Multiple Access Wireless Networks Research Articles

Power allocation for enhancing energy efficiency in unmanned aerial vehicle networks

SummaryThe non‐orthogonal multiple access (NOMA) wireless networks are a robust multi‐access mechanism that serves multiple clients accessing the same resource block simultaneously. The fifth‐generation wireless networks offer huge efficiency in spectrum utilization, which can be exploited to deploy NOMA for LOS communication of unmanned aerial vehicles (UAVs). Previous research has extensively analyzed various aspects of NOMA‐UAV communication systems, including user access fairness, coverage, maximizing system capacity, and total energy efficiency. However, only a few researchers have focused on maximizing the EE of NOMA‐UAV wireless networks with user quality of service (QoS) constraints. This paper proposes a fuzzy logic‐based crossover‐based coati optimization algorithm for maximizing the energy efficiency of NOMA‐UAV, along with user scheduling. The main objective of this model is to offer a solution to the joint energy efficiency and user QoS scheduling problem. The fuzzy decision‐making strategy optimizes the energy efficiency (EE) of NOMA‐UAV by selecting appropriate power and time resources. Additionally, the crossover‐based coati optimization algorithm transforms the subchannel allocation problem into a two‐sided matching procedure. The efficiency of the proposed algorithm is evaluated concerning overall residual energy, number of remaining nodes, and time consumption. The experimental outcomes demonstrate the capability of the proposed model in optimizing the energy efficiency of the NOMA‐UAV network by identifying the optimal resource set in terms of time and power while satisfying the clients' QoS.

Online Multi-Agent Reinforcement Learning for Multiple Access in Wireless Networks

Online Multi-Agent Reinforcement Learning for Multiple Access in Wireless Networks

Simultaneous Utilization of Multiple Radio Access Networks in Ubiquitous 6G Connectivity for Autonomous Ships: Opportunities and Challenges

The growing significance of ubiquitous 6G connectivity within the maritime sector is a consequence of its evolution into an era characterized by the adoption of autonomous ships. This evolution necessitates the development of adaptable communication capabilities, even in the face of increasing heterogeneity in Radio Access Networks (RANs). This heterogeneity is a consequence of the extended lifespans of maritime communication technologies used by both legacy and emerging ships at sea, in contrast to the generational shift seen in terrestrial communication technologies. This paper undertakes a comprehensive examination and provides an insightful overview of communication technologies within the framework of 3rd Generation Partnership Project (3GPP) standards with the aim of preparing for the forthcoming 6G standardization to enable ubiquitous 6G connectivity in the maritime domain. The primary focus of this paper is the mobile RAN entities (e.g., satellites and uncrewed aerial vehicles (UAVs)) positioned for integration into the 5G and beyond systems. These entities are distinguished by their differences from conventional terrestrial RAN entities, which are typically stationary on land. This integration enables User Equipment (UE) to connect to various RAN entities, including mobile RANs, interconnected with core networks, thereby granting UE secure access to external internets through 5G and beyond systems, enabling them to enjoy a diverse range of application services, even in areas beyond terrestrial coverage, such as at sea. This paper further conducts an in-depth analysis of a transport layer-level solution known as the Access Traffic Steering, Switching, and Splitting (ATSSS) feature enabling a concurrent connection to multiple RANs for data-traffic delivery. Furthermore, this paper explores opportunities and challenges for future research in the realm of forthcoming 6G standardization within 3GPP, especially when combined with the ATSSS features for the success of autonomous ships within the maritime sector. These considerations encompass the concept of autonomous ships as mobile RAN entities, the integration of legacy maritime communications into the 6G framework, and the variability in maritime channel measurements, generally employed as one of the criteria for selecting an appropriate RAN among multiple options, influenced by uncontrollable factors such as climate change.

Optimization of STAR-RIS-Assisted Hybrid NOMA mmWave Communication

Simultaneously reflecting and transmitting reconfigurable intelligent surfaces (STAR-RIS) has recently emerged out as prominent technology that exploits the transmissive property of RIS to mitigate the half-space coverage limitation of conventional RIS operating on millimeter-wave (mmWave). In this paper, we study a downlink STAR-RIS-based multi-user multiple-input single-output (MU-MISO) mmWave hybrid non-orthogonal multiple access (H-NOMA) wireless network, where a sum-rate maximization problem has been formulated. The design of active and passive beamforming vectors, time and power allocation for H-NOMA is a highly coupled non-convex problem. To handle the problem, we propose an optimization framework based on alternating optimization (AO) that iteratively solves active and passive beamforming sub-problems. Channel correlations and channel strength-based techniques have been proposed for a specific case of two-user optimal clustering and decoding order assignment, respectively, for which analytical solutions to joint power and time allocation for H-NOMA have also been derived. Simulation results show that: 1) the proposed framework leveraging H-NOMA outperforms conventional OMA and NOMA to maximize the achievable sum-rate; 2) using the proposed framework, the supported number of clusters for the given design constraints can be increased considerably; 3) through STAR-RIS, the number of elements can be significantly reduced as compared to conventional RIS to ensure a similar quality-of-service (QoS).

Markov chain modelling of ordered Rayleigh fading channels in non‐orthogonal multiple access wireless networks

AbstractA first‐order finite‐state Markov chain (FSMC) typically models the Rayleigh fading channel in the open literature because the first‐order FSMC is analytically tractable and can derive closed‐form results. Non‐orthogonal multiple access (NOMA) has been recognised as a novel wireless technology that addresses challenges in the next generation of mobile communications. According to the power‐domain NOMA protocol, channels in the NOMA wireless network are sorted by the channel gain. Then considering NOMA, there is insufficient information on how to further form a suitable model for ordered Rayleigh fading channels based on the first‐order FSMC. Given the mathematical statement on how to model the order statistics of multidimensional Markov chains for ordered Rayleigh fading channels, the authors consider these order statistics as a Markov chain, and propose specific processes of representing the state space and constructing the transition probability matrix accordingly. Numerical and simulation results validate the mathematical correctness and accuracy of these novel processes. In addition, for ordered Rayleigh fading channels, the performances of various methods of partitioning the entire signal‐to‐noise ratio range are compared. The performance comparison results are the same as those obtained for the individual unordered Rayleigh fading channel.

Design and Experiment of Satellite-Terrestrial Integrated Gateway with Dynamic Traffic Steering Capabilities for Maritime Communication.

This study presents the architectural design and implementation of a multi-RAT gateway (MRGW) supporting dual satellite and terrestrial connectivity that enables moving maritime vessels, such as autonomous surface ships, to be connected to multiple radio access networks in the maritime communication environment. We developed an MRGW combining LTE and very-small-aperture terminal (VSAT) access networks to realize access traffic steering, switching, and splitting functionalities between them. In addition, we developed communication interfaces between the MRGW and end-devices connecting to their corresponding radio access networks, as well as between the MRGW and the digital bridge system of an autonomous surface ship, enabling the MRGW to collect wireless channel information from each RAT end-device and provide the collected data to the digital bridge system to determine the optimal navigation route for the autonomous surface ship. Experiments on the MRGW with LTE and VSAT end-devices are conducted at sea near Ulsan city and the Kumsan satellite service center in Korea. Through validation experiments on a real maritime communication testbed, we demonstrate the feasibility of future maritime communication technologies capable of providing the minimum performance necessary for autonomous surface ships or digitized aids to navigation (A to N) systems.

Multi-Objective Energy Efficient Resource Allocation in Massive Multiple Input Multiple Output-Aided Heterogeneous Cloud Radio Access Networks

In this work, a novel energy efficient multi-objective resource allocation algorithm for heterogeneous cloud radio access networks (H-CRANs) is proposed where the trade-off between increasing throughput and decreasing operation cost is considered. H-CRANs serve groups of users through femto-cell access points (FAPs) and remote radio heads (RRHs) equipped with massive multiple input multiple output (MIMO) connected to the base-band unit (BBU) pool via front-haul links with limited capacity. We formulate an energy-efficient multi-objective optimization (MOO) problem with a novel utility function. Our proposed utility function simultaneously improves two conflicting goals as total system throughput and operation cost. With this MOO, we jointly assign the sub-carrier, transmit power, access point (AP)(RRH/FAP), RRH, front-haul link, and BBU. To address the conflicting objectives, we convert the MOO problem into a single-object optimization problem using an elastic-constraint scalarization method. With this approach, we flexibly adjust trade-off parameters to choose between two objective functions. To propose an efficient algorithm, we deploy successive convex approximation (SCA) and complementary geometric programming (CGP) approaches. Finally, via simulation results we discuss how to select the values of trade-off parameters, and we study their effects on conflicting objective functions (i.e., throughput and operation cost in MOO problem). Simulation results also show that our proposed approach can offload traffic from C-RANs to FAPs with low transmit power and thereby reduce operation costs by switching off the under-utilized RRHs and BBUs. It can be observed from the simulation results that the proposed approach outperforms the traditional approach in which each user is associated to the AP (RRHs/FAPs) with the largest average value of signal strength. The proposed approach reduces operation costs by 30% and increases throughput index by 25% which in turn leads to greater energy efficiency (EE).

A New Look at AI-Driven NOMA-F-RANs: Features Extraction, Cooperative Caching, and Cache-Aided Computing

Non-orthogonal multiple access-enabled fog radio access networks (NOMA-F-RANs) are thought of as a promising enabler to release network congestion, reduce delivery latency, and improve fog user equipment (F-UEs) quality of service. Nevertheless, the effectiveness of NOMA-F-RANs highly relies on the charted feature information (e.g., preference distribution, positions, and mobilities) of F-UEs as well as the effective caching, computing, and resource allocation strategies. In this article, we explore how artificial intelligence (AI) techniques are utilized to solve foregoing challenges. Specifically, we first elaborate on the NOMA-F-RANs architecture, shedding light on the key modules, namely, cooperative caching and cache-aided mobile edge computing. Then, the potentially applicable AI-driven techniques in solving the principal issues of NOMA-F-RANs are reviewed. Through case studies, we show the efficacy of AI-enabled methods in terms of the latent feature extraction and cooperative caching of F-UEs. Finally, future trends of AI-driven NOMA-F-RANs, including open research issues and challenges, are identified.

Performance analysis of overlay cognitive NOMA network with imperfect SIC and imperfect CSI

In this paper, we investigate a multiple users cooperative overlay cognitive radio non-orthogonal multiple access (CR-NOMA) network in the presence of imperfect successive interference cancellation (SIC) and imperfect channel state information (CSI). In the context of cellular network, cell-center cognitive secondary users act as relays to assist transmission from the primary user (PU) transmitter to the cell-edge PU receiver via NOMA. According to the received signals between the primary transmitter and multiple cognitive secondary center users, the best cell-center cognitive SU with the maximum signal to noise ratio (SNR) is selected to transmit the PU’s signals and its own signal to cell-edge users through NOMA principle. Then, the PU cell-edge user combine the signals received from direct transmission in the first phase and relay transmission from the best cell-center cognitive SU in the second phase by selection combining (SC). To measure the performance of the system quantitatively, we derive the end-to-end outage probability and capacity for the primary and secondary networks by taking the imperfect SIC and CSI into consideration. Finally, the performance analysis is validated by the simulations, and show that serious interference caused by imperfect SIC and (or) imperfect CSI reduce the system performance.

Service-Aware User Association and Resource Allocation in Integrated Terrestrial and Non-Terrestrial Networks: A Genetic Algorithm Approach

In 6G networks and beyond, multiple radio access networks (RANs), including; the satellite, high altitude platforms, low altitude platforms, and the terrestrial network, will co-exist. These networks are characterized by different capabilities and limitations in meeting the envisioned 6G contrasting user requirements. Therefore, associating users with the appropriate radio access network (RAN) in such an integrated network is rigorous and complex. In this work, the user association and resource allocation problem is formulated as a multi-objective optimization problem (MOOP), aiming to maximize data rate while minimizing mobility-induced handoff in the integrated network. Moreover, the problem is formulated in such a way as to prioritize the service provisioning of mission-critical users. The weighted sum method is adopted to simplify and transform the MOOP into a single-objective optimization problem (SOOP). In order to solve the formulated NP-hard SOOP, a genetic algorithm (GA) whose fitness value is based on the user’s service group is proposed. The performance of the proposed algorithm is evaluated by comparing it to the optimal solution, the greedy signal-to-interference-plus-noise ratio (SINR) based association, and the random user association algorithms. Simulation results show that as the number of access nodes in the network increases, the GA’s spectrum efficiency (SE) remains within 0.4% of the optimal solution. Moreover, the GA outperforms all three schemes in user acceptance ratio (AR) and handoff probability.

A Downlink RIS-Aided NOMA System With Hardware Impairments: Performance Characterization and Analysis

Reconfigurable intelligent surface (RIS) has recently been envisioned to provide an adaptable channel for futuristic wireless communication networks. This work characterized the impact of hardware impairments in the downlink RIS-aided non-orthogonal multiple access (NOMA) wireless networks. We considered the hardware impairments in the transceivers of the base station (BS) and users, phase error at the RIS and the imperfect successive interference cancellation (SIC) for the NOMA users. Specifically, the performance is evaluated by deriving the closed-form expressions for the outage probability (OP), spectral efficiency (SE) and energy efficiency (EE). Additionally, the performance of the orthogonal multiple access (OMA) system is also evaluated and compared with its NOMA counterpart. The RIS-aided NOMA outperforms OMA for the ideal hardware scenario. However, the hardware impairments significantly deteriorate RIS-aided NOMA's performance compared to RIS-aided OMA. Further, we have also compared the proposed RIS-aided NOMA with the baseline full-duplex relaying (FDR) based NOMA. The results show that the gains are more significant for a RIS-aided system than FDR.

Forward-Reverse Orthogonal Matching Pursuit-Union-Subspace Pursuit-Based Multiuser Detector for Uplink Grant-Free NOMA Networks

Multiuser Detection (MUD) is quite challenging in uplink grant-free non-orthogonal multiple access wireless communication networks in which users sporadically transmit data. The reason for this is that the base station (BS) must perform detection of both multiuser activity and user signals concurrently, because knowledge of user activity status is not available at the BS. In this paper, a new multiuser detector, named the Forward-Reverse Orthogonal Matching Pursuit–Union–Subspace pursuit (FROMPUS)-based MUD, is proposed. The detector takes advantage of the concept of an initial support set. This serves as initial knowledge that is then employed in the reconstruction of active users’ signals. In addition, the detector uses the “serial-include” technique of incorporating a likely support set element candidates and a reliability testing procedure in which the most prominent elements of the support set are selected. To assess the performance of the proposed detector, computer simulations are performed. The results obtained for various parameter settings show that the FROMPUS performs better than any of the other five detectors considered in this paper. However, this excellent performance comes with a slightly higher computational complexity cost. Nonetheless, the cost is inconsequential, since the detector operates at the BS where complexity is of low priority in comparison to performance.

A Miniaturized Planar Monopole Antenna Based on a Coupling Structure for Compact Mobile Internet of Things (IoT) and Electric Vehicles (EVs) Device Applications in 5G, LTE, WLAN, WiMAX, Sirius/XM Radio, V2X, and DSRC Wireless Systems

A miniaturized internal antenna with monopole structure is constituted, including three radiating strips of a compact prototype and a back-coupling pad to improve the impedance matching, which achieves a wide bandwidth of 2.972 GHz between the operating frequencies of 2315–5285 MHz, and is introduced and researched. There is an urgent need for a complete frequency-continuous and large bandwidth design in the current wireless communication design to achieve a multimode, multifrequency, physical phenomenon design with large bandwidth and continuous operating frequency. The recommended antenna provides a broadband operation in an electric vehicles (EVs) and Internet of Things (IoT) devices application embedded in the wireless communication standard for 5G, LTE, V2X, WLAN, WiMAX, Sirius/XM Radio, V2X, and DSRC to support the multiband application. This design is embedded side edge of overall placement in the device and is integrated applicable to the trend of heterogeneous wireless multiple access networks in electric vehicle and Internet of Things system devices, which covered the 5G with supporting the band of n7/n38/n40/n53/n77/n78/n79/n90, the 4G with supporting the band of 7/38/40/41/42/43/48/67, the V2X and DSRC for the operating frequencies between 2500 and 5000 MHz, the Sirius/XM Radio for the operating frequencies of 2320–2345 MHz, the ISM band of WiFi and BT covering the band of 2450–2483.5 and 5150–5350 MHz, and the WiMAX also supporting the band of 2300–2690 and 3400–3690 MHz. Moreover, the compact antenna manufactured an FR4 material with the antenna area of 5 × 10 × 0.8 mm3 and the ground area of 33.5 × 10 × 0.8 mm3. The proposed design better benefits a narrow space on the PCB with a low profile and is easy to make with a circuit board design.

Artificial Intelligence-Based Network Selection and Optimized Routing in Internet of Vehicles

Abstract Internet of Vehicles (IoV) is a network of vehicles communicating with each other by exchanging road traffic information via radio access technologies. Two potential technologies of V2X that have gained attention over the past years are DSRC and cellular networks such as 4G LTE and 5G. DSRC is suitable for low latency communications, however provides a shorter coverage range whereas, 4G LTE offers a wide coverage range but has high transmission time intervals. In contrast, 5G offers higher data rates, low latencies but prone to blockages. Single technology might not fully accommodate the requirements of vehicular communications. Hence, it is required to interwork with more than one radio access network to satisfy the requirements of safety vehicular applications. One issue identified when working with multiple radio access networks is the selection of the most appropriate network for vertical handover. Usually, in the previous works, the network is selected directly or will be connected to the available network due to which the handover had to take place frequently resulting in unnecessary handovers. Hence, in the existing state-of-the-art, the need for handover is not validated. In this paper, we have proposed a dynamic Q-learning algorithm to validate the need for handover, and then, appropriate selection of network would take place by using a fuzzy convolutional neural network. Besides, a modified jellyfish optimization algorithm is proposed to select the shortest paths by forming V2V pairs that take into account channel metrics, vehicle metrics, and vehicle performance metrics. The proposed algorithms are then evaluated using OMNET++ and compared with the existing state-of-the-art concerning mean handover, HO failure, throughput, delay, and packet loss as the performance metrics.

Energy-Efficient Parallel Multi-Access Edge Computing in OFDMA Wireless Networks

We study a parallel multi-access edge computing (MEC) system in this work and investigate its energy-efficient resources management. Particularly, the MEC system consists of an edge node and a number of mobile users, where each user has an inelastic computing task with nonnegligible duration of task execution and nonnegligible size of task result. In addition, the MEC system operates in orthogonal frequency division multiple access (OFDMA) wireless networks and can support the transmission as well as the execution of different tasks in parallel. On this basis, we build a nonconvex mixed-integer nonlinear programming (MINLP) problem aiming to minimize the energy consumed by the system. By using the McCormick method and exploring the problem's structural properties, we first equivalently transform it to a manageable convex MINLP problem, and then devise two low-complexity suboptimal algorithms. Finally, numerical results are presented to show the effectiveness of our proposed algorithms.

A Modular Web-Based Software Solution for Mobile Networks Planning, Operation and Optimization

Mobile networks management is increasingly critical due to heavy communications usage by customers and complex due to the multiple technologies and systems deployed. Thus, Mobile Network Operators (MNOs) are constantly looking for better software solutions and tools to help them increase network performance and manage their networks more efficiently. In this paper, we present a modular web-based software solution to tackle problems related to mobile network planning, operation and optimization. The solution is focused on a set of functional requirements carefully chosen to support the network life cycle management, from planning to Operation and Maintenance (OAM) and optimisation stages. Based on a 3-tier modular architecture and implemented using only open-source software, the solution handles multiple data sources (e.g., Drive Test (DT) and Performance Management (PM)) and multiple Radio Access Network (RAN) technologies. MNOs can explore all available data through a flexible and user-friendly web interface, that also includes map-based visualization of the network. Moreover, the solution incorporates a set of recently developed and validated RAN algorithms, supporting tasks of network diagnosis, optimization, and planning. Also, with the purpose of optimizing the network, MNOs can investigate network simulations, using the RAN algorithms, of how the network will behave under certain conditions, and visualize the outcome of those simulations.

On the Performance of Downlink Non-Orthogonal Multiple Access Wireless Networks With Directional Beamforming and Limit of the User Number

Non-orthogonal multiple access (NOMA) is an enabling and key wireless technology for the next generation of mobile communications. Generally, previous works focus on the performance in NOMA networks, based on some assumptions. For instance, the number of users in a NOMA group, is two-category: 1) only two users; 2) any number of users without restriction. And just a certain factor of channel gain, e.g., the propagation path loss or the small scale fading, is subjectively considered to be dominant. Besides, there is lack of the closed-form expression for the probability density function (PDF) of ordered channel gain. In this paper, we investigate a downlink cellular NOMA network with the directional beamforming and the upper limit for user number (ULFUN) in a NOMA group. Meanwhile, the path loss and the Rayleigh fading are taken into account as a whole for ordering channel gains. Based on the derived closed-form PDF of ordered channel gain, we analyze the performance for an arbitrary user in the network and derive closed-form expressions for the outage probability, the approximate meta distribution of conditional success probability (CSP) and the ergodic data rate. The provided simulations demonstrate that the proposed analytical results are accurate and reasonable. Moreover, it illustrates that the existence of ULFUN can have a positive impact on the performance gain in low SINR environments, while in terms of the outage probability, ULFUN would become a non-negligible and negative factor in the improvement of performance for high SINR environments.

Intelligent Network Slicing With Edge Computing for Internet of Vehicles

In this paper, we present an application-specific Multi-Access Edge Computing (MEC) network architecture by leveraging the Control and User Plane Separation (CUPS) in mobile core networks to offload data processing from central servers to edge servers to reduce the transmitted traffic volume and also the response latency of connected vehicle mobility service. We first apply deep learning to classify packets of different applications to different Radio Access Networks (RAN) slices for application-specific spectrum scheduling. Then, we slice Evolved Packet Core (EPC) and deploy EPC data plane slices on-demand for each application and route packets from RAN slices to edge servers. By applying network slicing, multiple RAN, EPC and MEC slices that support different categories of services with different quality of service (QoS) requirements can be deployed in the same physical infrastructure. We prototype the proposed application-specific CUPS architecture using modified open source software OpenAirInterface on our deeply programmable platform. The preliminary experimental results show the feasibility and efficiency of proposed application-specific CUPS architecture, which can achieve a significant decrease in transmission data volume and latency.

Analysis of backlog and delay in downlink power-domain non-orthogonal multiple access wireless networks

As important quality-of-service (QoS) performance indicators, backlog and delay in wireless networks must be in compliance with the requirement of low-latency and high-reliability scenarios. Non-orthogonal multiple access (NOMA) is a novel and key wireless technology for the next generation of mobile communications. Its potential impacts on backlog and delay deserve to be specially studied. Meanwhile, the stochastic network calculus is an effective tool for analyzing network performances such as backlog and delay. In this paper, for both orthogonal multiple access (OMA) scheme and NOMA scheme, we utilize the stochastic network calculus to characterize respectively cumulative service processes of channels in a downlink network. Based on moment generating functions, the closed-form expressions of the upper bounds on backlog and delay are derived through the definite integral and special function. Simulation results validate reasonableness and effectiveness of the proposed approach. We show that in terms of backlog and delay, performances cannot be always improved by substituting NOMA scheme for OMA scheme, except when the channel gain gap between users is significantly big. Moreover, the sensitivity to the same factor such as arrival rate, transmission power and power allocation, differs from user to user in NOMA. By simulations, it is further demonstrated that the performance loss due to a finite buffer in practical scenarios is acceptable and sometimes negligible, as long as the buffer size is set to be just moderate.

Multipath MMT-based approach for streaming high quality video over multiple wireless access networks

Demand for video streaming content and multimedia services have increased dramatically. Delivering high throughput and low delay video data are real challenges to provide and sustain high quality mobile video streaming services. One type of solution to provide quality of experience is to exploit multipath strategies by taking advantage of the multiple network interfaces that are currently available in most wireless devices. Our work aims at delivering contributions by exploring the advantages of multipath video streaming using the MPEG Media Transport protocol. MPEG Media Transport is a multimedia application layer protocol with the capability of hybrid media delivery. In this article, we propose a Content-Aware and Path-Aware (CAPA) scheduling strategy for this protocol, considering both video features and path condition. The performance of our CAPA proposal is evaluated using ns-3 DCE with different realistic multipath network scenarios. We evaluate the performance of CAPA over heterogeneous wireless networks under congested network and wireless lossy network conditions, which are common network situations with significant adverse effects on video quality. Our approach yields considerable video quality improvement in both scenarios compared to Path-Aware strategy and a simple scheduling strategy, called Evenly Splitting. For congested network scenario, CAPA could increase PSNR, respectively, by up to 4.25 dB (12.97%), and 7.22 dB (20.58%) compared to Path-Aware and Evenly Splitting strategies. It could also improve SSIM, respectively, by up to 0.033 (3.78%), and 0.102 (12.54%) compared to Path-Aware and Evenly Splitting strategies. For wireless lossy network scenario, CAPA increases PSNR, respectively, by up to 6.84 dB (20.30%), and 9.43 dB (30.32%) compared to Path-Aware and Evenly Splitting strategies. The proposed strategy also provides improvements in terms of SSIM, by up to 0.100 (12.72%), and 0.113 (14.23%) compared to Path-Aware and Evenly Splitting strategies, respectively. We also evaluate how proposed CAPA handles the streaming of different video bitrates under congested network conditions to provide sufficient video quality. Finally, we provide an initial validation of fairness of CAPA to confirm fair access to the resources available across all paths.