error rate (BER) with a given transmit signal candidate set and a given reflecting pattern candidate set. To further improve the performance, this paper optimizes multiple reflecting patterns and their associated transmit signal sets in continuous fields for JRM and SRM. Numerical results show that JRM and SRM with the proposed system optimization methods considerably outperform existing schemes in BER. [ABSTRACT FROM AUTHOR]

Topology optimization techniques are very promising methods, because they allow one to obtain an initial conceptual structure starting with minimal information on the design object. This paper proposes a novel topology optimization method that takes into account an asymmetrical rotor structure. The phase angle of stator current is not considered in the topology optimization process, thanks to an asymmetrical rotor. This paper designs the rotor topology of a permanent magnet synchronous generator. The obtained rotors have several topologies by considering permanent magnets with different magnetized directions, and have a better performance than that of Prius generator. [ABSTRACT FROM AUTHOR]

This paper considers the problem of user association to small-cell base stations (SBSs) in a heterogeneous and small-cell network (HetSNet). Two optimization problems are investigated, namely, maximizing the set of associated users to the SBSs (the unweighted problem) and maximizing the set of weighted associated users to the SBSs (the weighted problem), under signal-to-interference-plus-noise ratio constraints. Both problems are formulated as linear integer programs. The weighted problem is known to be NP-hard, and in this paper, the unweighted problem is proved to be NP-hard as well. Therefore, this paper develops two heuristic polynomial-time algorithms to solve both problems. The computational complexity of the proposed algorithms is evaluated and is shown to be far more efficient than the complexity of the optimal brute-force (BF) algorithm. Moreover, this paper benchmarks the performance of the proposed algorithms against the BF algorithm, the branch-and-bound CPLEX-based algorithm, and state-of-the-art algorithms, through numerical simulations. The results demonstrate the close-to-optimal performance of the proposed algorithms. They also show that the weighted problem can be solved to provide solutions that are fair between users or to balance the load among SBSs. [ABSTRACT FROM PUBLISHER]

Total variation (TV) is a one of the most popular regularizers for stabilizing the solution of ill-posed inverse problems. This paper proposes a novel proximal-gradient algorithm for minimizing TV regularized least-squares cost functionals. Unlike traditional methods that require nested iterations for computing the proximal step of TV, our algorithm approximates the latter with several simple proximals that have closed form solutions. We theoretically prove that the proposed parallel proximal method achieves the TV solution with arbitrarily high precision at a global rate of converge that is equivalent to the fast proximal-gradient methods. The results in this paper have the potential to enhance the applicability of TV for solving very large-scale imaging inverse problems. [ABSTRACT FROM AUTHOR]

The emergence of interference alignment (IA) as a degrees-of-freedom optimal strategy motivates the need to investigate whether IA can be leveraged to aid conventional network utility maximization algorithms, which are typically only capable of finding locally optimal solutions. To test the usefulness of IA in this context, this paper proposes a two-stage optimization framework for the downlink of a $G$ -cell multi-antenna network with $K$ users/cell. The first stage of the proposed framework focuses on nulling interference from a set of dominant interferers using IA, while the second stage optimizes transmit and receive beamformers to maximize a network-wide utility using the IA solution as the initial condition. Further, this paper establishes a set of new feasibility results for partial IA that can be used to guide the number of dominant interferers to be nulled in the first stage. Through simulations on specific topologies of a cluster of base stations, it is observed that the impact of IA depends on the choice of the utility function and the presence of out-of-cluster interference. In the absence of out-of-cluster interference, the proposed framework outperforms straightforward optimization when maximizing the minimum rate, while providing marginal gains when maximizing sum rate. However, the benefit of IA is greatly diminished in the presence of significant out-of-cluster interference. [ABSTRACT FROM AUTHOR]

In this paper, circuit level analysis of the high frequency and low noise performance of an RF CMOS device with L\mathrm{ eff}= 36 nm is performed using various layout schemes. By using the modeling methodology of interconnect metals and vias, it is found that the gate parasitic capacitance from the interconnects mainly affects the degradation of high frequency and noise performance. An optimized layout scheme is proposed to reduce the gate parasitic resistance and capacitance in this paper, and the proposed layout exhibits improved RF behaviors for fT , f\mathrm {\mathrm {MAX}} , and NFmin at 26 GHz up to ~13%, ~24%, and ~18% compared with the reference layout scheme, respectively. [ABSTRACT FROM PUBLISHER]