Open-Loop Beamforming Technique for MIMO System and Its Practical Realization

The concept of close-loop beamforming for MIMO system was well known proposed the singular value decomposition on channel matrix. This technique can improve the capacity performance, but the cost of feedback channel and the complexity processing discard the interest of implementation. Therefore, this paper aims to investigate the benefit of using an open-loop beamforming for MIMO system in practical approaches. The low-profile concept of open-loop beamforming which is convenient for implementation is proposed by just inserting Butler matrices at both transmitter and receiver. The simulation and measurement results indicate that the open-loop beamforming with Butler matrix outperforms the conventional MIMO system. Although, the close-loop beamforming offers a better performance than open-loop beamforming technique, the proposed system is attractive because it is low cost, uncomplicated, and easy to implement

Refinement Method for Weighting Scheme of Fully Spatial Beamformer

So far, a wideband spatial beamformer has been proposed. This kind of beamformer has a major contribution as its weighting coefficients are real valued in which they can be simply realized by attenuators or amplifiers. However, so far, the range of attenuation or amplification is relatively large which is not practical for hardware realization. Therefore, this paper proposes a concept to reduce the range of weighting coefficients hence, the hardware realization becomes practical. In this paper, a full prototype of wideband spatial beamformer is constructed to reflect the true beamforming performance of the proposed refinement method. Its radiation patterns obtained from simulation and measurement are compared. As a result, we can reduce the attenuation or amplification range while some radiation characteristic is remained

Exploiting multi-verse optimization and sine-cosine algorithms for energy management in smart cities

[EN] Due to the rapid increase in human population, the use of energy in daily life is increasing day by day. One solution is to increase the power generation in the same ratio as the human population increase. However, that is usually not possible practically. Thus, in order to use the existing resources of energy efficiently, smart grids play a significant role. They minimize electricity consumption and their resultant cost through demand side management (DSM). Universities and similar organizations consume a significant portion of the total generated energy; therefore, in this work, using DSM, we scheduled different appliances of a university campus to reduce the consumed energy cost and the probable peak to average power ratio. We have proposed two nature-inspired algorithms, namely, the multi-verse optimization (MVO) algorithm and the sine-cosine algorithm (SCA), to solve the energy optimization problem. The proposed schemes are implemented on a university campus load, which is divided into two portions, morning session and evening session. Both sessions contain different shiftable and non-shiftable appliances. After scheduling of shiftable appliances using both MVO and SCA techniques, the simulations showed very useful results in terms of energy cost and peak to average ratio reduction, maintaining the desired threshold level between electricity cost and user waiting timeUllah, B.; Hussain, I.; Uthansakul, P.; Riaz, M.; Khan, MN.; Lloret, J. (2020). Exploiting multi-verse optimization and sine-cosine algorithms for energy management in smart cities. Applied Sciences. 10(6):1-21. https://doi.org/10.3390/app1006209512110

Enhancing the Energy Efficiency of mmWave Massive MIMO by Modifying the RF Circuit Configuration

Hybrid architectures are used in the Millimeter wave (mmWave) Massive MIMO systems, which use a smaller number of RF chains and reduces the power and energy consumption of the mmWave Massive MIMO systems. However, the majority of the hybrid architectures employs the conventional circuit configuration by connecting each of the RF chains with all the transmitting antennas at the base station. As a result, the conventional circuit configuration requires a large number of phase shifters, combiners, and low-end amplifiers. In this paper, we modify the RF circuit configuration by connecting each of the RF chains with some of the transmitting antennas of mmWave Massive MIMO. Furthermore, the hybrid analogue/digital precoders and decoders along with the overall circuit power consumptions are modelled for the modified RF circuit configuration. In addition, we propose the alternating optimization algorithm to enhance the optimal energy efficiency and compute the optimal system parameters of the mmWave Massive MIMO system. The proposed framework provides deeper insights of the optimal system parameters in terms of throughput, consumed power and the corresponding energy efficiency. Finally, the simulation results validate the proposed framework, where it can be seen that the proposed algorithm significantly reduces the power and energy consumptions, with a little compromise on the system spectral gain

Investigations into Adaptive MIMO Systems for Indoor Wireless Communications

Over the last few years, wireless communications has experienced the rapid growth in the demand for provision of high data rate wireless multimedia services. This rapid growth has triggered the search for new ways to improve the spectrum efficiency of wireless communication systems, as the available radio spectrum becomes nearly congested. Multiple Input Multiple Output (MIMO) systems have emerged as a promising technology to enhance the spectrum efficiency of present and future wireless communications systems by exploiting the spatial domain at both transmitter and receiver sites. The move to exploit the spatial or angular domain to the purpose of enhancing the operation of wireless communication systems has already been made by adaptive or smart antennas. They use multiple-element antennas with suitable signal processing only at transmitter or receiver. MIMO systems make a further step and employ multiple-element antennas accompanied by suitable signal processing algorithms both at transmitter and receiver. While smart antennas can operate well in environments characterized by approximate Line Of Sight (LOS) conditions, MIMO systems exhibit the largest advantages over the traditional Single Input Single Output (SISO) systems in pure Non Line Of Sight (NLOS) environments. Although MIMO systems have been successfully demonstrated in both theoretical and practical works, but those results are based on the assumption of independent identically distributed (i.i.d.) channels or pure NLOS conditions. Real signal propagation environments are characterized by both LOS and NLOS conditions. This is typical for indoor environments. The MIMO system has to adapt to this varying signal propagation situation. This thesis aims at investigating adaptive MIMO systems whose operation can be adapted to the varying indoor environments. The thesis commences with investigations into MIMO channel models which can be suitable for indoor environments. Both physical and non physical MIMO channels models are considered. The investigated channel models are verified by using measured data, available from various sources. The next task launched in this thesis is evaluating of the MIMO channel capacity under the mixed LOS and NLOS conditions. In this regard, a new transmission scheme is proposed to achieve the highest information transfer rate compared with the other schemes proposed in literatures. This is achieved at the expense that transmitter requires the information knowledge of Rice K factor and Signal to Noise Ratio (SNR), as measured at the receiver site. While assessing the MIMO channel capacity, of importance is the issue of signal normalizations. Because the different normalizations provide different results with respect to the assumed transmitted or received power. This thesis proposes to clearly include one of the two assumptions while observing capacity of fixed transmitted power or fixed received power. It is pointed out that this approach enables better understating of benefits of MIMO systems from the practical perspective. The MIMO capacity is a figure of merit of high importance from the viewpoint of information theory. The thesis points out that in order to assess MIMO performance from the practical point of view other measures such as Bit Error Rate (BER), computational complexity, processing delay have to be also investigated. The remaining parts of the thesis focus on adaptive MIMO OFDM system whose operation is assessing those other perform measures. A mathematical formulation of signal model is presented followed by the own-proposed adaptive algorithm implemented in MATLAB. The proposed algorithm is based on Lagrange multiplier method which offers an optimal solution without using an iterative procedure. It is shown that the new algorithm is advantageous over the most commonly used greedy algorithm. Through the manifest examples it is shown that the proposed algorithm can be of significant value to adaptive MIMO OFDM systems. The work undertaken as part of this thesis has been published in several journals and refereed conference papers, which underline the originality and significance of the thesis contributions

An Optimal Design of Multiple Antenna Positions on Mobile Devices Based on Mutual Coupling Analysis

The topic of practical implementation of multiple antenna systems for mobile communications has recently gained a lot of attention. Due to the area constraint on a mobile device, the problem of how to design such a system in order to achieve the best benefit is still a huge challenge. In this paper, genetic algorithm (GA) is used to find the optimal antenna positions on a mobile device. Two cases of 3×3 and 4×4 MIMO systems are undertaken. The effect of mutual coupling based on Z-parameter is the main factor to determine the MIMO capacity concerning the objective function of GA search. The results confirm the success of the proposed method to design MIMO antenna positions on a mobile device. Moreover, this paper introduces the method to design the antenna positions for the condition of nondeterministic channel. The concern of channel variation has been included in the process of finding optimal MIMO antenna positions. The results suggest that the averaging position from all GA solutions according to all channel conditions provides the most acceptable benefit

Performance of Antenna Selection in MIMO System Using Channel Reciprocity with Measured Data

The channel capacity of MIMO system increases as a function of antenna pairs between transmitter and receiver but it suffers from multiple expensive RF chains. To reduce cost of RF chains, antenna selection (AS) method can offer a good tradeoff between expense and performance. For a transmitting AS system, channel state information (CSI) feedback is required to choose the best subset of available antennas. However, the delay and error in feedback channel are the most dominant factors to degrade performances. In this paper, the concept of AS method using reciprocal CSI instead of feedback channel is proposed. The capacity performance of proposed system is investigated by own developing Testbed. The obtained results indicate that the reciprocity technique offers a capacity close to a system with perfect CSI and gains a higher capacity than a system without AS method. This benefit is from 0.9 to 2.2 bps/Hz at SNR 10 dB