Hybrid Multiple Access Research Articles

Hybrid Multiple Access Techniques Performance Analysis Of Dynamic Resource Allocation

The Non-Orthogonal Multiple Access (NOMA) and the Orthogonal Frequency Division Multiple Access (OFDMA) are promising techniques for next-generation wireless systems. Although much attention in the literature considered these techniques, an effective combination between such systems as hybrid multiple access (HMA) needs more attention. For this reason, this paper devotes to proposing two different scenarios of HMA using multi-user OFDM (MU-OFDM) and power domain (PD) NOMA for downlink transmission to overcome the limitations of both Orthogonal Multiple Access (OMA) and NOMA. Due to the randomness of a wireless channel, different user grouping strategies and dynamic power allocation (DPA) strategies are employed to satisfy users’ requirements. The proposed systems give high flexibility utilizing bitrate allocation and user fairness. System results show superior performance to traditional OMA and NOMA systems. The achieved variety of fairness is helpful for the diversity of applications which is a principal requirement for beyond-fifth-generation (B5G) networks. The next step in this analysis is to enhance the proposed systems’ spectral efficiency (SE) by introducing beamforming for massive Multiple-Input Multiple-Output (MIMO) systems. The bit error rate (BER) result of the proposed system achieves almost the same error floor with a benefit of approximately 10 dB signalto-noise ratio (SNR) when 90 resource blocks (RBs) are used.

Cost-Aware Resource Allocation for Hybrid Multiple Access in Cell-Free MIMO System

Cost-Aware Resource Allocation for Hybrid Multiple Access in Cell-Free MIMO System

Maximizing throughput and energy efficiency in 6G based on phone user clustering enabled UAV assisted downlink hybrid multiple access HetNet

Maximizing throughput and energy efficiency in 6G based on phone user clustering enabled UAV assisted downlink hybrid multiple access HetNet

Machine Learning-Based Reliable Transmission for UAV Networks With Hybrid Multiple Access

Machine Learning-Based Reliable Transmission for UAV Networks With Hybrid Multiple Access

Experimental demonstration of a hybrid OFDMA/NOMA scheme for multi-user underwater wireless optical communication systems

In this work, a hybrid multiple access scheme for multi-user underwater wireless optical communication (UWOC) systems is proposed and presented. The scheme combines the respective advantages of orthogonal frequency-division multiple access (OFDMA) and non-orthogonal multiple access (NOMA) scheme to reduce the unfairness among users caused by the unique high-frequency fading characteristics of the underwater optical channel. Multi-verse optimizer (MVO) algorithm is further introduced for solving the nonlinear problem of user power allocation. Experimental results in different water quality environments demonstrate that the hybrid OFDMA/NOMA scheme with MVO effectively improves the communication performance of the established 4-user UWOC system compared to the pure NOMA scheme and the pure OFDMA scheme, and a system sum rate of 3.94 Gbit/s is achieved.

Satisfying strict deadlines for cellular Internet of Things through hybrid multiple access

AbstractLatency‐constrained aspects of cellular Internet of Things (IoT) applications rely on Ultra‐Reliable and Low Latency Communications (URLLC), which highlight research on satisfying strict deadlines. In this study, we address the problem of latency‐constrained communications with strict deadlines under average power constraint using Hybrid Multiple Access (MA), which consists of both Orthogonal MA (OMA) and power domain Non‐Orthogonal MA (NOMA) as transmission scheme options. We aim to maximize the timely throughput, which represents the average number of successfully transmitted packets before deadline expiration, where expired packets are dropped from the buffer. We use Lyapunov stochastic optimization methods to develop a dynamic power assignment algorithm for minimizing the packet drop rate while satisfying time average power constraints. Moreover, we propose a flexible packet dropping mechanism called Early Packet Dropping (EPD) to detect likely to become expired packets and drop them immediately. Numerical results show that Hybrid MA improves the timely throughput compared to conventional OMA by up to and on average by more than . With EPD, these timely throughput gains improve to and , respectively.

Hybrid Multiple Access for Network Slicing Aware Mobile Edge Computing

Meeting huge traffic demand with resource constraints imposes a significant challenge for future generation wireless networks. In this work, we propose to utilize limited resources in a dense mobile edge computing (MEC) network to compute user equipment (UE) tasks through a novel hybrid multiple access (HYMA) scheme that employs both non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA). The main purpose of using HYMA is to reduce the co-channel interference incurred in NOMA by selectively deploying OMA while maintaining the required signal-to-interference ratio. We adopt partial offloading of computing tasks in the MEC network. We also employ network slicing to efficiently utilize the resources of the MEC network to meet different types of application requirements. We prioritize NOMA to increase spectral efficiency as well as energy efficiency. We first formulate a mixed integer nonlinear programming (MINLP) optimization problem to minimize the total energy consumption for both local computing and wireless transmission of the MEC network and propose an algorithm consisting of three parts (UE association, computing resource allocation, and wireless resource and uplink transmission power allocation) to solve the MINLP problem with less computational complexity. We demonstrate the viability of our solution via extensive simulations.

Hybrid Multiple-Access: Mode Selection, User Pairing and Resource Allocation

Hybrid Multiple-Access: Mode Selection, User Pairing and Resource Allocation

Deep transfer reinforcement learning for resource allocation in hybrid multiple access systems

This paper proposes a resource allocation scheme for hybrid multiple access involving both orthogonal multiple access and non-orthogonal multiple access (NOMA) techniques. The proposed resource allocation scheme employs multi-agent deep reinforcement learning (MA-DRL) to maximize the sum-rate for all users. More specifically, the MA-DRL-based scheme jointly allocates subcarrier and power resources for users by utilizing deep Q networks and multi-agent deep deterministic policy gradient networks. Meanwhile, an adaptive learning determiner mechanism is introduced into our allocation scheme to achieve better sum-rate performance. However, the above deep reinforcement learning adopted by our scheme cannot optimize parameters quickly in the new communication model. In order to better adapt to the new environment and make the resource allocation strategy more robust, we propose a transfer learning scheme based on deep reinforcement learning (T-DRL). The T-DRL-based scheme allows us to transfer the subcarrier allocation network and the power allocation network collectively or independently. Simulation results show that the proposed MA-DRL-based resource allocation scheme can achieve better sum-rate performance. Furthermore, the T-DRL-based scheme can effectively improve the convergence speed of the deep resource allocation network.

A Low-Complexity Location-Based Hybrid Multiple Access and Relay Selection in V2X Multicast Communications

This study investigated relay-assisted mode 1 sidelink (SL) multicast transmission, which encounters interference from mode 2 SL transmission, for use in low-latency vehicle-to-everything communications. To accommodate mode 1–mode 2 SL traffic, we use the hybrid multiple access (MA) approach, which combines orthogonal MA (OMA) and nonorthogonal MA (NOMA) schemes. We introduce a low-complexity location-based hybrid MA algorithm and its associated relay selection that can be used when SL channel state information is unavailable.

Hybrid Multiple Access Transmission in Satellite-Aerial-Terrestrial Networks

This letter proposes a hybrid multiple access (HMA) scheme to provide reliable connectivity for heterogeneous users in a satellite-aerial-terrestrial network (SATN). Here, multiple internet-of-things devices access a multi-antenna aerial platform through space division multiple access (SDMA) technology while the aerial platform and the earth station communicate with the satellite using uplink non-orthogonal multiple access (NOMA). Considering perfect and imperfect successive interference cancellation (SIC) at the satellite, we derive the closed-form ergodic sum rate (ESR) expression for the uplink transmission with the proposed HMA scheme, which is verified through Monte Carlo simulations. Besides, the priority of the proposed scheme is also demonstrated to some benchmarks in terms of ESR.

On the Average Age-of-Information for Hybrid Multiple Access Protocols

We consider age of information of data transmitted over a single-hop wireless network using a hybrid multiple access (MAC) protocol. We characterize the average age of information (AAoI) for this hybrid MAC using analytical upper and lower bounds as well as an approximation for symmetric and asymmetric scenarios. Simulations show that the upper and lower bounds and the approximation provide a good characterization of the AAoI. We compare the AAoI performance of hybrid MACs with EZMAC and a centralized policy. We observe that the hybrid MAC performance is close to that of the centralized policy in the symmetric case.

On the Performance of Laser-Powered UAV-Assisted SWIPT Enabled Multiuser Communication Network With Hybrid NOMA

Owing to the factors such as controllable mobility, ready-to-use technology, low cost, easy implementation, and so on, unmanned aerial vehicle (UAV) possesses tremendous potential to be one of the primary candidates for next-generation (6G) wireless networks. This paper presents a UAV-assisted multiuser communication network where a multiple antenna UAV base station (BS) serves multiple single antenna ground users (GUs). UAV-BS uses a laser source-based charging mechanism to fulfill its power requirement and applies simultaneous wireless information and power transfer (SWIPT) in the downlink in order to provide desired power to energy-constrained GUs. Also, a clustering-based hybrid multiple access technique is used that combines both orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) signaling to transmit the information to all GUs, simultaneously. Due to the involved analytical complexity corresponding to the multiple antennas and users, we use a hybrid beamforming method for efficient communication. Next, we analyze the performance of the proposed framework in terms of user outage probabilities, their respective throughput, and average power harvested considering non-linear energy harvesting and derive expressions of these performance metrics. Moreover, we formulate an optimization problem where the throughput of one GU is maximized by optimally choosing the power allocation parameter while ensuring the desired target throughput at other GU in each cluster. We also illustrate how crucial is the optimal selection of the target rates to maximize the network performance. Simulation results are provided to validate the accuracy of derived expressions and to highlight the dominance of hybrid beamforming and hybrid NOMA compared to conventional methods on the performance of the considered network.

Multiplexing Capacity of Hybrid PD-SCMA Heterogeneous Networks

Hybrid multiple access schemes are considered potential technologies towards achieving optimal spectrum sharing for future heterogeneous networks. Multiple users are multiplexed on a single resource unit in the code domain for sparse code multiple access (SCMA) or power domain for power domain non-orthogonal multiple access (PD-NOMA) and in both domains for the hybrid power domain sparse code non-orthogonal multiple access (PD-SCMA). This allows for effective spectrum usage but comes at a cost of increased detection complexity resulting in loss of performance in terms of outages. It is therefore imperative to determine the user multiplexing capacity for effective performance. This work investigates codebook capacity bounds in small cells, pairing and power capacity bounds for the number of small cell user equipment’s (SUEs) and macro cell user equipment’s (MUEs) that can be multiplexed on a codebook for the developed PD-SCMA technology. Closed-form solutions for codebook, pairing and power multiplexing capacity bounds are derived. The performance of the system results into low outage when the system’s point of operation is within the multiplexing bounds. To alleviate the resource allocation (RA) challenges of such a system at the transmitter, dual parameter ranking (DPR) and alternate search method (ASM) based RA schemes are proposed. The results show significant capacity gain with DPR-RA in comparison with conventional schemes.

Impact of multiple antennas on hybrid multiple access scheme employing NOMA and OMA simultaneously considering user request

Impact of multiple antennas on hybrid multiple access scheme employing NOMA and OMA simultaneously considering user request

Hybrid Space-Frequency Access for Underwater Acoustic Networks

Underwater acoustic networks have recently risen as an effective support to several marine and oceanic applications. However, the potential presence of a large number of nodes simultaneously connected has pushed the scientific community to address the matter of communication resources management, hence to provide the best possible performance for each link. Since underwater acoustic communications (UWACs) suffer from limited bandwidth and long propagation delays, medium access control results really challenging. The best known schemes, that are time division, frequency division and code division multiple access have been considered to handle this problem in underwater scenarios, even though they suffer from some weaknesses. Furthermore, spatial division multiple access achievable in Multiple-Input Multiple-Output (MIMO) systems has emerged as a promising technique able to fit with the multipath propagation typically characterizing UWACs. Dealing with underwater medium access control, we investigate the feasibility of a novel hybrid multiple access technique that works in a bi-dimensional resources domain, namely space and frequency. This solution is aimed to mitigate the multi-user interference by exploiting spatial diversity and, whenever necessary, by applying frequency reuse. Finally, we discuss the feasibility and potential of massive MIMO paradigm, conveniently recast into the underwater context.

Partial Computation Offloading in NOMA-Assisted Mobile-Edge Computing Systems Using Deep Reinforcement Learning

Mobile-edge computing (MEC) and nonorthogonal multiple access (NOMA) have been regarded as promising technologies for beyond fifth-generation (B5G) and sixth-generation (6G) networks. This study aims to reduce the computational overhead (weighted sum of consumed energy and latency) in a NOMA-assisted MEC network by jointly optimizing the computation offloading policy and channel resource allocation under dynamic network environments with time-varying channels. To this end, we propose a deep reinforcement learning algorithm named ACDQN that utilizes the advantages of both actor-critic and deep Q-network methods and provides low complexity. The proposed algorithm considers partial computation offloading, where users can split computation tasks so that some are performed on the local terminal while some are offloaded to the MEC server. It also considers a hybrid multiple access scheme that combines the advantages of NOMA and orthogonal multiple access to serve diverse user requirements. Through extensive simulations, it is shown that the proposed algorithm stably converges to its optimal value, provides approximately 10%, 27%, and 69% lower computational overhead than the prevalent schemes, such as full offloading with NOMA, random offloading with NOMA, and fully local execution, and achieves near-optimal performance.

Coordinated Direct and Relay Transmission for Multiuser Networks: NOMA or Hybrid Multiple Access?

This letter proposes non-orthogonal multiple access (NOMA) based coordinated direct and relay transmission (CDRT) and hybrid multiple access (HMA) protocols for a general CDRT system, where a base station directly serves cell-center users (CCUs), while it communicates with cell-edge users (CEUs) via a relay. The ergodic sum capacity (ESC), capacity scaling and the number of successive interference cancellation (SIC) operations are derived correspondingly. If the number of CCUs is less than that of CEUs and the interference level of imperfect SIC is small, the proposed NOMA-based CDRT can achieve much better ESC than HMA. Otherwise, the proposed HMA achieves a better performance-complexity tradeoff. Numerical results verify the effectiveness and superiority of the proposed protocols.

Hybrid multiple access for channel allocation‐aided eMBB and URLLC slicing in 5G and beyond systems

In this work, we propose a hybrid multiple access (HMA) scheme that combines heterogeneous non‐orthogonal multiple access (H‐NOMA) with heterogeneous orthogonal multiple access (H‐OMA), aiming at increasing the sum‐rate of 5G and beyond (B5G) networks. More specifically, the HMA strategy is applied in the network slicing of enhanced mobile broadband (eMBB) and grant‐free ultra‐reliable low‐latency communications (URLLC), two important use cases in the scope of B5G. By considering that the max‐matching diversity (MMD) algorithm is used to allocate channels to eMBB users, we show that the proposed HMA scheme can achieve considerable gains when compared to the standalone MMD‐aided H‐OMA and H‐NOMA schemes.

Latency Optimization for Computation Offloading With Hybrid NOMA–OMA Transmission

The Internet-of-Things (IoT) platform is faced with critical challenges posed by the conflict between resource-hungry IoT applications and resource-constrained IoT devices. Mobile-edge computing provides a promising solution by allowing IoT devices to offload their computation to nearby edge servers to enable fast and energy-efficient data processing. In this article, we study a scenario, where two IoT users (IoT devices) offload their computation workloads to an edge server with hybrid nonorthogonal multiple access (NOMA)–orthogonal multiple access (OMA) transmission. The hybrid multiple access transmission incorporates three offloading methods, namely, hybrid NOMA, pure NOMA, and pure OMA. The offloading-method selection, together with user selection, which determines the roles played by different IoT users in data transmission, comprises our offloading strategy and is optimized to minimize the maximal offloading latency of the two IoT users. By exploiting the method of successive convex approximation, we design an efficient algorithm to solve the complicated nonconvex problem and rigorously prove the convergence of our algorithm. Extensive numerical tests show that our scheme can always help IoT users to flexibly choose the best offloading strategy. Inspired by experimental observations, we analytically establish the criteria for the three offloading methods. We show that pure OMA transmission is never the best offloading method, except in some extreme cases that rarely occur in practice, while pure NOMA transmission is the most desirable offloading method in terms of latency minimization. We then propose detection approaches for the best offloading strategy with both offloading-method selection and user selection under certain system settings. The user selection is applied to avoid the pure OMA transmission and encourage the pure NOMA transmission.