A novel design approach for NB-IoT networks using hybrid teaching-learning optimization

In this paper, we present and address the problem of designing green LTE networks with Internet of Things (IoT) nodes. We consider the new NarrowBand-IoT (NB-IoT) wireless technology that will emerge in current and future access networks. The main objective is to reduce power consumption by responding to the instantaneous bit rate demand by the user and the IoT node. In this context, we apply emerging evolutionary algorithms to the above problem. More specifically, we apply the Teaching-Learning-Optimization (TLBO), the Jaya algorithm, and a hybrid algorithm. This hybrid algorithm named TLBO-Jaya uses concepts from both algorithms in an effective way. We compare and discuss the preliminary results of these algorithms

Shaped Beam Pattern Synthesis of Antenna Arrays Using Composite Differential Evolution with Eigenvector-Based Crossover Operator

This paper addresses the problem of designing shaped beam patterns with arbitrary arrays subject to constraints. The constraints could include the sidelobe level suppression in specified angular intervals, the mainlobe halfpower beamwidth, and the predefined number of elements. In this paper, we propose a new Differential Evolution algorithm, which combines Composite DE with an eigenvector-based crossover operator (CODE-EIG). This operator utilizes eigenvectors of covariance matrix of individual solutions, which makes the crossover rotationally invariant. We apply this novel design method to shaped beam pattern synthesis for linear and conformal arrays. We compare this algorithm with other popular algorithms and DE variants. The results show CODE-EIG outperforms the other DE algorithms in terms of statistical results and convergence speed

Application of opposition-based learning concepts in reducing the power consumption in wireless access networks

The reduction of power consumption in wireless access networks is a challenging and important issue. In this paper, we apply Opposition-Based Learning (OBL) concepts for reducing the power consumption of LTE base stations. More specifically, we present a new Modified Biogeography Based Optimization (BBO) algorithm enhanced with OBL techniques. We apply both the original BBO and the new Modified Opposition BBO (MOBBO) to network design cases to the city of Ghent, Belgium, with 75 possible LTE base station locations. We optimize the network towards two objectives: coverage maximization and power consumption minimization. Preliminary results indicate the advantages and applicability of our approach

Experimental optimization of exposure index and quality of service in WLAN networks

This paper presents the first real-life optimization of the Exposure Index (EI). A genetic optimization algorithm is developed and applied to three real-life Wireless Local Area Network scenarios in an experimental testbed. The optimization accounts for downlink, uplink and uplink of other users, for realistic duty cycles, and ensures a sufficient Quality of Service to all users. EI reductions up to 97.5% compared to a reference configuration can be achieved in a downlink-only scenario, in combination with an improved Quality of Service. Due to the dominance of uplink exposure and the lack of WiFi power control, no optimizations are possible in scenarios that also consider uplink traffic. However, future deployments that do implement WiFi power control can be successfully optimized, with EI reductions up to 86% compared to a reference configuration and an EI that is 278 times lower than optimized configurations under the absence of power control

A multi-objective approach to indoor wireless heterogeneous networks planning

We present a multi-objective optimization approach for indoor wireless network planning subject to constraints for exposure minimization, coverage maximization and power consumption minimization. We consider heterogeneous networks consisting of WiFi Access Points (APs) and Long Term Evolution (LTE) femtocells. We propose a design framework based on Multi-objective Biogeography-based Optimization (MOBBO). The results of the proposed method indicate the advantages and applicability of the multi-objective approach

Multi-cell massive MIMO network optimization towards power consumption in suburban scenarios

In this paper, we propose a simulation-based method to design low power multi-cell multi-user massive MIMO network by optimizing the positions of the base stations. Two realistic outdoor suburban areas have been considered in Ghent, Belgium (Europe) and Kinshasa, the Democratic Republic of Congo (Africa), in which the power consumption, the energy efficiency, the network capacity and the multiplexing gain are investigated and compared with LTE networks. The results of the simulations demonstrated that massive MIMO networks provide better performance in the crowded scenario where user's mobility is relatively low. A massive MIMO BS consumes 5-8 times less power than the LTE networks, with a pilot reuse pattern of 3 that helps obtaining a good tradeoff between the higher bit rate requested and the low power requirements in cellular environment