Joint Sensor Clustering and Power Allocation in Wireless Body Area Networks Based on NOMA - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Official websites use .gov A .gov website belongs to an official government organization in the United States. Secure .gov websites use HTTPS A lock ( Lock Locked padlock icon ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites. Search Log in Dashboard Publications Account settings Log out Search… Search NCBI Primary site navigation Search Logged in as: Dashboard Publications Account settings Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide PERMALINK Copy As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice Healthc Technol Lett . 2026 Apr 18;13(1):e70075. doi: 10.1049/htl2.70075 Search in PMC Search in PubMed View in NLM Catalog Add to search Joint Sensor Clustering and Power Allocation in Wireless Body Area Networks Based on NOMA Danhao Deng Danhao Deng 1 Department of Electronic and Communication Engineering, North China Electric Power University, Baoding, China Find articles by Danhao Deng 1, ✉ , Dawei Liu Dawei Liu 1 Department of Electronic and Communication Engineering, North China Electric Power University, Baoding, China Find articles by Dawei Liu 1 , Jiayue Li Jiayue Li 1 Department of Electronic and Communication Engineering, North China Electric Power University, Baoding, China Find articles by Jiayue Li 1 Author information Article notes Copyright and License information 1 Department of Electronic and Communication Engineering, North China Electric Power University, Baoding, China ✉ Corresponding author. Revised 2026 Feb 18; Received 2025 Nov 28; Accepted 2026 Mar 16; Collection date 2026 Jan-Dec. © 2026 The Author(s). Healthcare Technology Letters published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology. This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. PMC Copyright notice PMCID: PMC13091016 PMID: 42006175 ABSTRACT With the transformation of modern medical care towards a prevention‐centred model and the growing demand for high‐density sensor deployments in wireless body area networks (WBANs), traditional orthogonal multiple access (OMA) schemes face critical limitations in spectral efficiency and energy sustainability. Non‐orthogonal multiple access (NOMA) combined with radio frequency energy harvesting (RFEH) emerges as a promising solution, yet existing research often overlooks the strict energy causality constraints of sensors. To address this issue, this paper proposes a novel joint optimization framework for NOMA‐assisted WBANs, which strategically pairs predefined sensor groups (classified by body positions) into NOMA clusters and optimizes power allocation while accounting for harvested RF energy. The proposed sorted‐greedy joint algorithm enhances successive interference cancellation (SIC) efficiency by pairing sensor groups with the largest channel gain differences, followed by a greedy power allocation strategy that maximizes throughput under energy and power constraints. Simulation results demonstrate that the proposed algorithm outperforms conventional greedy and random clustering approaches in total system throughput across varying numbers of sensor groups, sensors per group, and transmission power levels. Keywords: body area networks, body sensor networks, data communication, wireless sensor networks This paper addresses an optimization problem for the NOMA assisted wireless body area networks. A joint sensor clustering and power allocation algorithm is proposed to maximize the system sum throughput. Abbreviations ANA anti‐nuclear antibodies APC antigen‐presenting cells IRF interferon regulatory factor 1. Introduction With the intensification of the aging population trend and the improvement of the whole nation's awareness of health management, modern medical care is undergoing a profound transformation from a “treatment‐centred” model to a “prevention‐centred” one. Against this backdrop, wireless body area networks (WBANs), as a core branch of the Internet of Things (IoT) in the medical field [ 1 ], have emerged as a pivotal technology for modern healthcare [ 2 ]. By deploying a series of miniature, low‐power consumption intelligent sensor nodes inside or on the surface of the human body, WBANs continuously and in real‐time collect key physiological parameters such as electrocardiogram (ECG), electroencephalogram (EEG), blood glucose, blood oxygen, and body temperature [ 3 ]. Data collected via WBANs is wirelessly transmitted to a central coordinator or remote telemedicine centre. The central coordinator or remote telemedicine centre facilitates the execution of remote monitoring, chronic disease management, emergency response, and personalized healthcare for wearers, while it offers revolutionary technical support [ 4 ]. For the aforementioned reasons, WBANs have remained a hot research topic. However, traditional WBANs predominantly employ contention‐based or orthogonal multiple access (OMA) technologies, for example, time‐division multiple access (TDMA), in which sensors communicate in designated, non‐overlapping time slots [ 5 ]. While OMA simplifies receiver design and avoids intra‐network interference, it inherently limits the number of supported devices and constrains the spectral efficiency [ 6 ]. Hence, with the proliferation of high‐data‐rate applications and the increasing number of sensor nodes, for example, continuous glucose monitoring and real‐time electromyography transmission, OMA is unsuitable for next‐generation WBANs. Furthermore, as sensor nodes are typically battery‐powered and are either implanted within or worn on the body, replacing their batteries is highly impractical [ 7 ]. Consequently, achieving high spectral efficiency and ultra‐low power consumption within a massive connectivity framework has become a pivotal challenge hindering the large‐scale application of WBANs [ 8 ]. Energy harvesting (EH) presents a promising solution to the energy limitation problem. Early research explored harvesting from ambient sources like thermal, kinetic, or solar energy [ 9 ]. More recently, radio‐frequency energy harvesting (RFEH), particularly via simultaneous wireless information and power transfer (SWIPT), has gained significant traction for its ability to provide controllable and sustainable power [ 10 ]. Protocols such as power splitting and time switching have been extensively studied for point‐to‐point links [ 11 ]. The integration of EH into cooperative relay networks has further been investigated to combat the severe path loss and shadowing in body area channels. Studies [ 12 ] and [ 13 ] designed routing protocols based on human body posture perception, significantly improving the reliability of data transmission. Study [ 14 ] proposed an adaptive energy‐efficient MAC protocol for RF energy‐harvesting WBANs, and study [ 15 ] further investigated a joint power allocation strategy in classified WBANs to achieve coordinated optimization of wireless information and power transfer. However, these studies overlooked the natural clustering characteristic of sensors in WBANs, which was grouped according to physiological functions or body locations. This characteristic is precisely a critical breakthrough for optimizing user pairing strategies in non‐orthogonal multiple access (NOMA). To address the spectral efficiency bottleneck, NOMA has been introduced as a key enabling technology for 5G and beyond. Specifically, NOMA allows multiple users to share the same time‐frequency resource block via power domain multiplexing or code domain multiplexing [ 16 ]. Its application in conventional cellular and sensor networks has shown substantial gains in connectivity and throughput [ 16 ]. The combination of NOMA with EH, often termed EH‐NOMA, has been explored in general wireless networks. For instance, [ 17 ] investigated the sum‐throughput maximization problem in a downlink EH‐NOMA system. However, the unique architecture and requirements of WBANs necessitate a specialized design for EH‐NOMA schemes. Unlike most sensor networks, WBANs often involve sensors grouped by physiological function or body location, which is rarely leveraged in conventional NOMA user pairing strategies [ 18 ]. Therefore, research on NOMA‐assisted WBANs is still in its infancy stage. Our work investigates a NOMA assisted WBAN architecture where sensors are inherently grouped by their anatomical placement. We propose a novel joint optimization scheme that strategically pairs the predefined sensor groups into NOMA clusters and performs power allocation, explicitly accounting for the energy harvested from RF signals. 2. System Model As shown in Figure 1 , this paper considers a NOMA assisted WBAN system, which consists of one source ( S ) and on‐body sensors ( V ). Acting as the network coordinator or hub, S is typically an intelligent device worn at the waist or carried by the user, such as a smartphone or a dedicated personal base station. S typically possesses dual‐phase capabilities. According to different body parts, we subdivide these sensors into K groups denoted by k ∈ K = { 1 , 2 , … , K } . The k th group of sensors contains N k nodes denoted by V k , n ∈ V k , n = { V k , 1 , … , V k , N k } . FIGURE 1. Open in a new tab The NOMA assisted WBAN system. We assume that each sensor group can harvest energy from S . Each sensor stores the harvested energy in the corresponding battery. We assume all sensor groups are paired into M clusters indexed by m ∈ M = { 1 , 2 , … , M } in a pairwise manner, that is, M = K 2 . The formed sensor clusters then transmit information data to the source via NOMA. In the NOMA transmission, the channel between k th sensor group and S follows the path‐loss model, and can be given by: PL ( d k ) = PL ′ + 10 ϕ log 10 d k d ′ , (1) where ϕ is the path‐loss exponent, d ′ is the reference distance, PL ′ is the path loss at the reference distance d ′ , and d k is the distance between the central of the k th sensor group and S . Then, we obtain the channel gain from the the k th sensor group to S , denoted as g k l s , and the channel gain from S to the sensor, denoted as h k s l , which are given by: h k s l = g k l s = 10 − PL ′ − 10 ϕ log 10 d k d ′ 10 . (2) As shown in Figure 2 , we assume that the total transmission time T is divided into ( 1 + M ) time slots indexed by t i ∈ T = { t 0 , t 1 , … , t M } with equal length T / ( 1 + M ) . The whole communication process is divided into two phases: the wireless energy harvesting (WEH) phase and the wireless information transmission (WIT) phase. In the WEH phase, all sensor groups harvest energy from the source during time slot t 0 . After the energy harvesting is completed, the system enters the WIT phase. In the WIT phase, time slot t m is allocated to the m th sensor cluster, during which the sensor cluster transmits information back to the source. FIGURE 2. Open in a new tab Transmission protocol. 2.1. WEH Phase During the WEH phase, the S simultaneously broadcasts RF signals to all sensors. The received signal at the k th sensor group can be expressed as: y k l = P s h k s l x s + n k , (3) where P s represents the transmit power of the S . x s denotes the transmitted baseband signal of the S with unit power, and n k represents the noise. Therefore, the harvested energy at the sensor V k , n in the k th group can be obtained as: E V k , n l = α t 0 P s | h k s l | 2 = α P s | h k s l | 2 T 1 + M , (4) where α ∈ [ 0 , 1 ] denotes the EH conversion efficiency at each node. 2.2. WIT Phase During the WIT phase, every two sensor groups are paired, and the paired sensor groups employ NOMA technology in the uplink to transmit data to S with different power levels. Based on Equation ( 4 ), the transmit power of at the sensor V k , n in the k th group can be derived as: P V k , n l = E V k , n l t i ρ k l = α P s | h k s l | 2 ρ V k , n l , (5) where ρ V k , n l ∈ [ 0 , 1 ] denotes the power‐splitting ratio of the sensor V k , n in the k th group. Therefore, according to Equation ( 5 ), the transmit power of the k th sensor group can be expressed as P k l = ∑ n = 1 N k α P s | h k s l | 2 ρ V k , n l . (6) Meanwhile, we named the paired sensor groups in one cluster as sensor group A and sensor group B , respectively. We assume g a l s represents the channel gain from sensor group A to S , and g b l s represents the channel gain from sensor group B to S . Thus, the received signal at S can be expressed as: y s = P a l g a l s x a + P b l g b l s x b + n s , (7) where x a denotes the transmitted baseband signal with unit power from the sensor group A and x b denotes the transmitted baseband signal with unit power from the sensor group B . Furthermore, we assume g a l s < g b l s , that is, sensor group A is the weak user and sensor group B is the strong user. According to the SIC strategy, S must first decode the signal of the strong user before decoding the signal of the weak user. For simplicity, we assume perfect SIC. When decoding the signal from sensor group B , the signal from sensor group A is treated as interference. Therefore, the signal‐to‐interference‐plus‐noise ratio (SINR) for sensor group B can be expressed as: γ b l = P b l | g b l s | 2 σ s 2 + P a l | g a l s | 2 = ∑ n = 1 N k α P s | h b s l | 2 ρ V b , n l | g b l s | 2 σ s 2 + ∑ n = 1 N k α P s | h a s l | 2 ρ V a , n l | g a l s | 2 , (8) where σ s 2 is the received noise power at S . From Equation ( 8 ), the throughput from sensor group B to S can be expressed as: R b l = 1 1 + M log 2 1 + γ b l . (9) After successfully decoding the signal from sensor group B , S can subtract it from the total received signal y s : y ∼ s = y s − P b l g b l s x b ≈ P a l g a l s x a + n s , (10) the remaining signal y ∼ s then contains only the signal from sensor group A and noise. Hence, the signal‐to‐noise ratio (SNR) for sensor group A is: γ a l = P a l | g a l s | 2 σ s 2 = ∑ n = 1 N k α P s | h a s l | 2 ρ V a , n l | g a l s | 2 σ s 2 . (11) Consequently, the throughput from sensor group A to S can be expressed as: R a l = 1 1 + M log 2 1 + γ a l . (12) Therefore, the total throughput within one time slot t i during the NOMA transmission can be obtained as: R t i = R a l + R b l . (13) 3. Optimization Strategy In this section, we investigate the sum throughput maximization problem for the NOMA assisted WBAN system under the energy causality constraints described. Specifically, our objective is to jointly optimize the sensor pairing and power allocation. Therefore, the optimization problem can be formulated as: ( P 1 ) : max C , { P k l } k ∈ K ∑ i = 1 M R t i , (14) subject to: C 1 : 0 ≤ ρ k l ≤ 1 , k ∈ K C 2 : ∑ n = 1 N K α P s | h k s l | 2 ρ V k , n l ≤ P k , max l , C 3 : ∑ k = 1 K P k l ≤ P s , (15) where C denotes the sensor clustering variables, C 1 denotes the non‐negativity for the power‐splitting ratios. C 2 is the total power constraint for the k ‐th sensor group, limiting the sum of the transmit powers of all sensors in the k ‐th group to a predefined maximum value P k , max l . C 3 is the total system power constraint, limiting the sum of the transmit powers of all sensors within the source's transmit power P s . Combining with Equation ( 13 ), the total throughput in time slot t i can be derived as: R t i = R a l + R b l = 1 1 + M log 2 1 + γ a l + log 2 1 + γ b l = 1 1 + M log 2 1 + P a l | g a l s | 2 σ s 2 1 + P b l | g b l s | 2 σ s 2 + P a l | g a l s | 2 = 1 1 + M log 2 1 + P a l | g a l s | 2 + P b l | g b l s | 2 σ s 2 . (16) As indicated by Equation ( 16 ), the total throughput is jointly determined by the sensor group pairing strategy and the power allocation scheme. However, the joint optimization of sensor clustering and power allocation presents a non‐convex and computationally challenging problem. To address this issue, we decompose the throughput maximization problem into two subproblems: optimal power allocation and efficient sensor clustering, and we propose an alternating optimization strategy based on a sorted‐greedy joint algorithm. 3.1. Sensor Clustering Strategy In sensor clustering, we design a sorted clustering algorithm. In uplink NOMA systems, the total throughput is primarily determined by the SNR of sensor group and the channel gain disparity among sensors within the same cluster. The latter is governed by the sensor clustering strategy. Generally, a larger channel gain difference between paired sensors leads to more effective SIC. Specifically, when sensors within a cluster exhibit significant differences in channel conditions, the source node receiver can execute a reliable SIC decoding sequence based on distinct received signal power levels. The source first decodes the signal of the sensor with the higher channel gain, where interference from the sensor with the lower channel gain can be approximately neglected due to its relatively weak power, thereby ensuring a high success rate in the first decoding step. Subsequently, the source reconstructs and subtracts the decoded strong sensor's signal from the received composite signal, thereby eliminating the dominant co‐channel interference for the subsequent decoding of the weak sensor's signal. To pair the sensor groups with the largest channel gain difference, we propose a sorted clustering algorithm. Since each sensor group comprises n sensors with independent channel gains, we represent the channel gain of a sensor group by the average channel gain of its n constituent sensors. All sensor groups are initially placed in an unclustered set U = 1 , 2 , … , K , where U denotes the set of indices of unclustered sensor groups. The sensor groups in set U are then sorted in descending order based on their average channel gains. After sorting, the sensor group with the highest channel gain is paired with the sensor group with the lowest channel gain, forming the first cluster C 1 . This process is repeated by then pairing the group with the second‐highest channel gain with the group with the second‐lowest channel gain, and so on, until all sensor groups in set U are allocated into clusters. As shown in Figure 3 . FIGURE 3. Open in a new tab Sensor clustering strategy. 3.2. Power Allocation Strategy Regarding power allocation, for a given clustering scheme C , the power allocation problem is decomposed into M sub‐problems. For each cluster ( a , b ) , we employ a greedy power allocation algorithm. The sum rate of the system in time slot t i is given by: R t i = 1 1 + M log 2 1 + P b l | g b l s | 2 σ s 2 + P a l | g a l s | 2 + log 2 1 + P a l | g a l s | 2 σ s 2 . (17) We define G a = | g a ls | 2 and G b = | g b ls | 2 . When P b l is fixed and P a l is variable, R t i becomes a function of P a l . Differentiating Equation ( 17 ) with respect to P a l yields: ∂ R t i ∂ P a l = 1 ( 1 + M ) G a σ s 2 + P a l G a ln 2 1 − P b l G b P a l G a + σ s 2 + P b l G b . (18) Setting ∂ R t i ∂ P a l = 0 , we have ( 1 − P b l G b P a l G a + σ s 2 + P b l G b ) = P a l G a + σ s 2 P a l G a + σ s 2 + P b l G b = 0 . Since P a l is a non‐negative variable, the function R t i ( P a l ) is monotonically increasing with respect to P a l when P b l is fixed. According to constraint C 3 , the throughput R t i is maximized when P a l = P a , max l . Similarly, when P a l is fixed and P b l is variable, R t i becomes a function of P b l . Differentiating equation ( 17 ) with respect to P b l yields: ∂ R t i ∂ P b l = 1 ( 1 + M ) ln 2 1 P a l G a + σ s 2 + P b l G b . (19) Since 1 P a l G a + σ s 2 + P b l G b > 0 , it follows that the function R t i ( P b l ) is monotonically increasing. According to constraint C 3 , the throughput R t i is maximized when P b l = P b , max l . 4. Simulation Results This section validates the proposed algorithm presented in Section 4 through numerical simulation results. Meanwhile, we investigate the impact of various key system parameters on the performance of the NOMA assisted WBAN system. In the system simulations, the transmission power is configured as P s = 1 mW , and the energy harvesting conversion efficiency is set to α = 0.85 . Considering the low‐power transmission characteristics in human body environments, both the noise power and the system's maximum power are set to very small values. As shown in Figure 1 , since the sensors are placed on the human torso forming a planar configuration, we establish a Cartesian coordinate system with the source as the origin and calculate the distance between the sensors and the source based on their positions. The parameter values for the path loss, reference distance, and path loss exponent in the WBAN path loss model are provided in Table 1 [ 19 ]. TABLE 1. Simulation parameters of WBANs. Parameter Hand Head Torso Leg PL ′ 32.2 41.2 35.7 38.5 d ′ 10 10 10 10 ϕ 3.35 3.23 3.38 3.42 Open in a new tab Figure 4 shows the impact of different sensor groups on the total system throughput. We assume each sensor group contains N K = 2 nodes. The total system throughput is influenced by varying the number of sensor groups K . We compare the random‐greedy joint algorithm, the greedy–greedy joint algorithm, and the proposed sorting‐greedy algorithm. It can be observed that as the number of sensor groups K increases from 4 to 8, the total system throughput of all three algorithms decreases. The primary reason for this phenomenon is that, under the condition of a fixed system transmission power P s , the increase in the number of sensor groups K leads to a reduction in the energy harvested by each group. According to Shannon's formula, the channel capacity of a sensor is proportional to the sensor's transmission power. Consequently, as the number of sensor groups K increases, the total system throughput decreases. Furthermore, it can be observed that the total system throughput of the proposed sorting‐greedy algorithm is significantly higher than that of the other two algorithms. In the greedy algorithm for sensor clustering, each selection aims to maximize the sum rate in the current time slot. However, this locally optimal selection leads to a sharp decline in the pairing quality of subsequent remaining groups, ultimately resulting in a lower global total throughput. FIGURE 4. Open in a new tab Total system throughput under different sensor groups. Figure 5 illustrates the impact of the number of sensors per group on the total system throughput. We set the number of sensor groups to K = 6 and vary the number of sensors per group N K to observe its influence on the total system throughput. It can be observed that as N K increases from 2 to 6, the total system throughput of all three joint algorithms increases accordingly. This behaviour can be attributed to the fact that when the number of sensors within each group increases, each sensor harvests energy from the source node via wireless energy transfer. The total energy available per group is the sum of the energy collected by all sensors within the group. In NOMA, the maximum transmission power of a sensor group is determined by the total harvested energy. A higher total energy results in a higher maximum transmission power for the group, thereby increasing the actual transmission power allocated to both the weak and strong sensors. The increase in transmission power directly improves the SINR of the weak sensor and enhances the SNR of the strong sensor. Ultimately, this leads to an improvement in the per‐cluster sum rate according to Shannon's formula. Since the rate of each cluster increases with the number of sensors per group, the total system throughput naturally rises accordingly. FIGURE 5. Open in a new tab Total system throughput under different sensors per group. Figure 6 illustrates the impact of different transmission power levels on the total system throughput. The number of sensor groups is set to K = 6 and the number of sensors per group N K = 4 . It is evident that as the transmission power P s increases from 1 to 2 mW, the total system throughput of all three algorithms increases accordingly. Among them, the sorting‐greedy joint algorithm significantly outperforms the other two algorithms. FIGURE 6. Open in a new tab Total system throughput under different power. 5. Conclusion In this paper, we investigated the sum throughput maximization problem for a NOMA assisted WBAN system. A novel joint optimization framework was proposed, which pairs predefined sensor groups into NOMA clusters and performs power allocation under strict energy causality constraints derived from harvested RF energy. The proposed sorted‐greedy joint algorithm effectively addresses the non‐convex challenge of jointly optimizing sensor clustering and power allocation by first pairing sensor groups with the largest channel gain differences to enhance SIC efficiency, followed by a greedy power allocation strategy that maximizes the sum throughput under energy and power constraints. Simulation results demonstrate that the proposed algorithm significantly outperforms conventional greedy and random clustering approaches in terms of total system throughput, under varying numbers of sensor groups, sensors per group, and transmission power levels. Author Contributions Danhao Deng : funding acquisition, writing – review and editing, supervision. Dawei Liu : software, data collation, validation, formal analysis, investigation, writing – original draft, visualisation. Jiayue Li : writing – review and editing. Funding This work was supported by the Fundamental Research Funds for the Central Universities, China (No. 2024MS115). Conflicts of Interest The authors declare no conflicts of interest. Data Availability Statement The data that support the findings of this study are available from the corresponding author upon reasonable request. References 1. Olatinwo D. D., Abu‐Mahfouz A. M., Hancke G. P., and Myburgh H. C., “Energy Efficient Priority‐Based Hybrid MAC Protocol for IOT‐Enabled WBAN Systems,” IEEE Sensors Journal 23, no. 12 (2023): 13524–13538. [ Google Scholar ] 2. Yuan X., Tian H., Wang H., Su H., Liu J., and Taherkordi A., “Edge‐Enabled WBANs for Efficient QoS Provisioning Healthcare Monitoring: A Two‐Stage Potential Game‐Based Computation Offloading Strategy,” IEEE Access 8 (2020): 92718–92730. [ Google Scholar ] 3. Qi W. and Su H., “A Cybertwin Based Multimodal Network for ECG Patterns Monitoring Using Deep Learning,” IEEE Transactions on Industrial Informatics 18, no. 10 (2022): 6663–6670. [ Google Scholar ] 4. Ding X., Clifton D., Ji N., et al., “Wearable Sensing and Telehealth Technology With Potential Applications in the Coronavirus Pandemic,” IEEE Reviews in Biomedical Engineering 14 (2021): 48–70. [ DOI ] [ PubMed ] [ Google Scholar ] 5. Afridi A., Hameed I., García C. E., and Koo I., “Throughput Maximization of Wireless Powered IoT Network With Hybrid NOMA‐TDMA Scheme: A Genetic Algorithm Approach,” IEEE Access 12 (2024): 65241–65253. [ Google Scholar ] 6. Mandawaria V., Sharma E., and Budhiraja R., “Spectral Efficiency for Massive MIMO Multi‐Relay NOMA Systems With CSI Errors,” in 2020 28th European Signal Processing Conference (EUSIPCO) (IEEE, 2021), 1648–1652. [ Google Scholar ] 7. Hu J., Xu G., Hu L., Li S., and Xing Y., “An Adaptive Energy Efficient MAC Protocol for RF Energy Harvesting WBANs,” IEEE Transactions on Communications 71, no. 1 (2023): 473–484. [ Google Scholar ] 8. Samanta A. and Nguyen T. G., “Quality‐Driven Energy‐Efficient Big Data Aggregation in WBANs,” IEEE Sensors Letters 6, no. 8 (2022): 1–4. [ Google Scholar ] 9. Harini M., Venkateshwaran E., Bharath Balaji S., Srikamu C., and Jayabharathy R., “Performance Analysis of NOMA Over OMA System for B5G Applications,” in 2024 Global Conference on Communications and Information Technologies (GCCIT) (IEEE, 2024), 1–6. [ Google Scholar ] 10. Ramalingam L., Mariappan S., Parameswaran P., et al., “The Advancement of Radio Frequency Energy Harvesters (RFEHs) as a Revolutionary Approach for Solving Energy Crisis in Wireless Communication Devices: A Review,” IEEE Access 9 (2021): 106107–106139. [ Google Scholar ] 11. Wu S., Guo C., Deng Z., Jiao J., Zhang N., and Zhang Q., “Optimizing Age of Information in Adaptive NOMA/OMA/Cooperative‐SWIPT‐NOMA System,” IEEE Transactions on Wireless Communications 21, no. 12 (2022): 11125–11138. [ Google Scholar ] 12. Choi S., Baek H., and Lim J., “Comparison of User Pairing Methods for NOMA in Beamforming Training Within 802.11ad WLANs,” in 2024 15th International Conference on Information and Communication Technology Convergence (ICTC) (IEEE, 2024), 1873–1874. [ Google Scholar ] 13. Nooh H., Won S., Ng S. X., Sohail M. F., Kim M., and El‐Hajjar M., “Optimal User Pairing Strategy for Minimum Power Utilization in Downlink Non‐Orthogonal Multiple Access Systems,” IEEE Open Journal of the Communications Society 5 (2024): 4125–4137. [ Google Scholar ] 14. Guo W., Li X., Gan Y., and Lu T., “A MAC Protocol Based on Coefficient of Variation Method in Energy Harvesting Wireless Body Area Network,” in 2023 8th International Conference on Computer and Communication Systems (ICCCS) (IEEE, 2023), 172–180. [ Google Scholar ] 15. Li S., Yang H.‐C., Xu F., Hu H., and Hu F., “Energy‐Efficient Relay Transmission for WBAN: Energy Consumption Minimizing Design With Hybrid Supervised/Reinforcement Learning,” IEEE Internet of Things Journal 11, no. 10 (2024): 17770–17779. [ Google Scholar ] 16. Chamkhia H., Erbad A., Al‐Ali A., Mohamed A., Refaey A., and Guizani M., “PLS Performance Analysis of a Hybrid NOMA‐OMA Based IoT System With Mobile Sensors,” in 2022 IEEE Wireless Communications and Networking Conference (WCNC) (IEEE, 2022), 1419–1424. [ Google Scholar ] 17. Himanshi and Nandal V., “A Comparative Study of OMA and NOMA in 5G Networks, Analyzing Performance and Optimizing Wireless Network Capacity Using NOMA,” in 2023 First International Conference on Advances in Electrical, Electronics and Computational Intelligence (ICAEECI) (IEEE, 2023), 1–6. [ Google Scholar ] 18. Askari Z., Abouei J., Jaseemuddin M., and Anpalagan A., “Energy‐Efficient and Real‐Time NOMA Scheduling in IOMT‐Based Three‐Tier WBANs,” IEEE Internet of Things Journal 8, no. 18 (2021): 13975–13990. [ Google Scholar ] 19. Li S., Hu F., Xu Z., Mao Z., Ling Z., and Liu H., “Joint Power Allocation in Classified WBANs With Wireless Information and Power Transfer,” IEEE Internet of Things Journal 8, no. 2 (2021): 989–1000. [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Data Availability Statement The data that support the findings of this study are available from the corresponding author upon reasonable request. Articles from Healthcare Technology Letters are provided here courtesy of Wiley ACTIONS View on publisher site PDF (1.8 MB) Cite Collections Permalink PERMALINK Copy RESOURCES Similar articles Cited by other articles Links to NCBI Databases Cite Copy Download .nbib .nbib Format: AMA APA MLA NLM Add to Collections Create a new collection Add to an existing collection Name your collection * Choose a collection Unable to load your collection due to an error Please try again Add Cancel Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top