User Clustering and Power Allocation for Energy Efficiency Maximization in Downlink Non-Orthogonal Multiple Access Systems

Non-orthogonal multiple access (NOMA) has been considered a promising technique for the fifth generation (5G) mobile communication networks because of its high spectrum efficiency. In NOMA, by using successive interference cancellation (SIC) techniques at the receivers, multiple users with different channel gain can be multiplexed together in the same subchannel for concurrent transmission in the same spectrum. The simultaneously multiple transmission achieves high system throughput in NOMA. However, it also leads to more energy consumption, limiting its application in many energy-constrained scenarios. As a result, the enhancement of energy efficiency becomes a critical issue in NOMA systems. This paper focuses on efficient user clustering strategy and power allocation design of downlink NOMA systems. The energy efficiency maximization of downlink NOMA systems is formulated as an NP-hard optimization problem under maximum transmission power, minimum data transmission rate requirement, and SIC requirement. For the approximate solution with much lower complexity, we first exploit a quick suboptimal clustering method to assign each user to a subchannel. Given the user clustering result, the optimal power allocation problem is solved in two steps. By employing the Lagrangian multiplier method with Karush–Kuhn–Tucker optimality conditions, the optimal power allocation is calculated for each subchannel. In addition, then, an inter-cluster dynamic programming model is further developed to achieve the overall maximum energy efficiency. The theoretical analysis and simulations show that the proposed schemes achieve a significant energy efficiency gain compared with existing methods.

The Economics of Innovation behind Cluster Dynamic Processes

Marshallian industrial districts may be an important stage in the emergence of networked groups of SMEs enjoying both Marshallian and Jacobian externalities. But sustainable growth depends upon transitioning to industrial districts within industrial ecosystems in which both local and national governments work together to craft and undertake policy frameworks that combine centralized strategic policy planning at the national level with decentralized authority and accountability at the local level. The concept of the industrial ecosystem is a modern expression of Marshall’s ‘collective organization of the district as a whole’. It is a way to think of a region’s population of specialized and differentiated enterprises and extra-firm, capability development infrastructures as integral to its productive structures and competitive advantage. It extends the policy domain from a market-centric to a production-centric economics perspective that focuses on capabilities and innovation. Major contributors include Adam Smith, Charles Babbage, Marshall, Allyn Young, Edith Penrose, Simon Kuznets and Jane Jacobs.

Identification of molecular cluster evaporation rates, cluster formation enthalpies and entropies by Monte Carlo method

We address the problem of identifying the evaporation rates for neutral molecular clusters from synthetic (computer-simulated) cluster concentrations. We applied Bayesian parameter estimation using a Markov chain Monte Carlo (MCMC)algorithm to determine cluster evaporation/fragmentation rates from known cluster distributions, assuming that the clustercollision rates are known. We used the Atmospheric Cluster Dynamic Code (ACDC) with evaporation rates based on quantumchemical calculations to generate cluster distributions for a set of electrically neutral sulphuric acid and ammonia clusters. We then treated these concentrations as synthetic experimental data, and tested two approaches for estimating the evaporation rates. First we have studied a scenario where at one single temperature time-dependent cluster distributions are measured before thesystem reaches a time-independent steady-state. In the second scenario only steady-state cluster distributions are measured, butat several temperatures. This allowed us to use multiple sets of concentrations at different temperatures. Additionally, in thelatter case the evaporation rates were represented in terms of cluster formation enthalpies and entropies which were considered to be free parameters. This reparametrization reduced the number of unknown parameters, since several evaporation ratesdepend on the same cluster formation enthalpy and entropy values. We show that in the second setting, even if only two temperatures were used, the temperature-dependent steady-state dataoutperforms the first setting for parameter identification. We can thus conclude that for experimentally determining evaporationrates, cluster distribution measurements at several temperatures are recommended over time-dependent measurements at one temperature.

A Clustering System for Dynamic Data Streams Based on Metaheuristic Optimisation

This article presents the Optimised Stream clustering algorithm (OpStream), a novel approach to cluster dynamic data streams. The proposed system displays desirable features, such as a low number of parameters and good scalability capabilities to both high-dimensional data and numbers of clusters in the dataset, and it is based on a hybrid structure using deterministic clustering methods and stochastic optimisation approaches to optimally centre the clusters. Similar to other state-of-the-art methods available in the literature, it uses “microclusters” and other established techniques, such as density based clustering. Unlike other methods, it makes use of metaheuristic optimisation to maximise performances during the initialisation phase, which precedes the classic online phase. Experimental results show that OpStream outperforms the state-of-the-art methods in several cases, and it is always competitive against other comparison algorithms regardless of the chosen optimisation method. Three variants of OpStream, each coming with a different optimisation algorithm, are presented in this study. A thorough sensitive analysis is performed by using the best variant to point out OpStream’s robustness to noise and resiliency to parameter changes.

Resting-state Dynamics as a Cortical Signature of Anesthesia in Monkeys

Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New Background The mechanism by which anesthetics induce a loss of consciousness remains a puzzling problem. We hypothesized that a cortical signature of anesthesia could be found in an increase in similarity between the matrix of resting-state functional correlations and the anatomical connectivity matrix of the brain, resulting in an increased function-structure similarity. Methods We acquired resting-state functional magnetic resonance images in macaque monkeys during wakefulness (n = 3) or anesthesia with propofol (n = 3), ketamine (n = 3), or sevoflurane (n = 3). We used the k-means algorithm to cluster dynamic resting-state data into independent functional brain states. For each condition, we performed a regression analysis to quantify function-structure similarity and the repertoire of functional brain states. Results Seven functional brain states were clustered and ranked according to their similarity to structural connectivity, with higher ranks corresponding to higher function-structure similarity and lower ranks corresponding to lower correlation between brain function and brain anatomy. Anesthesia shifted the brain state composition from a low rank (rounded rank [mean ± SD]) in the awake condition (awake rank = 4 [3.58 ± 1.03]) to high ranks in the different anesthetic conditions (ketamine rank = 6 [6.10 ± 0.32]; moderate propofol rank = 6 [6.15 ± 0.76]; deep propofol rank = 6 [6.16 ± 0.46]; moderate sevoflurane rank = 5 [5.10 ± 0.81]; deep sevoflurane rank = 6 [5.81 ± 1.11]; P < 0.0001). Conclusions Whatever the molecular mechanism, anesthesia led to a massive reconfiguration of the repertoire of functional brain states that became predominantly shaped by brain anatomy (high function-structure similarity), giving rise to a well-defined cortical signature of anesthesia-induced loss of consciousness.