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Adaptive Scheduling for Beyond 3G Cellular Networks
Published in Mohamed Ibnkahla, Adaptation and Cross Layer Design in Wireless Networks, 2018
Studies to implement efficient adaptive scheduling techniques for OFDMA follow two main chains, each of which has its importance and complementarities to the other chain. These two chains can be defined as follows:Adaptive physical layer scheduling: In this study chain, the designed scheduling techniques consider only the conditions of the user physical channels in the adaptation process. In this case, the traffic of all users is regarded as a continuous stream of bits. The scheduling algorithms are thus designed to adaptively allocate the OFDMA subcarriers or subchannels to different users, then adaptively load the subcarriers granted by each user with coded bits from its traffic stream.Adaptive cross-layer scheduling: In this study chain, the designed scheduling algorithms consider both channel and traffic conditions of users in the adaptation process. However, these schedulers are mainly designed to adaptively allocate the subcarriers or subchannels to users without paying much attention to the bit loading issue.
Introduction
Published in M. K. Salman Alnaimi, Yahya Abid, Badlishah R. Ahmad, Mobile WiMAX Systems, 2014
Mohammed K. Salman, Yahya Abid, Badlishah R. Ahmad
OFDMA facilitates the allocation of user resources in the time domain (slots) and frequency domain (subchannels); therefore, the allocation of radio resources can be controlled in time and frequency for every user. In order to achieve the goal of the FFR technique, the downlink (DL) subframe is divided into two parts. The first one is called the R1 zone and is used to serve users of a cell center area (yellow part), where all the available subchannels (bandwidth) are used (F1, F2, and F3), as shown in Figure 1.2b. The second part is called the R3 zone and is used to serve users of a cell border area. In the R3 zone, the subchannels (bandwidth) are divided into three segments or three bands (F1, F2, and F3), where each cell can only use one band to serve cell border users, as illustrated in Figure 1.2a.
Skeleton Extraction of Routing Topology in UAV Networks
Published in Fei Hu, Xin-Lin Huang, DongXiu Ou, UAV Swarm Networks, 2020
Zhe Chu, Lin Zhang, Zhijing Ye, Fei Hu, Bao Ke
New antennas or other physical components should be considered in lower layers. In general, there are other four types of antennas [12]: single-input single-output (SISO), single-input multi-output (SIMO), multi-input single-output (MISO) and multi-input multi-output (MIMO). The backbone UAVs may need to use MIMOs for multipath propagation. This enables UAVs to send and receive more than one packet simultaneously over the same radio channel. MIMO is often affiliated with orthogonal frequency division multiplexing (OFDM) or orthogonal frequency division multiple access (OFDMA) for high-throughput transmissions.
User-relay assisted cellular networks with multiple antennas
Published in International Journal of Electronics, 2019
OFDMA is a very popular transmission technology for high data rate communication systems because of the robustness against frequency-selective fading and high spectral efficiency. Using multiple antennas technologies such as multiple-input multiple-output (MIMO) or multiple-input single-output (MISO) also provides a capacity gain without increasing the bandwidth or transmit power in rich scattering environments and diversity gain to combat signal fading compared to classical single-input single-output (SISO) systems. However, if there are long distances between the users and the base station (BS), it may be difficult to reach the required capacity values by using only multiple antennas in conventional cellular networks. Moreover, it is difficult to cover areas suffering from bad channel conditions. Additional BSs can be established on these networks as a solution but the new BSs increase the cost of mobile operators. Using low-cost relay stations (RSs) in cellular networks without deploying new BSs can be an alternative cost-effective solution for this problem. In relaying operation, path loss and shadowing effects become less dominant so the low power communication is possible since the direct path is divided into shorter links (Laneman, Tse, & Wornell, 2004; Pabst et al., 2004).
Performance of modified and low complexity pulse shaping filters for IEEE 802.11 OFDM transmission
Published in Journal of Information and Telecommunication, 2019
Tulsi Pawan Fowdur, Louvi Doorganah
Inter Symbol Interference (ISI) is a problem that is encountered in most communication systems including 802.11a/g. ISI occurs when the symbols in OFDM overlap with each other in bandlimited channels. As a result, the probability of error due to distortion effects increases and the performance of the OFDM system degrades. Pulse shaping filters are therefore used to limit the effects of ISI and improve the error performance of the OFDM system. Pulse shaping filters have several conditions to satisfy to curb the ISI effects (Nyquist, 1928). The main condition is that the equivalent impulse response of the transmitting and receiving filters should have zero crossings at multiples of the symbol period, T (Nyquist, 1928). Several works have proposed enhanced pulse shaping filters to solve the problem of ISI and to improve the performance of OFDM. An overview of the current pulse shaping filters is given next.
Optimised periodic precoder-based blind channel estimation for MIMO-OFDM systems
Published in International Journal of Electronics Letters, 2018
Bhasker Gupta, Shivani Gupta, Ashutosh Kumar Singh, Hem Dutt Joshi
MIMO (multiple input multiple output) (Bölcskei & Zurich, 2006; Spencer, Peel, Swindlehurst, & Haardt, 2004) along with orthogonal frequency-division multiplexing (OFDM) (Changping, Ying, Kyujin, & Kyesan, 2014; Gupta & Saini, 2013a, 2013b; Hu, Du, Zhang, & Wang, 2014; Motazedi & Dianat, 2016) serve as platform for 4G and beyond wireless networks. The channel estimation in such systems is quite challenging because data symbols are sent in blocks. The estimation techniques like subspace method (Muquet, De Courville, & Duhamel, 2002; Xu, 2002) using pilot symbols (Hassibi & Hochwald, 2003; Hidayat, Isnawati, & Setiyanto, 2011; Negi & Cioffi, 1998) and DFT method (Jie & Liqun, 2011; Zheng, Su, & Wang, 2006) result in high computational complexity, bandwidth inefficiency and channel order overestimation. To overcome these problems, various blind channel estimation algorithms and its variants are proposed in Müller, Cottatellucci, & Vehkaperä (2014); Tu & Champagne (2012) and Zhang (2002). One of most common blind estimation technique in OFDM systems is cyclic prefix (CP) based, which shows better performance at high signal-to-noise ratio (SNR) values but with high computational cost. However, non-redundant blind estimation techniques are also proposed to achieve better results at moderate SNR values with reduced complexity (Gao & Nallanathan, 2007a, 2007b; Shin, Heath, & Powers, 2008).