Uplink Non-orthogonal Multiple Access System Research Articles

Uplink Non-Orthogonal Multiple Access With Golden Codeword Constellation

Non-orthogonal multiple access (NOMA) is a technique to improve spectral efficiency. In uplink NOMA (UL-NOMA) systems, mobile multiusers are globally synchronized to share the same time and frequency resources, and transmit their own independent symbols to the base station (BS). This paper proposes an UL-NOMA system with Golden codeword constellation (GCC). In the proposed UL-NOMA system, two users, the center user and the edge user, transmit their own independent Golden codewords to the BS. Compared to the conventional UL-NOMA systems, the proposed UL-NOMA system not only preserves the spectral efficiency, but also improves error performance. The fast essentially maximum likelihood (FE-ML) detection with dynamic signal detection subset (DSDS) is proposed to decode the Golden codewords. A lower bound on error performance for both the center user and edge user is further derived. Simulation results show that the derived lower bound well predicts the error performance of UL-NOMA with GCC. Simulation results also show that the proposed UL-NOMA system outperforms the conventional UL-NOMA system by at least 2 dB signal-to-noise ratio (SNR) for both the center user and edge user at a bit error rate of $2 \times 10^{-5}$ . Finally, both complexity analysis and simulation results show that the proposed FE-ML with DSDS result in a 68% complexity reduction compared to the FE-ML with SDS at an SNR of 23 dB for the center user transmitting 64QAM and the edge user transmitting 16QAM.

Deep Reinforcement Learning Based Dynamic User Access and Decode Order Selection for Uplink NOMA System With Imperfect SIC

Perfect successive interference cancellation (pSIC) is often assumed in power domain Non-orthogonal multiple access (NOMA) systems. Besides, signals are generally decoded in the order of received signal strength from the strongest to the weakest (DS2W). However, pSIC is impractical in the real NOMA system, and choosing the order of DS2W for the imperfect SIC NOMA systems may lead to decoding failure. In this letter, the imperfect SIC is considered, and a hierarchical deep reinforcement learning-based user access and decoding order selection (HDRUD) algorithm is proposed for the considered uplink NOMA. The simulation shows that the algorithm is significantly better than traditional access and decoding schemes, especially when the SIC is imperfect or the quality of service (QoS) requirements of users are different.

NOMA-based UAV-aided networks for emergency communications

High spectrum efficiency (SE) requirement and massive connections are the main challenges for the fifth generation (5G) and beyond 5G (B5G) wireless networks, especially for the case when Internet of Things (IoT) devices are located in a disaster area. Non-orthogonal multiple access (NOMA)-based unmanned aerial vehicle (UAV)-aided network is emerging as a promising technique to overcome the above challenges. In this paper, an emergency communications framework of NOMA-based UAV-aided networks is established, where the disasters scenarios can be divided into three broad categories that have named emergency areas, wide areas and dense areas. First, a UAV-enabled uplink NOMA system is established to gather information from IoT devices in emergency areas. Then, a joint UAV deployment and resource allocation scheme for a multi-UAV enabled NOMA system is developed to extend the UAV coverage for IoT devices in wide areas. Furthermore, a UAV equipped with an antenna array has been considered to provide wireless service for multiple devices that are densely distributed in disaster areas. Simulation results are provided to validate the effectiveness of the above three schemes. Finally, potential research directions and challenges are also highlighted and discussed.

Outage Performance of CDF-Based Scheduling in Downlink and Uplink NOMA Systems

The scheduling strategy is a vital precondition for achieving remarkable performance benefits offered by non-orthogonal multiple-access (NOMA) schemes. In this article, the cumulative distribution function (CDF)-based scheduling for NOMA (CS-NOMA) networks with randomly deployed users is investigated. We consider two practical successive interference cancellation (SIC) restrictions, namely imperfect SIC and SIC power difference constraint, in both downlink and uplink transmissions. Exact analytical expressions for the outage probabilities of the two scheduled users are derived in both fixed power allocation (FPA) and cognitive-radio-inspired power allocation (CPA) scenarios. To get more insights, high signal to noise ratio (SNR) approximations or bounds of the outage probabilities are derived, and then be utilized to analyze the achieved diversity orders. Assuming the number of near and far users are respectively represented as $K$ and $B$ . Results reveal that, in downlink transmission, the far user can achieve a diversity order of $B$ in both FPA and CPA policies, while the near user's diversity order will be reduced from $K$ (in FPA) to $\text{min}\lbrace K,B\rbrace$ (in CPA). However, in uplink transmission, the achieved diversity orders of the near and far users will be increased from zeros to $K$ and $\text{min}\lbrace K,B\rbrace$ , respectively. Simulation results are provided to validate the accuracy of the analytical expressions.

Power allocation scheme for maximizing spectral efficiency and energy efficiency tradeoff for uplink NOMA systems in B5G/6G

Non-orthogonal multiple access (NOMA) is one of the key potential technologies for beyond 5G, i.e., 6G, networks. However, power allocation for spectral efficiency (SE) and energy efficiency (EE) tradeoff for uplink NOMA systems is still an open problem. In this study, we investigate the power allocation problem to maximize SE–EE tradeoff for systems with multi-cluster multi-user applications. Firstly, the power allocation optimization problem is formulated with the constraints of power budget and the minimum rate required by each user. Then, the optimization problem is decomposed into a group of sub-problems, which aim to maximize the tradeoff of SE–EE for each cluster. Finally, these sub-problems are simplified and solved by using bisection method and monotonicity of function. Simulation results show that the proposed scheme achieves much higher SE–EE tradeoff than the existing schemes.

UAV-Enabled Uplink Non-Orthogonal Multiple Access System: Joint Deployment and Power Control

In order to overcome the inherent latency in multi-user orthogonal multiple access (OMA) unmanned aerial vehicle (UAV) networks. In this paper, we investigate a UAV-enabled uplink non-orthogonal multiple access (NOMA) network, where a UAV is deployed to collect the messages transmitted by the ground users. To maximize the users' sum-rate, we formulate a optimization problem, in which the UAV deployment position and the power control are jointly optimized subject to the transmission power constraints and the quality of service (QoS) constraints. However, this problem is a mixed integer non-convex optimization problem and thus it is difficult to solve directly. Therefore, we first transform the non-convex optimization problem into a convex one based on the first-order approximation technique and the penalty function method. Then, a successive convex approximate (SCA) technique based iterative algorithm as well as its initialization scheme are developed to find a suboptimal solution to the original problem. Afterwards, a low-complexity approximate algorithm is proposed to reduce the high computational complexity of the iterative algorithm. Numerical results show that our proposed iterative algorithm and low-complexity approximate algorithm can achieve a similar performance, and they can efficiently improve the sum-rate compared with OMA scheme and conventional NOMA scheme.

Uplink Non-Orthogonal Multiple Access Over Mixed RF-FSO Systems

In this paper, we consider a relay-assisted uplink non-orthogonal multiple access (NOMA) system. In this system, two radio frequency (RF) users are grouped for simultaneous transmissions, over each resource block, to an intermediate relay. The relay then forwards the amplified version of the users’ aggregated signals, in the presence of multiuser interference, to a relatively far destination. In order to cope with the users’ ever-increasing desire for higher data rates, a high-throughput free-space optics (FSO) link is employed as the relay-destination backhaul link. It is assumed that the FSO backhaul link is subject to Gamma-Gamma turbulence with pointing error. Also, a Rayleigh fading model is considered for the user-relay access links. Under these assumptions, we derive closed-form expressions for the outage probability and tractable forms, involving only one-dimensional integrals, for the ergodic capacity. Moreover, the outage probability and ergodic capacity analysis are extended to the conventional RF-backhauled systems in the presence of multiuser interference to both relay and destination nodes, and Rician fading for the relay-destination RF link. Our results reveal the superiority of FSO backhauling for high-throughput and high-reliability NOMA systems compared to RF backhauling. This work can be considered as a general analysis of dual-hop uplink NOMA systems as well as the first attempt to incorporate power-domain NOMA in mixed RF-FSO systems.

DRL-Based Energy-Efficient Resource Allocation Frameworks for Uplink NOMA Systems

Nonorthogonal multiple access (NOMA) is one of the promising technologies to meet the huge access demand and high data-rate requirements of the next-generation networks. In this article, we investigate the joint subchannel assignment and power allocation problem in an uplink multiuser NOMA system to maximize the energy efficiency (EE). Different from conventional model-based resource allocation methods, we propose three deep-reinforcement-learning (DRL)-based frameworks to solve this nonconvex optimization problem, referred to as the discrete DRL-based resource allocation (DDRA) framework, continuous DRL-based resource allocation (CDRA) framework, and joint DRL and optimization resource allocation (DORA) framework. Specifically, for the DDRA framework, a multi-DQN-based network is designed to dynamically allocate resources discretely, which can reduce the output dimension and improve the learning efficiency. To overcome the loss of power discretization in DDRA, a joint DQN and deep deterministic policy-gradient (DDPG)-based network (CDRA framework) is designed to generate the resource allocation policy. The DORA framework is then proposed as a performance boundary. Finally, an event-triggered learning method is combined with all three frameworks to further reduce the computational consumption. The numerical results show that the proposed frameworks can improve the EE performance of the uplink NOMA system and reduce the computation time.

Effect of Impulsive Noise on Uplink NOMA Systems

Non-orthogonal multiple access (NOMA) was recently proposed as a viable technology that can potentially provide the spectral efficiency, low latency, and massive connectivity requirements of future radio networks. In this context, numerous ultra-high reliability technologies such as the industrial Internet of things, smart grids, and smart homes present environments which are characterized by the presence of impulsive electromagnetic interference, known as impulsive noise. Under such conditions, the power domain multiplexing in NOMA is expected to render the system particularly sensitive to this additional impulsive noise. Therefore, in this article, we quantify the effects of impulsive noise on the outage performance of uplink NOMA systems. Extensive Monte-Carlo simulations as well as offered analytical results demonstrate the vulnerability of the involved NOMA users to this type of noise. This highlights the need for effective modeling of the impulsive noise as well as the design of mitigation techniques that are suitable for the particular demands and challenges of NOMA.

RETRACTED: Energy-aware resource management for uplink non-orthogonal multiple access: Multi-agent deep reinforcement learning

Abstract Non-orthogonal multiple access (NOMA) is one of the promising technologies to meet the huge access demand and the high data rate requirements of the next generation networks. In this paper, we investigate the joint subchannel assignment and power allocation problem in an uplink multi-user NOMA system to maximize the energy efficiency (EE) while ensuring the quality-of-service (QoS) of all users. Different from conventional model-based resource allocation methods, we propose two deep reinforcement learning (DRL) based frameworks to solve this non-convex and dynamic optimization problem, referred to as discrete DRL based resource allocation (DDRA) framework and continuous DRL based resource allocation (CDRA) framework. Specifically, for the DDRA framework, we use a deep Q network (DQN) to output the optimum subchannel assignment policy, and design a distributed and discretized multi-DQN based network to allocate the corresponding transmit power of all users. For the CDRA framework, we design a joint DQN and deep deterministic policy gradient (DDPG) based network to generate the optimal subchannel assignment and power allocation policy. The entire resource allocation policies of these two frameworks are adjusted by updating the weights of their neural networks according to feedback of the system. Numerical results show that the proposed DRL-based resource allocation frameworks can significantly improve the EE of the whole NOMA system compared with other approaches. The proposed DRL based frameworks can provide good performance in various moving speed scenarios through adjusting learning parameters.

Design and Analysis of Probabilistic Shaping Scheme for Uplink Nonorthogonal Multiple Access Systems

Probabilistic shaping (PS) combined with bipolar amplitude shift keying modulation is a promising transmission scheme to increase spectral efficiency. Different from the existing design of the point to point communications, we propose a PS-based uplink nonorthogonal multiple access (NOMA) scheme for fading channels to support multiple connections simultaneously, where successive interference cancellation (SIC)-based maximum a posterior (MAP) detection is implemented at the receiver. To exploit the shaping gain for each user, we first theoretically analyze the ergodic channel capacity and the constellation constrained (CC) capacity with discrete-constellation inputs, respectively. And then, a multi-step optimization scheme for maximizing the ergodic CC capacity is proposed to obtain the optimal probability mass function (PMF) of these input signals. The order of optimization is inverse to the SIC decoding order so that for each specific user, the remaining multiuser interference is calculated based on the optimal PMF of the later decoded users. Furthermore, based on this non-equiprobable transmission scheme, the closed-form pairwise error probability expressions for each user are obtained for the first time. Finally, compared with the uniform scheme, the simulations show that the proposed PS NOMA strategies achieve more than 1 dB gain in terms of the error performance.

Stable Throughput Region and Average Delay Analysis of Uplink NOMA Systems With Unsaturated Traffic

This paper aims at shedding light on the impact of unsaturated traffic on the performance of uplink non-orthogonal multiple access (NOMA) transmissions. Nevertheless, the unsaturated traffic gives rise to the discontinuous interference and the inherent interaction of queues, which in turn highly complicates the performance evaluation. By utilizing tools from queuing theory, we first explicitly characterize the stable throughput region, which represents the region of traffic arrival rates on the condition that the queuing delay converges in distribution to a bounded random variable. In light of this, the critical condition under which NOMA can extend the stable throughput region of orthogonal multiple access (OMA) is derived. Then, we propose an algorithmic solution to evaluate the average delay incurred from both queuing and transmission. It is interestingly found that the superiority of NOMA over OMA in terms of average delay heavily hinges on the temporal traffic dynamics of each user. In particular, NOMA enjoys a clear advantage when the traffic arrival rate of the user with stronger channel condition considerably exceeds the traffic arrival rate of the user with weaker channel condition. The derived results can provide helpful guidance to fully leverage the comparative advantages of NOMA under various traffic conditions.

Non-Orthogonal Multiple Access (NOMA) in Uplink Poisson Cellular Networks With Power Control

This paper develops an analytical framework for multi-cell uplink NOMA systems based on stochastic geometry. We propose two scenarios for the clustering of NOMA UEs and derive the Laplace transform of the inter-cell interference taking into account uplink power control. We utilize two different ordering techniques, namely mean signal power- (MSP-) and instantaneous signal-to-intercell-interference-and-noise-ratio- (ISĨNR-) based, for the successive interference cancellation process at the BSs. For each technique, we present a signal-to-interference-and-noise-ratio (SINR) analysis and derive the transmission success probabilities for the NOMA UEs. We show that uplink power control, which generally reduces the signal power disparity between UEs, does not necessarily degrade the NOMA performance. We discuss how UE clustering and the power control exponent impact this finding. ISĨNR-based ordering, which jointly considers path loss, fading, inter-cell interference, and noise, is generally superior to MSP-based ordering. Moreover, we show that the advantage of NOMA vanishes when the target SINR exceeds a certain threshold. A comparison of the two UE clustering scenarios indicates that excluding the UEs which are relatively far from the serving BS may improve the NOMA performance.

BER Performance of Uplink NOMA With Joint Maximum-Likelihood Detector

We mathematically derive an upper bound of bit-error rate (BER) of uplink non-orthogonal multiple access (NOMA) systems with quadrature phase shift keying (QPSK) modulation in fading channels. In particular, we exploit the joint maximum-likelihood (ML) detector at a base station (BS) with multiple antennas since it results in the optimal BER performance of a super-imposed QPSK modulated symbols from multiple users. In particular, we obtain a closed-form integral of Q-function with Erlang distributed random variable to derive the BER. We also obtain diversity order of the uplink NOMA systems. Through extensive computer simulations, we validate that our analytical results match well with the simulation results especially in high signal-to-noise ratio (SNR) regime.

Random Access Games With Cost of Waiting for Uplink NOMA Systems

In power domain non-orthogonal multiple access (NOMA) used for uplink random access (RA) systems, users need to control transmit power such that the received power at the base station can be one of the predetermined values. Thus, selecting one of the predetermined values incurs different transmission costs. Furthermore, users can often withdraw their (re)transmission, which involves a waiting (or regret) cost. This letter analyzes ${N}$ -user non-cooperative game of uplink NOMA RA systems, where waiting cost is taken into account. As results, we show the condition, under which a unique mixed-strategy Nash equilibrium (MNE) exists and its efficiency with respect to social welfare maximization (SWM).

Robust Joint Optimization of Transmit Power and Decoding Order in Uplink NOMA Systems

We consider a robust design problem for achieving max-min fairness amongst users in an uplink non-orthogonal multiple access system under imperfect channel state information. Contrary to the conventional approach adopted in the literature, we propose an optimal decoding order-based successive interference cancellation technique by introducing new binary variables, which results in a difficult class of mixed-integer nonconvex optimization problem. For a practical application, we devise an efficient suboptimal solution based on the inner convex approximation framework, which solves a second-order-cone program at each iteration. Simulation results are provided to demonstrate its performance gain over state-of-the-art designs. The proposed design also yields data rates close to those obtained by an exhaustive search method.

Uplink Non-Orthogonal Multiple Access with Channel Estimation Errors for Internet of Things Applications.

One of the key requirements for next generation wireless or cellular communication systems is to efficiently support a large number of connections for Internet of Things (IoT) applications, and uplink non-orthogonal multiple access (NOMA) schemes can be used for this purpose. In uplink NOMA systems, pilot symbols, as well as data symbols can be superimposed onto shared resources. The error rate performance can be severely degraded due to channel estimation errors, especially when the number of superimposed packets is large. In this paper, we discuss uplink NOMA schemes with channel estimation errors, assuming that quadrature phase shift keying (QPSK) modulation is used. When pilot signals are superimposed onto the shared resources and a large number of devices perform random accesses concurrently to a single resource of the base station, the channels might not be accurately estimated even in high SNR environments. In this paper, we propose an uplink NOMA scheme, which can alleviate the performance degradation due to channel estimation errors.

Joint User Pairing and Subchannel Allocation for Multisubchannel Multiuser Nonorthogonal Multiple Access Systems

In this paper, we investigate the issues of joint user pairing and subchannel allocation (JUP-SA) in a multisubchannel multiuser nonorthogonal multiple access (NOMA) system. We first investigate the uplink and propose a JUP-SA scheme so that the minimum achievable diversity order of users is maximized. By deriving the upper and lower bounds of the outage probability of the worst-performance user, the achievable diversity order of the scheme is obtained. Following this, the downlink of the system is considered and a JUP-SA scheme that maximizes the minimum diversity order is presented. The closed-form expression of the worst-case outage probability is then derived and validated by simulation results. Numerical results show that both schemes in uplink and downlink NOMA systems can achieve the same diversity order as that of exhaustive search.

GPU Accelerated Successive Interference Cancellation for NOMA Uplink with User Clustering

Non-orthogonal multiple access (NOMA) can achieve high throughput by using the same time and frequency resources for multiple users. NOMA distinguishes multiple users in power domain by computationally-heavy successive interference cancellation (SIC) method. Recently, outsourcing baseband computations to graphics processing units (GPUs) have become an attractive solution for some wireless communication applications, particularly for the ones include parallel processing. Although SIC is a sequential operation, when user clustering is deployed, multiple SIC operations are required and GPU based computation becomes a natural solution to alleviate the high computation demand of SIC receivers. In this work, we implemented GPU based SIC implementation for uplink NOMA systems with user clustering and our results reveal a significant speedup when compared to that of using central processing unit based computations.

Analysis of the Dynamic Ordered Decoding for Uplink NOMA Systems With Imperfect CSI

Instead of successive interference cancellation (SIC) with fixed decoding order, we investigate the dynamic-ordered decoding scheme based on the estimated instantaneous received signal power in case of imperfect channel state information (CSI) for uplink nonorthogonal multiple access systems. The impacts of channel estimation error on both decoding process and order determination are theoretically analyzed in terms of the outage probability for each user. From our analysis, imperfect CSI introduces not only extra interference on the desired signal but also the incorrect decoding order. As shown in the simulations, the dynamic-ordered SIC receiver outperforms the traditional fixed one in case of good quality of estimated CSI. However, the advantage decreases with poor channel estimation quality.