It is shown that, particularly for terrestrial cellular telephony, the interference-suppression feature of CDMA (code division multiple access) can result in a many-fold increase in capacity over analog and even over competing digital techniques. A single-cell system, such as a hubbed satellite network, is addressed, and the basic expression for capacity is developed. The corresponding expressions for a multiple-cell system are derived. and the distribution on the number of users supportable per cell is determined. It is concluded that properly augmented and power-controlled multiple-cell CDMA promises a quantum increase in current cellular capacity. >

On the capacity of a cellular CDMA system

In studying the performance of coded communications over memoryless channels (with or without fading), the results are given as upper bounds on the average bit error probability (BEP). In principle, there are three different approaches to arriving at these bounds, all of which employ obtaining the so-called pairwise error probability , or the probability of choosing one symbol sequence over another for a given pair of possible transmitted symbol sequences, followed by a weighted summation over all pairwise events. In this chapter, we will focus on the results obtained from the third approach since these provide the tightest upper bounds on the true performance. The first emphasis will be placed on evaluating the pairwise error probability with and without CSI, following which we shall discuss how the results of these evaluations can be used via the transfer bound approach to evaluate average BEP of coded modulation transmitted over the fading channel.

Coded Communication over Fading Channels

The dielectric properties of tissues

. Groundwater is one of the most valuable natural resources in the world (Jha
et al., 2007). However, it is not an unlimited resource; therefore
understanding groundwater potential is crucial to ensure its sustainable use.
The aim of the current study is to propose and verify new artificial
intelligence methods for the spatial prediction of groundwater spring
potential mapping at the Koohdasht–Nourabad plain, Lorestan province, Iran.
These methods are new hybrids of an adaptive neuro-fuzzy inference system
(ANFIS) and five metaheuristic algorithms, namely invasive weed optimization
(IWO), differential evolution (DE), firefly algorithm (FA), particle swarm
optimization (PSO), and the bees algorithm (BA). A total of 2463 spring
locations were identified and collected, and then divided randomly into two
subsets: 70 % (1725 locations) were used for training models and the
remaining 30 % (738 spring locations) were utilized for evaluating the
models. A total of 13 groundwater conditioning factors were prepared for
modeling, namely the slope degree, slope aspect, altitude, plan curvature,
stream power index (SPI), topographic wetness index (TWI), terrain roughness
index (TRI), distance from fault, distance from river, land use/land cover,
rainfall, soil order, and lithology. In the next step, the step-wise
assessment ratio analysis (SWARA) method was applied to quantify the degree
of relevance of these groundwater conditioning factors. The global
performance of these derived models was assessed using the area under the
curve (AUC). In addition, the Friedman and Wilcoxon signed-rank tests were
carried out to check and confirm the best model to use in this study. The
result showed that all models have a high prediction performance; however,
the ANFIS–DE model has the highest prediction capability (AUC  =  0.875),
followed by the ANFIS–IWO model, the ANFIS–FA model (0.873), the ANFIS–PSO
model (0.865), and the ANFIS–BA model (0.839). The results of this research
can be useful for decision makers responsible for the sustainable management
of groundwater resources.

/pdf/spatial-prediction-of-groundwater-spring-potential-mapping-19c8mk4r18.pdf

Spatial prediction of groundwater spring potential mapping based on an adaptive neuro-fuzzy inference system and metaheuristic optimization

Optimal chiller loading by improved invasive weed optimization algorithm for reducing energy consumption

Invasive weed optimization for model order reduction of linear MIMO systems

Cognitive radio is an emerging wireless technology that is envisaged as a solution to the spectrum scarcity issue. To improve spectrum utilization, cognitive (unlicensed) wireless users are assigned an opportunistic access to vacant channels on the condition they avoid interference with primary (licensed) users. In this paper we present an impressive design of a low complexity and high efficiency dynamic spectrum access technique for cognitive radio networks. This spectrum assignment algorithm does not require central controllers nor the pre-establishment and maintenance of common control channels. Yet, it can provide throughput and fairness levels that approach the performance of centralized systems. In addition, the proposed technique reacts extremely well to disturbances in the cognitive radio network configuration, including when primary users are activated, or when newcomer cognitive users join the network. Furthermore, we present in this work an analytical model that can be used to provide quick predictions of the performance of our proposed algorithm.

Dynamic resource allocation using load estimation in distributed cognitive radio systems

Predictive maintenance is a smart solution for many industrial and commercial plants; it enables users to fix their devices before they fail. It is based on a mathematical model that predicts the failure time of the connected devices. In order to optimize the mathematical model large amount of data is required that increases the accuracy of prediction. Internet of Things (IoT) can provide these models with the required large data. Machine learning techniques have been used to produce increasingly effective solutions to predict the remaining useful life (RUL) of devices accurately. In this research a mathematical model is devised for general device that is connected to a central processing unit via IoT. The IoT provides a means of communication for the devices. It collects a huge amount of data from various sites. The large amount of collected data makes the prediction much closer to the true values. Two types of Neural Nets is used, Vanilla-RNN and LSTM-RNN, both showed good prediction results for a simple example of light bulbs. LSTM-RNN demonstrates better prediction results, where we recommend for critical devices. Other devices can use Vanilla-NN for its simplicity.

IOT Based Predictive Maintenance Using LSTM RNN Estimator

Antenna arrays are used in the CDMA-based cellular systems in order to increase the systems capacity. Different approaches benefit from the spatial separation between users. Smart and adaptive beam antennas are the best proposed solution for these systems. Several methods are used to provide the system with a radiation pattern that increases the signal-to-interference ratio (C/I). Antenna configuration is the way the array elements are distributed in space. This distribution can influence the design of the beam formation or the adaptation method, such that, the calculation of the different parameters requires a known array configuration. Antenna configuration is discussed in several research papers including planar arrays and circular arrays. Here, we introduce a general antenna array structure and derived its optimal parameters, and then we optimize the solution under different quantization errors using a genetic algorithm (GA) and an ant colony optimization (ACO). Results showed that we can configure th...

A General Configuration Antenna Array for Multi-User Systems with Genetic and Ant Colony Optimization

As the traditional resources (such as frequency, time, and space) are efficiently utilized, it becomes more and more challenging to satisfy the ever-lasting, capacity-growing, and users-boosting de

Jamal S. Rahhal

Papers

Invasive weed optimization for model order reduction of linear MIMO systems

Dynamic resource allocation using load estimation in distributed cognitive radio systems

IOT Based Predictive Maintenance Using LSTM RNN Estimator

A General Configuration Antenna Array for Multi-User Systems with Genetic and Ant Colony Optimization

General configuration MIMO system with arbitrary OAM