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Resource and Interference Management
Published in Wen Sun, Qubeijian Wang, Nan Zhao, Haibin Zhang, Chao Shen, Ultra-Dense Heterogeneous Networks, 2023
Wen Sun, Haibin Zhang, Nan Zhao, Chao Shen, Lawrence Wai-Choong Wong
Beamforming needs to adjust the amplitude and the phase of signals transmitted by each antenna according to prior information, so that the superimposed signal can be transmitted in the specified direction. There are two traditional beamforming methods: baseband digital beamforming and analog beamforming. Pure digital beamforming uses a digital processor to process the signals to make beamforming flexible with more degrees of freedom and less internal interference. However, the complex hardware implementation of pure digital beamforming has high energy consumption, and each antenna should be equipped with a special radio frequency link, making the application of pure digital beamforming to massive MIMO system costly. Analog beamforming uses time extension unit or equivalent phase to process the signals, reducing flexibility and generating some internal interference. However, the hardware implementation of analog beamforming is simple, and needs less energy consumption and lower cost. Based on the pros and cons of analog beamforming and digital beamforming, hybrid beamforming is considered to be the most suitable technology for 5G networks [121]. At present, there are two kinds of hybrid beamforming structures: fully-connected hybrid beamforming and sub-connected hybrid beamforming.
Antenna Design Challenges for 5G
Published in Mohammed Usman, Mohd Wajid, Mohd Dilshad Ansari, Enabling Technologies for Next Generation Wireless Communications, 2020
S. Arif Ali, Mohd Wajid, M. Shah Alam
With the launch of fifth-generation (5G) technology to cater to the demand for high data rate requirements, advanced antennas are required to deliver eMBB, URLLC, and mMTC services (Saunders 2018; Americas 2019; Sayidmarie et al. 2019). Therefore, upgraded antenna technology of massive MIMO (in contrast to the passive and limited MIMO of an earlier generation) with beamforming is an effective way to construct antenna patterns to achieve the desired goals of 5G communication. This technology requires driving M densely active antenna elements, where M = 64 or higher to build antenna beam patterns in different directions at the base station to communicate with the various mobile user equipment (UE) (see Figure 9.6a [Marzetta 2010]). Similar antenna arrays arrangements are used in the mobile UE to communicate with the base station and Wi-Fi user (see Figure 9.6b [Ojaroudiparchin, Shen, and Pedersen 2016])
Cognitive Radio Networks and Dynamic Spectrum Access
Published in Mohamed Ibnkahla, Wireless Sensor Networks, 2017
MIMO techniques refer to a family of techniques wherein multiple antennas are used to enhance system capacity and to mitigate channel impairments. These include beamforming, spatial multiplexing, and many others. Beamforming is a signal processing technique used to direct transmission and/or reception in MIMO systems. It can either be designed to maximize the communication throughput by enhancing the signal quality or to reduce interference. In general, beamforming can be used to achieve spatial selectivity by using either adaptive or fixed beam patterns to direct the transmission or the reception beam. On the other hand, spatial multiplexing is used to simultaneously transmit messages from different users by using different transmit antennas. The main advantage of spatial multiplexing is to increase the data rate achievable over the same time and frequency resources (Larsson and Stoica 2003).
Analysis of Precoder Decomposition Algorithms for MIMO System Design
Published in IETE Journal of Research, 2023
S. Markkandan, R. Logeshwaran, N. Venkateswaran
MIMO communication system transmits data through parallel subchannels by decomposing the complexMIMO channel into parallel unicast channels. The major requirement in MIMO communications is the decomposition of the data streams into parallel streams [6]. For this purpose, precoding or pre-equalization of the signal is done at the transmitter. Precoding is a simplification of the beam-forming method which supports single and multi-layer transmissions in a multi-antenna rich wireless communications environment [7] for diversity improvement and spatial multiplexing. The benefits achieved by beamforming are increased signal gain at the receiver due to the constructive addition of signals and reduction of multi-path fading [8]. In the case of single-layer beamforming, each transmitter antenna transmits the identical signal with suitable weighting so that the signal power gets maximized at the receiver. In order to increase the signal level at all the receiver antennas simultaneously multi-layer beamforming is essential [9].
Beamforming networks using the Nolen matrix for 5G applications
Published in Waves in Random and Complex Media, 2023
Hussam Keriee, Mohamad Kamal A. Rahim, Osman Ayop, Nawres Abbas Nayyef
Recently, phased antenna arrays and beamforming have gained great significance in several fields such as wireless communication systems specifically for fifth-generation (5G) technology. Generally, an antenna array is controlled by a feeding network, which maintains the amplitude and phase shift of each antenna element [1]. In such a system, the radiation beam is formed by the antenna array toward the desired direction. With this advantage, typically there are three main types of feeding networks controlling the antenna array; Parallel-feed network, series-feed network, and matrix-based network [2]. In parallel and series feed networks, the antenna array requires a large tuning wide phase shifters that are not able to generate multi beams. In contrast, a matrix-based feeding network consists of components such as a hybrid coupler, crossovers, and phase shifters [3]. In addition, a matrix-based feeding network has the feature of generating multi-beams concurrently. Several matrix feeding networks have been introduced such as the well-known Butler matrix, Blass matrix, and Nolen Matrix [4–6].
Codebooks design and performance evaluation based on antenna array response and signal to noise ratio for mmWave communication
Published in Journal of International Council on Electrical Engineering, 2018
Adam Mohamed Ahmed Abdo, Xiongwen Zhao, Abdinasir Ahmed
Beam-forming techniques help to investigate many benefits including the system throughput enhancement, spatial reuse, and minimal interference. Moreover, as the antenna size is directly proportional to wavelength, the mmWave antenna is tiny compared with another low-frequency communication. Therefore, the beam-forming technology can be used to improve the link quality as well as angle information distribution. All these factors motivate the researchers to focus on mmWave communication [4,5]. The analog beam-forming is much less hardware complexity and power consumption constraint so that it is preferred in 60 GHz indoor mmWave communication [2,5]. The main purpose of the array antenna is to improve the SNR by adding correlated signals and uncorrelated noise[6]. This improvement is measured in terms of array gain or array response [7]. The mmWave communication uses two techniques. The first is codebook based beam steering to solve energy constraint. The second one has phased array antennas with fixed amplitude beam weighting vector to solve complexity problem. In phased array antenna, the beam pattern is generated by shifting each antenna element RF signal [8]. Moreover, the codebook is referred in several ways to set antenna weight coefficients. Training is referred to the way of finding the best beam pair between transmitter and receiver [9]. Taking IEEE 802.15.3c as a reference codebook, there are many studies conducted in different ways to construct codebooks. In [9], uniform weighting based codebook is constructed by simple weighting window function and a weighting vector constituting a conjugate number. The circular codebook weights vector is constructed by circularly shifting each column [1]. In this paper, we propose a new form of codebook design based on the number of phase states. Additionally, the proposed codebook is compared with the reference codebooks using SNR and AF as performance evaluation factors.