Energy and spectral efficiency are the main challenges in 5th generation of mobile cellular networks. In this paper, we propose an optimization algorithm to optimize the energy efficiency by maximizing the spectral efficiency. Our simulation results show a significant increase in terms of spectral efficiency as well as energy efficiency whenever the mobile user is connected to a low power indoor base station. By applying the proposed algorithm, we show the network performance improvements up to 9 bit/s/Hz in spectral efficiency and 20 Gbit/Joule increase in energy efficiency for the mobile user served by the indoor base station rather than by the outdoor base station.
One of the crucial advancements in next-generation 5G wireless networks is the use of high-frequency signals specifically those are in the millimeter wave (mm-wave) bands. Using mmwave frequency will allow more bandwidth resulting higher user data rates in comparison to the currently available network. However, several challenges are emerging (such as fading, scattering, propagation loss etc.), whenever we utilize mm-wave frequency wave bands for signal propagation. Optimizing propagation parameters of the mm-wave channels system are much essential for implementing in the real-world scenario. To keep this in mind, this paper presents the potential abilities of high frequencies signals by characterizing the indoor small cell propagation channel for 28, 38, 60 and 73 GHz frequency band, which is considered as the ultimate frequency choice for many of the researchers. The most potential Close-In (CI) propagation model for mm-wave frequencies is used as a Large-scale path loss model. Results and outcomes directly affecting the user experience based on fairness index, average cell throughput, spectral efficiency, cell-edge user’s throughput and average user throughput. The statistical results proved that these mm-wave spectrum gives a sufficiently greater overall performance and are available for use in the next generation 5G mobile communication network.
Non-Orthogonal Multiple Access (NOMA) with Successive Interference Cancellation (SIC) is one of the promising techniques proposed for 5G systems. It allows multiple users with different channel coefficients to share the same (time/frequency) resources by allocating several levels of (power/code) to them. In this article, a design of a cooperative scheme for the uplink NOMA Wi-Fi transmission (according to IEEE 802.11 standards) is investigated. Various channel models are exploited to examine the system throughput. Convolutional coding in conformance to IEEE 802.11a/g is applied to evaluate the system performance. The simulation results have been addressed to give a clear picture of the performance of the investigated system.
Utilization of drones is going to become predominated in cellular networks as aerial base stations in order to temporary cover areas where stationary base stations cannot serve the users. Detecting optimal location and efficient number of drone-Base Stations (DBSs) are the targets we tackle in this paper. Toward this goal, we first model the problem using mixed integer non-linear programming. The output of the proposed method is the number and the optimal location of DBSs in a two-dimension area, and the object is to maximize the number of covered users. In the second step, since the proposed method is not solvable using conventional methods, we use a proposed method to solve the optimization problem. Simulation results illustrate that the proposed method has achieved its goals.
Due to rapid development of wireless systems and future implementation of the 5G system, it is necessary to increase number of the stations and/or number of radio emissions in current and new mobile service frequency bands. For each of the new or modified radio installation in Poland the Electromagnetic Field (EMF) strength levels has to be evaluated and measured/validated in accordance with allowable limits. In the paper the model of estimation of total EMF levels coming from mobile base stations radio emissions to be used for estimation of the whole country territory EMF levels is proposed. Results of preliminary analysis were also shown on practical examples. The model presented in the paper can be used for initial finding of possible places where exist the risk of exceedance of the maximum exposure limits and for analysis of potential radio network development taking into account current regulatory limits. The model will be used in computerized system SI2PEM which is developing in Poland for EMF levels controlling and validation purposes.
Massive multiple-input-multiple-output (MIMO) and beamforming are key technologies, which significantly influence on increasing effectiveness of emerging fifth-generation (5G) wireless communication systems, especially mobile-cellular networks. In this case, the increasing effectiveness is understood mainly as the growth of network capacity resulting from better diversification of radio resources due to their spatial multiplexing in macro- and micro-cells. However, using the narrow beams in lieu of the hitherto used cell-sector brings occurring interference between the neighboring beams in the massive-MIMO antenna system, especially, when they utilize the same frequency channel. An analysis of this effect is the aim of this paper. In this case, it is based on simulation studies, where a multi-elliptical propagation model and standard 3GPP model are used. We present the impact of direction and width of the neighboring beams of 5G new radio gNodeB base station equipped with the multi-beam antenna system on the interference level between these beams. The simulations are carried out for line-of-sight (LOS) and non-LOS conditions of a typical urban environment.
With the advent of massive MIMO and mmWave, Antenna selection is the new frontier in hybrid beamforming employed in 5G base stations. Tele-operators are reworking on the components while upgrading to 5G where the antenna is a last-mile device. The burden on the physical layer not only demands smart and adaptive antennas but also an intelligent antenna selection mechanism to reduce power consumption and improve system capacity while degrading the hardware cost and complexity. This work focuses on reducing the power consumption and finding the optimal number of RF chains for a given millimeter wave massive MIMO system. At first, we investigate the power scaling method for both perfect Channel State Information (CSI) and imperfect CSI where the power is reduced by ��/���� and ��/√���� respectively. We further propose to reduce the power consumption by emphasizing on the subdued resolution of Analog-to-Digital Converters (ADCs) with quantization awareness. The proposed algorithm selects the optimal number of antenna elements based on the resolution of ADCs without compromising on the quality of reception. The performance of the proposed algorithm shows significant improvement when compared with conventional and random antenna selection methods.
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