Bio Inspired Based Proper resource Allocation (BIB-PA) for energy efficiency using optimization algorithm

doi:10.21203/rs.3.rs-1734162/v1

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Bio Inspired Based Proper resource Allocation (BIB-PA) for energy efficiency using optimization algorithm

https://doi.org/10.21203/rs.3.rs-1734162/v1

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Massive MIMO is recognized as an important technology for advancing the deployment of 5th generation and beyond (B5G) networks. In a multi-cell large-scale multi-input multi-output (MIMO) non-orthogonal multi-access (NOMA) network, a base station (BS) with multiple antennas delivers radio frequency energy downlink and Internet of Things (IoT) devices. Uses the collected energy Supports the transmission of data about uplinks. This approach uses wireless power transfer (WPT) to investigate energy efficiency (EE) issues in large multi-cell MIMO NOMA networks. To maximize the EE of the network, we propose a new joint power, time, antenna selection, and subcarrier resource allocation scheme that can appropriately allocate time for power collection and data transmission. Both complete channel state information and incomplete channel state information (CSI) are considered and the corresponding EE performance is analyzed. The EE maximization problem, which is not trivial due to non-convexity, is formulated. First, we apply fractional nonlinear programming to transform the problem into a convex problem, and then we develop an ADMM-based approach (variance alternating direction method of multipliers) to solve this problem. This ADMM is combined with error reduction on the receiving side. Since these data errors are assumed with different channel estimation parameters and their relationship to energy efficiency has not yet been evaluated, a livestock grazing optimization algorithm (CGPO) is used to specify the data size capacity range for each RF link. Introduce error reduction. The proposed CGPO algorithm keeps tracking the error correction and detection process of the receiver for each radio frequency link. Finally, the numerical and analytical results show that the maximal Energy efficiency is achieved by a massive MIMO Systems using BIB-PA approach in the MATLAB platform.

non-orthogonal multi-access (NOMA)

base station (BS)

wireless power transfer (WPT)

channel state information (CSI)

Internet of Things (IoT)

The explosive development of the Internet of Things (IoT) has stimulated a huge demand for large-scale connectivity capabilities that allow large numbers of IoT devices to access wireless networks [1]. The accessed IoT device drains the battery as soon as it is used in a high power drain application [2]. Therefore, it is important to enable large, power-constrained IoT devices to communicate with each other spectrally and power-efficiently while meeting quality of service (QoS) requirements [3]. Wireless Power Transfer (WPT) uses an uninterruptible power supply to remotely charge IoT devices with a small rechargeable battery [4]. With these technologies, battery-powered IoT devices can extract radio frequency (RF) signal energies to extend their lifespan [5]. This allows us to meet the continuous power requirements of IoT devices [6]. Massive MIMO uses beams for spatial multiplexing to effectively concentrate received signal power to very narrow beams for very dense deployments of IoT networks. Assuming zero-forced precoding, the problem of interest is formulated as a concavo-convex fractional program with a centralized optimal solution based on the general Dinkelbach algorithm [36]. To facilitate distributed implementation, design issues are transformed into equivalent convex plans using CharnesCooper's transformations. Next, based on Multipliers' ADMM (Alternative Direction Method) framework, we will develop a numerical distributed algorithm that achieves fast convergence. Method [37] considered a wireless communication network (WPCN) in which multiple users draw power from a dedicated power plant to communicate with the information receiving station. The grid's maximum achievable energy efficiency (EE) is investigated by joint time allocation and power control, taking into account the initial battery energy of each consumer. The WPCN EE maximization problem can be transformed into two simplified network EE maximization problems by taking advantage of its special structure. Using fractional programming theory, the resulting non-convex problem was transformed into a standard convex optimization problem. This allows you to characterize the optimal solution structure for shared time allocation and power control and derive an efficient iterative algorithm to obtain the optimal solution. A wireless non-orthogonal multiple access (NOMA) uplink communication system [38] consisting of a base station and multiple energy harvesting users has been proposed. More specifically, with a focus on optimizing individual data rates and improving fairness, either linear programming or convex optimization can optimally and efficiently solve formulated problems.. Massive MIMO is also a method for realizing WPT systems because it can provide system-level energy efficiency (EE) [7], [8]. In [9], user scheduling, power allocation, and rate adaptation schemes have been optimized together to improve the EE of Orthogonal Frequency Division Multiple Access (OFDMA) networks. Nevertheless, the above transmission methods [7] to [9] are performed using the Orthogonal Multiple Access (OMA) method. Because each frequency subchannel can only be used by one user at a time, OMA is limited in terms of spectral efficiency (SE) for large network access IoT devices. Non-orthogonal multiple access (NOMA) has been proposed to effectively use the communication spectrum and improve system connectivity. This allows multiple devices to access the same subchannel in the power domain at the same time [10]. ]. [11] proposed a mobile user association scheme for small cellular networks. This scheme uses NOMA and Sequential Interference Cancellation (SIC) to maximize System Utility and minimize overall transmit power. NOMA can also extend the capacity range of the OMA via transceiver power multiplexing and SIC [12].

The combination of NOMA and MIMO (called "MIMONOMA") can significantly improve SE, channel capacity, and EE [13], [14]. The EE maximization problem in uplink NOMAbased millimeterwave largescale MIMO systems under QoS constraints has been studied [15]. Installing large area antennas in a base station (BS) can centralize transmit power in some areas, improving the throughput and channel rate of large MIMO networks [16]. Nonetheless, using a large antenna array dramatically increases the power consumption of the IoT network due to the large antennas used [17]. In this sense, it is important to find an effective way to select the correct antenna number to optimize the EE of the large multicell MIMONOMA network under consideration that enables WPT. The WPT-based large-scale MIMONOMA network architecture aims to be a positive approach to improving the SE and EE of IoT networks. Due to the variety of wireless resources (time, frequency, space, power, coded main, etc.) in B5G wireless communication, Massive MIMONOMA IoT has energy-efficient resource allocation over an uninterrupted power range. It's important to do. It is also important to find an efficient radio resource allocation strategy for large, resource-constrained MIMO MONOMA IoT environments that use incomplete Channel State Information (CSI). This is because it is difficult to achieve full CSI. The accuracy of the resulting CSI depends on channel feedback and quantization error [19]. As a result, you need to leverage NOMA for WPT to implement large-scale connectivity for IoT networks and properly allocate resources to distributed multi-antenna selection (AS) MIMO large-scale networks. The EE performance of a distributed ADMM-based energy-efficient resource allocation algorithm (called "PA") is shown. High data rates and low error

MIMO systems provide great incentives for large scale implementation. Error detection and correction play important roles in improving transmit power, efficiency, and average data transmitted per second. For this reason, we propose a cattle grazing pattern optimization algorithm that detects and corrects errors, records previous errors, and selects a high gain path that lowers the error rate and increases the data rate. The algorithm is to minimize the difference in error between the output and the input.. The number of checker bits added to the message data depends on the minimum Hamming distance between the bad message pattern and the original message data. Checker bits are formed by logically XORing a bit of message data. After the checker bit is added, the bit is forwarded to the destination. The receiver checks the transmitted data in 2D matrix format for errors and finds more error patterns than a 1D check using odd and even check bits. After finding the wrong binary message data, fix the error by sending the data bits a couple of times. After finding the path spacing between the binary bits received in each instance, the algorithm compares the path spacing of each instance bit to reproduce the modified or expected bits. Bits are accepted only if a match occurs in all cases of path deletion.

This white paper considers a large multi-cell MIMO network. Combined with the CGPO algorithm for error detection and analysis of IoT devices, it sends data after getting enough power from the BS.

WPT-based distributed large-scale MIMO NOMA model. Multi-antenna BS and large-scale rechargeable IoT devices will be deployed to meet large-scale connection requirements and achieve high EE. In the performance arena, NOMA technology is used to achieve SIC and improve EE.

Apply nonlinear optimization to transform the original non-convex fractional programming problem into a convex problem. Therefore, a new distributed ADMM-based energy-efficient resource allocation algorithm has been proposed to divide the global consensus problem into multiple consecutive optimization phases.

The effect of incomplete CSI on the system EE under various channel estimation parameters, using three values for the variance of the ADMM channel estimation error. Due to the consideration of ADMM parameters, there may be the error occurrence. This can be controlled by using cattle grazing pattern optimization algorithm and power consumption model. The error reduction algorithm is constructed in a way to optimize reduce the error and increase the energy efficiency. The error reduction in turn efficiently conserve the energy and the transmit power in proposed system.

The various approaches such as Dinkelbach based centralized algorithm [36], an OMA based scheme using lagrange multiplier [37], the benchmark algorithms as Dinkelbach based CA [38] and LA OMA [36] are simulated to prove that the BIBPA will provide better outcomes.

Simulation analysis is performed by ADMM updating the Lagrange multiplier with specific parameters [35], NOMA-based PA and NOMA-based BIBPA (incomplete CSI), EE comparison, transmit power comparison. Is an implemented approach that is performed against traditional and BIBPA.

Traditional layout of big MIMO-NOMA networks centered on enhancing the machine throughput or SE [6]. [24] confirmed that using NOMA in big MIMO networks can markedly enhance the SE, as compared with the ones the use of OFDMA. In [25], strength allocation changed into optimized to maximize the EE of single-service multiuser NOMA structures. By making use of SIC and superposition coding on the WPT-enabled NOMA receiver [26], the stable price and EE have been enhanced, that can assure the statistics protection and inexperienced communication. By making use of NOMA to the beam area MIMO, [27] enlarged the accessed tool variety at the identical frequency band simultaneously, and attained a excessive SE. [28] investigated a brand new multi-antenna non-orthogonal beam area framework, which executed the big connectivity for cell big MIMO IoT with restrained spectrum resources.

In [29], a joint optimization approach of transmission strength and switch time, changed into proposed for big antenna array structures to perform a long-distance and delay-assured aid allocation. Deploying a couple of transmission antennas in big MIMO-NOMA networks ought to doubtlessly scale up the machine SE, however this approach ended in high priced RF chain hardware cost, big power intake, and excessive sign processing complexity at transmitters [30]. The AS strategies offer a sensible approach to cope with such trouble at the same time as ensuring the variety and throughput profits of big MIMO. There are only some researches that specialize in the use of AS for NOMA structures [31]. Regarding to the taken into consideration multicell WPT-powered disbursed big MIMO-NOMA networks with AS, no present works has been supplied to enhance the machine EE performance.

Even though, the several technology are evolved to growth the performance there persist trouble of power intake. The era with higher performance and decreased strength intake is constantly an appealing for the application. To triumph over the problems in big connectivity and excessive EE in modern aid-restrained IoT environments, an power-green aid allocation optimization trouble is investigated withinside the disbursed big MIMO-NOMA networks deployed a couple of rechargeable IoT devices. Different from the works beneathneath best CSI assumption, we take the imperfect CSI into consideration. The impact of AS on WPT-primarily based totally big MIMO-NOMA networks has additionally now no longer but been studied. In mild of those observations, transmit strength, time, AS and subcarrier allocation are mutually investigated for the taken into consideration network.

Different from traditional grasping and centralized algorithms the use of the Lagrange multiplier [35], we invoke the disbursed ADMM technique to iteratively clear up the primal and twin sub-issues of the convex optimization. In addition, this document derives a set of Massive MIMO rules for achieving error subtraction overpower and occasional transmit power capture. It provides a machine version of the MIMO rule set for error discounts, the use of the livestock grazing pattern optimization rule set (CGPO), and a starch intake version. Error-free rulesets are designed to reduce errors and improve performance. Error-free rebates effectively maintain the power and transmission strength of the proposed machine. The end result engine is also compared to traditional paintings to show the effectiveness of the proposed paintings. The end result is maximum EE performance

MIMO is a time allocation method that uses NOMA to maximize the individual data rates of wirelessly operated communication systems and improve fairness [38]. All formalized optimization problems were solved using linear programming and convex optimization tools. This scheme is also combined with the energy harvesting ADMM, which was considered the base. ADMM [35] is an efficient algorithm that can be applied to distributed convex optimization. An important characteristic of ADMM is its ability to quickly converge to the optimal solution with high accuracy. ADMM has been successfully applied to a variety of optimization problems that can combine the benefits of double decomposition and extended Lagrange for multiple constraint optimization problems. Divide the convex problem into several consecutive optimization phases to improve the efficiency of problem solving and the quality of the optimal solution. To solve the non-convex optimization problem, the traditional method first relaxes and transforms it into a convex problem, and then solves the transformed problem using the greedy and centralized algorithms based on the Lagrange multiplier method. .. Due to the inefficient execution of the algorithm, these methods have a relatively high number of iterations and achieve only local optimization. The algorithm consists of three steps: (a) updating global variables, (b) updating local variables, and (c) updating Lagrange multipliers. Since the current iterations of local and global variables are available for all BSs, no additional information exchange is required to update the Lagrange multiplier at this step, and therefore no additional signaling overhead is incurred. Normally, all BSs first update global variables for a relatively long period of time (or instead, in one iteration) to maintain consistent levels of interference and EE. Each BS then individually addresses its own sub-issues such as AS and sub-carrier assignments. The Lagrange multiplier is then updated by collecting and using the local CSI of the IoT device in BS. Once local convergence is achieved, BSs exchange performance values with each other to reach the optimal solution and adjust local resource allocation appropriately in the next round. This interactive and iterative process can reduce signaling overhead. No CSI needs to be exchanged between the BSs and IoT devices. In addition, large problems are separated into multiple distributed small problems and solved at the same time, greatly improving the efficiency of solving optimization problems. Use CGPO to control system errors to reduce error values in the ADMM model. Evaluating these parameters can transform the overall methodology into a better EE approach.

This section shows the EE performance of the proposed bio-inspired algorithm (, designated as "BIBPA"), compared to the distributed ADMM-based energy-efficient resource allocation algorithm (denoted as "PA"). Consider multiple cells. The radius of each cell is fixed at 500m. In a large MIMO network, the system bandwidth is normalized with B = 1 Hz and the bandwidth of each subcarrier is BS = B / S Hz. IOT devices are randomly and evenly distributed within each cell. Both path loss and Rayleigh fading are taken into account. The simulation results are based on an average of 10,000 iterations. According to [35] some system parameters are listed in Table I.

Table I Simulation parameters [35]

Parameter	Value	Parameter	Value
K	6	£	0.8
U	15	λ	10^− 7
S	20 ~ 40	B	1 Hz
P_{bs Max}	46dBm	P_DAC	10 mW
P_{user Max}	23dBm	P_ADC	10 mW
α_{, k,u}	1/75	P_filt	2.5 mW
P_min	30.3 mW	P_syn	50 mW
P_LNA	20mW	P_IFA	3 mW

Figure 1 compares the EE of BIBPA with the EE of PA. The "BIBPA" EE at full CSI turns out to be superior to all other cases, thanks to its highly efficient AS scheme and full channel conditions. Considering the estimation error in the actual channel environment, the EE of "BIBPA" at incomplete CSI is lower than the EE at full CSI and EE of "PA".

Figure 2 shows the difference in performance between a full CSI and an incomplete CSI. At a transmission distance of 100 m, the system EE at full CSI is about 3.5 times the system EE at full CSI with σ2ek, u, s = 0.7.

, if you increase the transmission distance to 200m, the EE at full CSI will be about 1.5 times that of σ2ek, u, s = 0.7. The larger the variance of the channel estimate, the larger the gap between the variances of the difference. For example, the EE of σ2ek, u, s = 0.3 is higher than in both cases, and all observations in Fig. 2 show that the EE of the system depends on the accuracy of the CSI obtained.

Figure 3 shows the number of complete and incomplete CSI EEs and antennas. It can be seen that EE increases first and then decreases with this increase in the number of antennas. For example, with an incomplete CSI (σ2ek, u, s = 0.6), the EE curve increases from about 0.6x1023 bits / j / Hz at Mk = 10. After peaking 2.2 at Mk = 30 at 2.2x103 bits / j /, the "PA" system EE shows a downward trend. The reason is that the system-EE is an appropriate number of antennas that can be effectively improved, but there are too many antennas. Immediately power consumption will increase and EE will decrease. In addition, AS can reduce the hardware accomplices of multi-antenna systems. Therefore, it is necessary to properly determine the optimum number of antennas.

Figure 4 shows EEs with different antenna numbers and CSI estimation errors. As shown in Fig. 4, to achieve maximum EE, there is optimal through power for a particular antenna number and CSI. As the transmit power increases, the system EE increases slightly, then reaches maximum, and finally moderately decreases, wondering about complete or incomplete CSI. This shows that the EE of the system can be improved by properly allocating the transmit power. It also shows the various antenna numbers Nk, u, s = 20, 25, and 40. We also compare that EE is superior to incomplete CSI under complete and incomplete CSI.

To demonstrate the convergence performance of PA, Fig. 5 compares it with the Dinkelbach-based centralized algorithm [36] and the OMA-based scheme using the Lagrange multiplier [37]. Benchmark algorithms are called Dinkelbach-based CA [38] and "LAOMA [36]". Figure 5 shows the convergence of PA and the effect of the convergence parameter p. This shows that the optimal EE can be reached with a small number of iterations by adjusting ADMM when p = 0.068, and the optimal EE can be reached with 9 iterations. With p = 0.088, you can get the best EE in 7 iterations. These results illustrate that the convergence rate is dependent on the value of p .

It also analyzes the convergence between "Dinekelbach based CA", "LA OMA", PA, and BIMPA and shows the performance degradation between EE and the rate of convergence. Compare "BIAPA" with CA, Dinikelbach's "LAOMA". As the iterative index increases, you can see that the EE decreases first and then the system EE remains unchanged. It can be said that "BIAPA", "PA" and "CA from Dinkelbach" show very similar performance. Figure 5 shows that the difference in EE performance is negligible and that the distributed method converges faster.

This research paper explores energy-efficient resource allocation in large BIBPA-based MIMO NOMA networks. Common transmit power, antenna selection, and subcarrier allocation schemes have been proposed to solve the system's EE maximization problem. We solved the problem by calling a non-linear fractional programming approach to transform the original non-convex problem into a convex problem and develop a new distributed ADMM-based energy-efficient resource allocation algorithm. We considered both full and incomplete CSI and analyzed the impact of channel estimation errors on EE performance. Error reduction of channel estimation parameters is optimized by considering the CGPO algorithm of the ADMM model. Therefore, the simulation results show the convergence and effectiveness of the proposed algorithm for complete and incomplete CSI, and better EE performance than Dinkelbach's CA and LAOMA benchmark schemes.

6.Data availability statement

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

7.Declaration of statement

The authors declare that they have no conflict of interest.

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Bio Inspired Based Proper resource Allocation (BIB-PA) for energy efficiency using optimization algorithm

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3. Literature Survey

4. Methodology Of Bib-pa

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