Combining insertion losses (CIL) and the non-linear (NL) behavior of rectification circuitry pose significant challenges to element splitting-based self-sustainable Reconfigurable Intelligent Surfaces (ESS-RIS) that utilize an RF-combining architecture for their energy harvesting (EH) elements. These factors not only degrade the end-to-end communication performance but also hinder the ability to meet ESS-RIS’s operational energy requirements. This work, therefore, proposes a novel clustering-based energy harvesting architecture for EH elements in an ESS-RIS that optimizes element allocation, enhancing both self-sustainability and overall communication efficiency compared to the state-of-the-art. Our findings demonstrate that the proposed architecture maintains a significantly higher signal-to-noise ratio (SNR) by reducing the fraction of RIS elements required for EH by a large percentage. Following this, a statistical analysis using the Marcum-Q function and the central limit theorem approximation is also performed for the proposed architecture to compare the results to the one obtained from exact simulations in MATLAB. To address the increased hardware demands in the proposed architecture, an optimization problem is formulated and tackled using three approaches, viz, Joint Parameter Optimization, Alternating Optimization, and the Genetic Algorithm. These methods aim to balance communication performance with hardware complexity effectively. Finally, a time complexity analysis is conducted to evaluate the asymptotic worst-case and best-case bounds of the proposed approach.

The integration of reconfigurable intelligent surfaces (RIS) and simultaneous wireless information and power transfer (SWIPT) present a promising solution for sustainable and efficient wireless communications in large-scale IoT networks, especially within energy-constrained smart agriculture applications. However, most existing works assume ideal energy harvesting (EH) models and continuous RIS phase shifts that limit their practical relevance. This paper addresses these limitations by proposing a total transmit power minimization framework for a multi-user RIS-assisted MISO power-splitting (PS) SWIPT system. First, a practical logistic non-linear energy harvesting (NL-EH) model is adopted to better reflect the realistic behavior of RF energy conversion circuits. Second, a discrete phase shift (DPS) model with finite quantization levels is employed to account for practical RIS hardware constraints. Third, an alternating optimization algorithm is developed for the resulting non-convex optimization problem through joint optimization of the base station’s beamforming vectors, RIS reflection matrix, and PS ratio. Techniques such as Zero-Forcing (ZF), Semidefinite Relaxation (SDR), and Gaussian randomization (GR) are leveraged to address the associated sub-problems, while an alternate one-dimensional search strategy is used for RIS phase shift optimization. Finally, numerical simulations are conducted to validate the proposed framework in terms of performance and convergence. The results demonstrate robustness under imperfect channel state information (ICSI) and varying system parameters for next-generation IoT-enabled smart farming use-case.

This paper investigates the effectiveness of employing multiple reconfigurable intelligent surfaces (RIS) for simultaneous wireless information and power transfer (SWIPT) in a vehicle-to-infrastructure (V2I) system. The optimal RIS is selected for transmission based on instantaneous signal-to-noise ratio (SNR) values, with the objective of optimizing the SWIPT system employing the power-splitting (PS) protocol and nonlinear energy harvesting (NL-EH). A unified objective is proposed to maximize information rate and harvested energy via joint optimization of transmit power and power splitting factor. Nonconvexity is addressed via an iterative algorithm, supported by closed-form expressions obtained through Karush-Kuhn-Tucker (KKT) conditions. Monte-Carlo simulations are performed to validate the accuracy of the analytical expressions. Additionally, a deep neural network (DNN) framework is introduced for realtime optimization prediction, achieving superior SWIPT performance over single RIS configurations with reduced complexity and faster execution.

In this paper, we introduce a novel architecture for simultaneous wireless information and power transfer (SWIPT) featuring a single transmitter, an Intelligent Reflecting and Refracting Surface with multiple elements, and two users respectively receiving the reflected and refracted signals. A time-switching (TS) architecture of SWIPT is employed at the receivers, enabling orthogonal energy harvesting (EH) and information decoding. We formulate an optimization problem to maximize the sum throughput while meeting the EH constraints at both users. The optimal transmit power is determined while ensuring constraint satisfaction. A bisection method is used to find the optimal TS ratio, leveraging the unimodal nature of the throughput and EH functions for computational efficiency and guaranteed convergence. Numerical simulations validate the proposed approach, demonstrating significant performance gains in SWIPT systems.

The conventional reflection-only Reconfigurable Intelligent Surface (RIS) extends its 180-degrees coverage to 360-degrees with Simultaneously Transmitting and Reflecting-RIS(STAR-RIS). When integrated with the Simultaneous Wireless Information and Power Transfer (SWIPT) system, it enhances performance by directing energy and information signals to energy receiver (ER) and information receiver (IR), with Energy Splitting (ES) and Mode Switching (MS) operational protocol. Considering a practical system with discrete phase shift (DPS) model and non-linear energy harvesting (EH) model, we aim to maximize the weighted rate-energy (WRE) function, with certain QoS constraints by optimizing power parameters. An alternating optimizing (AO) algorithm solves the complicated WRE maximization problem. Numerical results highlight the impact of energy split ratio and element division on the performance, with the critical selection of DPS level (quantization bits) balancing device bulkiness (phase control bits) and system efficiency.

Distributed Reconfigurable Intelligent Surfaces (D-RIS) in a Simultaneous Wireless Information and Power Transfer (SWIPT) framework is investigated in this paper to enhance spectral efficiency (SE) and energy harvesting (EH) for an Internet-of-Things (IoT) node utilizing a power-splitting (PS) protocol with non-linear EH model. Two types of RIS selection strategies, namely Exhaustive RIS Approach (ERA) and Optimal RIS Approach (ORA) are analyzed and compared. Algorithmic solutions based on divide-and-conquer strategy are developed to optimize the PS ratio and transmission power, ensuring improvements in distinct objective of SE and EH. The analysis is performed through a comprehensive case study of five different scenarios, focusing on various factors such as RIS surface placement, node-to-RIS distance, RIS size, and their combined impact on system performance. Numerical simulations provide insights into the system's behavior under different configurations, revealing that the ERA strategy consistently outperforms the ORA in achieving better SE and EH outcomes. These results are viable in designing IoT systems using distributed RISs in data and energy domains.

Intelligent reflecting surfaces (IRSs) possess the capability to enrich connectivity within dynamic sixth-generation (6G) vehicular communication networks by redirecting signals in desired directions. This study explores a vehicle-to-vehicle (V2V) communication setup bolstered by multiple IRSs, evaluating its efficacy and pinpointing the optimal IRS considering end-to-end channel conditions tailored for applications within the Internet of Vehicles (IoV). The investigation extends to crafting an optimization framework aimed at maximizing data rates while minimizing transmit power. We derive approximate closed-form equations for key performance metrics, such as outage probability (OP), average symbol error rate (ASER), and ergodic capacity (EC), over an independent and nonidentically distributed (i.n.i.d.) double generalized Gamma (dGG) fading channel. To corroborate our theoretical findings, Monte-Carlo simulations are conducted. Moreover, we formulate closed-form expressions for optimization quandaries leveraging the Karush-Kuhn–Tucker (KKT) conditions. Additionally, we introduce a deep neural network (DNN) framework to extract various performance metrics based on Monte-Carlo simulations and to predict optimizations in real-time scenarios. Our findings underscore that the integration of multiple IRSs, along with augmenting the number of elements within each IRS, significantly amplifies system performance in contrast to existing V2V systems detailed in the literature. Furthermore, the DNN framework mitigates computational complexity and streamlines execution times compared to conventional simulation methods.

This paper addresses the challenge of balancing conflicting goals, namely data rate and energy harvesting (EH) in Simultaneous Wireless Information and Power Transfer (SWIPT) systems, while incorporating Reconfigurable Intelligent Surface (RIS) technology. We formulate a weighted optimization objective to address this issue, seeking to simultaneously maximize data rate, EH, and minimize transmit power utilization. The proposed approach involves optimizing time switching (TS) ratios and transmit power using a practical phase-dependent amplitude model for each RIS element’s reflectivity. To address this complex optimization problem involving ratio of concave-convex problem, the paper introduces fractional programming-based modified Dinkelbach Algorithm providing upper and lower bounds, which are then compared with Quadratic transform-related algorithms and solutions based on Karush-Kuhn-Tucker (KKT) conditions. Numerical findings highlight the effectiveness of the proposed algorithms in enhancing the overall performance of SWIPT systems with RIS technology.

In this paper, we explore the utilization of multiple reconfigurable intelligent surfaces (RIS) for simultaneous wireless information and power transfer (SWIPT) in a Vehicle-to-Infrastructure (V2I) system. The selection of RIS from several options is determined by the end-to-end instantaneous signal-to-noise ratio (SNR). Specifically, our work focuses on the time-switching (TS) SWIPT protocol, integrating non-linear energy harvesting (EH). The primary goal is to maximize information rate and harvested energy at the receivers by jointly optimizing transmit power and time-switching factors. To address the non-convex nature of the problem, the formulation is relaxed and solved iteratively in an alternating manner. In addition, closed-form expressions for optimization problems are derived using the Karush-Kuhn-Tucker (KKT) conditions, and the accuracy of the analysis is validated through comprehensive Monte Carlo simulations. Results demonstrate the significant superiority of our proposed multi-RIS aided schemes over single RIS configurations in terms of both information rate and maximum energy harvesting requirements. The integration of multi-RIS assisted communication setups exhibits substantial potential for enhancing SWIPT performance in V2I systems.

Future generation of Wireless Communications encompasses massive connectivity of energy-starved and heavy-data driven billions of Internet-of-Things (IoT) devices. In this vein, Reconfigurable Intelligent Surface (RIS) holds great promise while providing improved performance and efficiency in terms of energy, cost and spectrum. Simultaneous Wireless Information and Power Transmission (SWIPT) in conjunction with RIS makes a great partnership to suffice the IoT demands. This paper examines a SWIPT-IoT system that utilizes power-splitting (PS) and non-linear energy harvesting (EH) model to achieve more data rates in constraint environment. The IoT node receives both energy and information from the base station via RIS. We present a combined problem that aims to optimize the individual objectives of rate, EH, and transmit power, while taking into account various sets of quality-of-service (QoS)-based constraints. We introduce a set of iterative optimization algorithm that utilize a divide-and-conquer approach to effectively solve the aforementioned problems. Based on our computational results, we confer that in order to reap the benefits of PS-based SWIPT-IoTs, it is imperative to increase the size of RIS and position them in optimal proximity to both the base station and the user.

The reliability of command and control (C2) operation of the UAV is one of the crucial aspects for the success of UAV applications beyond 5G wireless networks. In this paper, we focus on the short-packet communication to maximize the coverage range of reliable UAV control. We quantify the reliability performance of the C2 transmission from a multi-antenna ground control station (GCS), which also leverages maximal-ratio transmission beamforming, by deriving the closed-form expression for the average block error rate (BLER). To obtain additional insights, we also derive the asymptotic expression of the average BLER in the high-transmit power regime and subsequently analyze the possible UAV configuration space to find the optimum altitude. Based on the derived average BLER, we formulate a joint optimization problem to maximize the range up to which a UAV can be reliably controlled from a GCS. The solution to this problem leads to the optimal resource allocation parameters including blocklength and transmit power while exploiting the vertical degrees of freedom for UAV placement. Finally, we present numerical and simulation results to corroborate the analysis and to provide various useful design insights.

Proliferating applications of unmanned aerial vehicles (UAVs) impose new service requirements, leading to several challenges. One of the crucial challenges in this vein is to facilitate the autonomous navigation of UAVs. Concretely, the UAV needs to individually process the visual data and subsequently plan its trajectories. Since the UAV has limited onboard storage constraints, its computational capabilities are often restricted and it may not be viable to process the data locally for trajectory planning. Alternatively, the UAV can send the visual inputs to the ground controller which, in turn, feeds back the command and control signals to the UAV for its safe navigation. However, this process may introduce some delays, which is not desirable for autonomous UAVs’ safe and reliable navigation. Thus, it is essential to devise techniques and approaches that can potentially offer low-latency solutions for planning the UAV’s flight. To this end, this paper analyzes a multi-access edge computing aided UAV and aims to minimize the latency of the task processing. More specifically, we propose an offloading strategy for a UAV by optimally designing the offloading parameter, local computational resources, and altitude of the UAV. The numerical and simulation results are presented to offer various design insights, and the benefits of the proposed strategy are also illustrated in contrast to the other baseline approaches.

Ambient backscatter communication (AmBC) technology can potentially offer spectral- and energy-efficient solutions for future wireless systems. This paper proposes a novel design to facilitate the spectrum sharing between a secondary system and a primary system based on the AmBC technique in intelligent reflective surface (IRS)-assisted unmanned aerial vehicle (UAV) networks. In particular, an IRS-aided UAV cooperatively relays the transmission from a terrestrial primary source node to a user equipment on the ground. On the other hand, leveraging on the AmBC technology, a terrestrial secondary node transmits its information to a terrestrial secondary receiver by modulating and backscattering the ambient relayed radio frequency (RF) signals from the UAV-IRS. The performance of such a system setup is analyzed by deriving the expressions of outage probability and ergodic spectral efficiency. Finally, we present the numerical results to provide useful insights into the system design and also validate the derived theoretical results using Monte Carlo simulations.

We consider a multi-group (MG) multicasting (MC) system wherein a multi-antenna transmitter serves heterogeneous users capable of either information decoding (ID) or energy harvesting (EH), or both. In this context, we investigate a precoder design framework to explicitly serve the ID and EH users categorized within certain MC and EH groups. Specifically, the ID users are categorized within multiple MC groups while the EH users are a part of single (last) group. We formulate a problem to optimize the energy efficiency in the considered scenario under a quality-of-service (QoS) constraint. An algorithm based on Dinkelback method, slack-variable replacement, and second-order conic programming (SOCP)/semi-definite relaxation (SDR) is proposed to obtain a suitable solution for the above-mentioned fractional-objective dependent non-convex problem. Simulation results illustrate the benefits of proposed algorithm under several operating conditions and parameter values, while drawing a comparison between the two proposed methods.

The quest for finding a small-sized energy supply to run the small-scale wireless gadgets, with almost an infinite lifetime, has intrigued humankind since past several decades. In this context, the concept of Quantum batteries has come into limelight more recently to serve the purpose. However, the main issue revolving around the closed-system design of Quantum batteries is to ensure a loss-less environment, which is extremely difficult to realize in practice. In this paper, we present the modeling and optimization aspects of a Radio-Frequency (RF) Energy Harvesting (EH) assisted Quantum battery, wherein several EH modules (in the form of micro- or nano- sized integrated circuits (ICs)) help each of the involved Quantum sources achieve the so-called quasi-stable state. Specifically, a micro-controller manages the overall harvested energy from the RF-EH ICs and a photon emitting device, such that the emitted photons are absorbed by the electrons in the Quantum sources. In order to precisely model and optimize the considered framework, we formulate a transmit power minimization problem for an RF-based wireless system to optimize the number of RF-EH ICs under the given EH constraints at the Quantum battery-enabled wireless device. We obtain an analytical solution to the above-mentioned problem using a rational approach, while additionally seeking another solution obtained via a non-linear program solver. The effectiveness of the proposed technique is reported in the form of numerical results by taking a range of system parameters into account.

We propose a novel technique for total transmit power minimization and optimal precoder design in wireless multi-group (MG) multicasting (MC) systems. The considered framework consists of three different systems capable of handling heterogeneous user types viz., information decoding (ID) specific users with conventional receiver architectures, energy harvesting (EH) only users with non-linear EH module, and users with joint ID and EH capabilities having separate units for the two operations, respectively. Each user is categorized under unique group(s), which can be of MC type specifically meant for ID users, and/or an energy group consisting of EH explicit users. The joint ID and EH users are a part of the (last) EH group as well as any one of the MC groups distinctly. In this regard, we formulate an optimization problem to minimize the total transmit power with optimal precoder designs for the three aforementioned scenarios, under constraints on minimum signal-to-interference-plus-noise ratio and harvested energy by the users with respective demands. The problem may be adapted to the well-known semi-definite program, which can be typically solved via relaxation of rank-l constraint. However, the relaxation of this constraint may in some cases lead to performance degradation, which increases with the rank of the solution obtained from the relaxed problem. Hence, we develop a novel technique motivated by the feasible-point pursuit and successive convex approximation method in order to address the rank-related issue. The benefits of the proposed method are illustrated under various operating conditions and parameter values, with comparison between the three above-mentioned scenarios.

In this paper, we investigate the performance of simultaneous wireless information and power transfer (SWIPT) multiuser systems, in which a base station serves a set of users with both information and energy simultaneously via a power splitting (PS) mechanism. To capture realistic scenarios, a nonlinear energy harvesting (EH) model is considered. In particular, we jointly design the PS factors and the beamforming vectors in order to maximize the total harvested energy, subjected to rate requirements and a total transmit power budget. To deal with the inherent non-convexity of the formulated problem, an iterative optimization algorithm is proposed based on the inner approximation method and semide-finite relaxation (SDR), whose convergence is theoretically guaranteed. Numerical results show that the proposed scheme significantly outperforms the baseline max-min based SWIPT multicast and fixed-power PS designs.

We propose a novel formulation for joint maximization of total weighted sum-spectral efficiency and weighted sum-harvested energy to study Simultaneous Wireless Information and Power Transfer (SWIPT) from a pricing perspective. Specifically, we consider that a transmit source communicates with multiple destinations using Orthogonal Frequency Division Multiplexing (OFDM) system within a dual-hop relay-assisted network, where the destination nodes are capable of jointly decoding information and harvesting energy from the same radiofrequency (RF) signal using either the time-switching (TS) or power-splitting (PS) based SWIPT receiver architectures. Computation of the optimal solution for the aforementioned problem is an extremely challenging task as joint optimization of several network resources introduce intractability at high numeric values of relays, destination nodes and OFDM sub-carriers. Therefore, we present a suitable algorithm with sub-optimal results and good performance to compute the performance of joint data processing and harvesting energy under fixed pricing methods by adjusting the respective weight factors, motivated by practical statistics. Furthermore, by exploiting the binary options of the weights, we show that the proposed formulation can be regulated purely as a sum-spectral efficiency maximization or solely as a sum-harvested energy maximization problem. Numerical results illustrate the benefits of the proposed design under several operating conditions and parameter values.

In this paper, we investigate the performance of cache-assisted simultaneous wireless information and power transfer (SWIPT) cooperative systems, in which one source communicates with one destination via the aid of multiple relays. In order to prolong the relays’ serving time, the relays are assumed to be equipped with a cache memory and energy harvesting (EH) capability. Based on the time-splitting mechanism, we analyze the effect of caching on the system performance in terms of the serving throughput and the stored energy at the relay. In particular, two optimization problems are formulated to maximize the relay-destination throughput and the energy stored at the relay subject to some quality-of-service (QoS) constraints, respectively. By using the KKT conditions and with the help of the Lambert function, closed-form solutions are obtained for the two formulated problems. In order to further improve the performance, a relay selection policy is introduced to select the best relay based on either the maximum throughput between the relays’ and destination link or maximum stored energy at the relay, for conveying information to the destination. Numerical results reveal significant benefits of incorporating caching capabilities to SWIPT systems, in terms of improved serving time, throughput, and EH performance at the relays.

We investigate the resource allocation and relay selection in a two-hop relay-assisted multi-user orthogonal frequency division multiple access (OFDMA) network, where the end-nodes support the simultaneous wireless information and power transfer (SWIPT) employing a power splitting (PS) technique. Our goal is to optimize the end-nodes’ PS ratios as well as the relay, carrier, and power assignment so that the sum-rate of the system is maximized subject to harvested energy and transmitted power constraints. Such joint optimization with mixed-integer non-linear programming structure is combinatorial in nature. Due to the complexity of this problem, we propose to solve its dual problem, which guarantees asymptotic optimality and less execution time compared to a highly-complex exhaustive search approach. Furthermore, we also present a heuristic method to solve this problem with lower computational complexity. The simulation results reveal that the proposed algorithms provide significant performance gains compared to a semi-random resource allocation and relay selection approach and is close to the optimal solution when the number of OFDMA sub-carriers is sufficiently large.

In this paper, we investigate resource allocation and relay selection in a dual-hop orthogonal frequency division multiplexing (OFDM)-based multi-user network where amplify-and-forward (AF) enabled relays facilitate simultaneous wireless information and power transfer (SWIPT) to the end- users. In this context, we address an optimization problem to maximize the end-users' sum-rate subjected to transmit power and harvested energy constraints. Furthermore, the problem is formulated for both time-switching (TS) and power- splitting (PS) SWIPT schemes.We aim at optimizing the users' SWIPT splitting factors as well as sub-carrier-destination assignment, sub-carrier pairing, and relay-destination coupling metrics. This kind of joint evaluation is combinatorial in nature with non-linear structure involving mixed-integer programming. In this vein, we propose a sub-optimal low complex sequential resource distribution (SRD) method to solve the aforementioned problem. The performance of the proposed SRD technique is compared with a semi- random resource allocation and relay selection approach. Simulation results reveal the benefits of the proposed design under several parameter values with various operating conditions to illustrate the efficiency of SWIPT schemes for the proposed techniques.

We investigate the performance of time switching (TS) based energy harvesting model for cache-assisted simultaneous wireless transmission of information and energy (Wi-TIE). In the considered system, a relay which is equipped with both caching and energy harvesting capabilities helps a source to convey information to a destination. First, we formulate based on the time-switching architecture an optimization problem to maximize the harvested energy, taking into consideration the cache capability and user quality of service requirement. We then solve the formulated problem to obtain closed-form solutions. Finally, we demonstrate the effectiveness of the proposed system via numerical results.

In this paper, we Investigate the performance of cache enabled millimeter wave (mmWave) ad-hoc network, where randomly distributed nodes are supported by a cache memory. Specifically, we study the optimal caching placement at the desirable mmWave node using a network model that accounts for the uncertainties in node locations and blockages. We then characterize the average success probability of content delivery. As a desirable side effect, certain factors like the density of nodes and increased antenna gain, can significantly increase the cache hit ratio in mmWave networks. However, a trade-off between the cache hit probability and the average successful content delivery probability with respect to the density of nodes is presented.

The narrow-beam operation in millimeter wave (mmWave) networks minimizes the network interference leading to noise-limited networks in contrast with interference-limited ones. The medium access control (MAC) layer throughput and interference management strategies heavily depend on the noise-limited or interference-limited regime. Yet, these regimes are not considered in recent mmWave MAC layer designs, which can potentially have disastrous consequences on the communication performance. In this paper, we investigate the performance of cache-enabled MAC-based mmWave ad hoc networks, where randomly distributed nodes are supported by a cache. The ad hoc nodes are modeled as homogenous Poisson point processes. Specifically, we study the optimal content placement (or caching placement) at desirable mmWave nodes using a network model that accounts for uncertainties both in node locations and blockages. We propose a contention-based multimedia delivery protocol to avoid collisions among the concurrent transmissions. Subsequently, only the node with smallest back-off timer among its contenders is allowed to transmit. We then characterize the average success probability of content delivery. We also characterize the cache hit ratio probability, and transmission probability of this system under essential factors, such as blockages, node density, path loss, and caching parameters.

In this paper, we study a problem of relay selection in a two-hop relaying network where the destination is equipped with Simultaneous Wireless Information and Power Transmission (SWIPT) capabilities. In contrast to conventional cooperative networks, the destination node is considered to be capable of simultaneously decoding information and harvesting energy from both the source and the relay transmissions. In this context, we formulate two optimization problems for both time switching (TS) and power splitting (PS) based SWIPT schemes. The first problem is the maximization of the overall user data rate while ensuring a minimum harvested power. The second problem focuses on the maximization of the overall harvested power at the user under the constraint on the minimum achievable rate. Assuming an amplify-and-forward (AF) relay protocol, closed-form solutions are obtained for the selection of an optimal relay, relay amplification coefficient and the optimal time or power splitting factor. The performance of the proposed relay selection strategies with the aforementioned objectives is evaluated and compared with the case of random relay selection. Furthermore, the Rate-Energy (R-E) tradeoff performance of the scenario with both the direct and indirect relay-assisted links is compared to the case where only a relay-assisted link is available. Our simulation results demonstrate the significant benefits of combining direct and indirect links in SWIPT-enabled cooperative networks in terms of the R-E tradeoff.

In this paper, we consider optimal transceiver design and relay selection for simultaneous transfer of data and energy in a cooperative network consisting of amplify-and-forward (AF) relays. In this context, we address two different problems and propose optimal relay selection and transceiver processing. Furthermore, the problems are formulated for both time switching (TS) and power splitting (PS) schemes. The first problem is the maximization of overall rate while ensuring a minimum harvested energy at the receiver. For this problem, we perform computations of optimal fractions to distribute the received signal corresponding to TS and PS schemes, the relay weighing coefficients, and optimal relay selection. In the second problem, optimal resource allocation for maximizing the overall harvested energy at the user under constraints on the minimum achievable rate has been formulated. For this problem, we propose a suitable solution to meet the requirements for both TS and PS. For both the problems, we obtain a closed form solution. Finally, the performance of proposed design under various operating conditions and parameter values are illustrated with a comparison between TS and PS schemes.