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Cross-Layer Design in Wireless Communications
Published in Jerry D. Gibson, Mobile Communications Handbook, 2017
Sayantan Choudhury, Jerry D. Gibson
As mentioned earlier, MIMO techniques add another dimension to the cross-layer framework. It is well known that MIMO techniques can be used to increase the data rate by using spatial multiplexing of the streams or to increase robustness by employing space-time coding. However, MIMO techniques require multiple transmit and receive antennas and power amplifiers that can increase the energy consumption. A comparison of energy consumption of MIMO techniques vs. single-antenna transmission was done in [25] where it was shown that using fixed modulation, single-antenna techniques are more energy efficient compared to MIMO, especially over small distances. However, by employing adaptive modulation, MIMO transmission can be made more energy efficient over all distances. The authors also showed that by using cooperative MIMO transmission, the energy transmission and delay could be reduced over certain distances.
Green Relay Techniques in Cellular Systems
Published in F. Richard Yu, Xi Zhang, Victor C. M. Leung, Green Communications and Networking, 2016
Yinan Qi, Fabien Heliot, Muhammad Ali Imran, Rahim Tafazolli
where λi = γi/n, i = {0,1}. The main purpose of (3.78) is the evaluation and comparison of the capacity of in-building MIMO AF systems in a faster way than time consuming Monte-Carlo simulations, and with a sufficient accuracy such that it can be used in network simulation and optimization. In addition, it can provide upper bounds on the achievable rate of generic cooperative MIMO AF systems. As far as the total power consumption of this MIMO AF system is concerned, it can be characterized as
MIMO in Vehicular Communication Networks: Channel Modeling, Cooperative Relaying, and Resource Allocation
Published in Fei Hu, Vehicle-to-Vehicle and Vehicle-to-Infrastructure Communications A Technical Approach, 2018
Christos N. Efrem, Athanasios D. Panagopoulos
The cooperative MIMO technique takes advantage of the diversity gain so as to reduce the transmission energy consumption. As shown in Figure 9.8, the source node cooperates with two of its nearby RSUs, implementing a cooperative MISO scheme to transmit a message to a vehicle (destination node). In addition to this, a cooperative MIMO transmission for I2V communication is illustrated in Figure 9.9.
A Transmit Antenna Selection based Energy-Harvesting MIMO Cooperative Communication System
Published in IETE Journal of Research, 2023
In this paper, a cooperative MIMO wireless system is considered with multiple antennas at the source. A power splitting relay harvests energy for transmitting the amplified signal to the destination. The novel contributions of the paper are summarized as: A transmit antenna selection scheme viz., norm-based antenna selection with energy harvesting is evaluated for a cooperative MIMO system using linear precoding.The detailed analysis of energy harvesting with bit error rate (BER) is presented and trade-off between the two parameters is achieved.The impact of choosing the optimal relay on this trade-off is discussed.Comparison of proposed antenna selection scheme with received SNR-based antenna selection and random antenna selection is performed.
A Back propagation Neural Network Model for HWSNs Using IMIMO with a Secured Routing Mechanism
Published in IETE Journal of Research, 2022
Y. Ishizuka et al. [20] studied the consumption of energy using a scheme called MPSK where space-time block coding is used in MIMO WSNs. There is an energy deduction at the upper band and the total consumption of energy is shown for both long and short distances, thereby obtaining optimal constellation size. Utilizing the cooperative MIMO, the energy is consumed at a long transmission distance.
A novel massive MIMO strategy for optimal antenna selection via hybrid algorithm
Published in International Journal of Electronics, 2022
Inumula Veeraraghava Rao, S. S. S Kalyan, S. Nagendram, John Philip Bhimavarapu
Hanif et al. (2018) have introduced an approach known as the ‘low-complexity antenna-subset selection method’ in ZF. This approach sequentially selected an antenna, which contributed more to the entire system’s sum rate. Research outcomes revealed that the adopted method achieved lower computational complexities than other existing schemes. Tang and Nie (2018) have introduced a system that resolved the AS crisis inside Large MIMO systems by optimizing channel capacity. In addition, a TAS method was introduced for M-MIMO in the post-processing step of the SMV framework. From the results obtained in the SMV approach, a rectangular sub-matrix has been picked up that significantly increased the volume in the channel matrix. Shaik et al. (2019) have suggested the QAM scheme for non-regenerative cooperative MIMO systems including TAS. The authors have considered a non-regenerative MIMO system with the AS technique for data transmission via a single transmit antenna. Jin et al. (2019) have introduced an AS method with the consideration of EE under the uplink massive MIMO system. The selection of antennas with higher CSI was created depending on the 2-norm channel matrix for data transmission. Abdullah et al. (2020) have presented the two effective low complexity AS methods for downlink Multi-User (MU) M-MIMO systems with MF precoding. UCAS is the first algorithm and SIRAS is the second one. By storing the multiplications of any two items in the channel matrix before the iterative algorithms begin, both techniques prevent vector multiplications during the iterative selection process. Nguyen et al. (2020) have implemented the TAS with full-duplex relaying of spatial modulation MIMO in IoT systems. The deployment of TAS in the SM-MIMO-FDR method was analyzed, in which the relay utilized the DF protocol. In existing research on the SM-MIMO method, TAS was ignored frequently due to the computational complexity, especially when FDR was deployed in the device. Tian et al. (2020) have developed receive AS and joint beam forming design in wiretap channels of large-scale MIMO. In addition, a BAB algorithm was used for identifying the optimal receiver antenna subset from imperfect and perfect CSI states. Initially, the problem of secrecy rate optimization was formulated in perfect CSI states. The authors have defined a minimizing objective function for making the issue appropriate in the BAB search. Nosrati et al. (2020) have introduced the multi-stage AS for adaptive beam forming in MIMO radar. Here, four phases of MIMO array configurations were developed with four selection strategies that provided low hardware cost in RF and DSP stages and reduced post-processing. The authors have defined the issues as the increased determinant and developed a united evaluation for decoupling the problem in joint.