Non-orthogonal Multiple Access Channels Research Articles

Energy-Efficient Design of STAR-RIS Aided MIMO-NOMA Networks

Simultaneous transmission and reflection-reconfigurable intelligent surface (STAR-RIS) can provide expanded coverage compared with the conventional reflection-only RIS. This paper exploits the energy efficient potential of STAR-RIS in a multiple-input and multiple-output (MIMO) enabled non-orthogonal multiple access (NOMA) system. Specifically, we mainly focus on energy-efficient resource allocation with MIMO technology in the STAR-RIS assisted NOMA network. To maximize the system energy efficiency, we propose an algorithm to optimize the transmit beamforming and the phases of the low-cost passive elements on the STAR-RIS alternatively until the convergence. Specifically, we first decompose the formulated energy efficiency problem into beamforming and phase shift optimization problems. To efficiently address the non-convex beamforming optimization problem, we exploit signal alignment and zero-forcing precoding methods in each user pair to decompose MIMO-NOMA channels into single-antenna NOMA channels. Then, the Dinkelbach approach and dual decomposition are utilized to optimize the beamforming vectors. In order to solve non-convex phase shift optimization problem, we propose a successive convex approximation (SCA) based method to efficiently obtain the optimized phase shift of STAR-RIS. Simulation results demonstrate that the proposed algorithm with NOMA technology can yield superior energy efficiency performance over the orthogonal multiple access (OMA) scheme and the random phase shift scheme.

Enhancing Physical-Layer Security for IoT With Nonorthogonal Multiple Access Assisted Semi-Grant-Free Transmission

Nonorthogonal multiple access (NOMA) assisted semi-grant-free transmission admits grant-free users to access the channels otherwise solely occupied by grant-based users, and has been recently attracting considerable attention in terms of accommodating massive connectivity and reducing access delay in Internet of Things (IoT). In this work, we investigate the security of semi-grant-free NOMA transmission in the presence of passive and active eavesdropping attacks. In particular, for the scenario-I with strong grant-based user and weak grant-free users, the scenario-I-based maximal user scheduling (IbMUS) and scenario-I-based optimal user scheduling (IbOUS) schemes are proposed to combat the passive and active eavesdropping, respectively. For the scenario-II with weak grant-based user and strong grant-free users, two parallel schemes, namely, the scenario-II-based maximal user scheduling (IIbMUS) and scenario-II-based optimal user scheduling (IIbOUS) schemes, are proposed to combat the passive and active eavesdropping, respectively. These proposed schemes enhance the security by scheduling a grant-free user with maximal main channel capacity/maximal secrecy capacity to access the NOMA channel on the premise of ensuring the grant-based user’s Quality of Service. Based on these proposed schemes, the exact secrecy outage probability (SOP) are analyzed to evaluate the system performance. The simulation results validates the theoretic analysis and the superiority of the proposed schemes. The IbOUS and IIbOUS schemes can achieve better performance than the IbMUS and IIbMUS schemes owing to the use of active eavesdropper’s channel state information (CSI). The SOP achieved by the proposed schemes can be further improved with the increasing number of grant-free users and decreasing target rate (or target secrecy rate).

Modified PSO Based Channel Allocation Scheme for Interference Management in 5G Wireless Mesh Networks

Efficient channel management is a challenge that next-generation wireless networks need to meet in order to satisfy increasing bandwidth demand and transmission rate requirements. Non-orthogonal multiple access (NOMA) is one of such efficient channel allocation methods used in 5G backhaul wireless mesh networks. In this paper, we propose a power demand-based channel allocation method for 5G backhaul wireless mesh networks by employing NOMA and considering traffic demands in small cells, thereby improving channel utility. In this scheme, we work with physical layer transmission. The foremost aim is to mutually optimize the uplink/downlink NOMA channel assignment in order to increase user fairness. The approach concerned may be divided into two steps. First, initial channel allocation is performed by employing the traveling salesman problem (TSP), due to its similarity to many-to-many double-side user-channel allocation. Second, the modified particle swarm optimization (PSO) method is applied for allocation updates, by introducing a decreasing coefficient which may have the form of a standard stochastic estimate algorithm. To enhance exploration capacity of modified the PSO, a random velocity is included to optimize the convergence rate and exploration behavior. The performance of the designed scheme is estimated through simulation, taking into account such parameters as through put, spectral efficiency, sum-rate, outage probability, signal to-interference plus noise ratio (SINR), and fairness. The proposed scheme maximizes network capacity and improves fairness between the individual stations. Experimental results show that the proposed technique performs better than existing solutions.

Performance of signal detection with trellis code for downlink non-orthogonal multiple access visible light communication

This paper examines the implementation of multi-level amplitude modulation enforced by trellis coding in a downlink non-orthogonal multiple access channel for visible light communication (VLC). The non-orthogonal transmission is applied using superposition coding and successive interference cancellation with the support of a trellis decoder. The VLC is addressed by a channel model according to Lambert. Two levels of maximum likelihood signal detection are observed and compared, each for corresponding 4 and 8 trellis-coded modulation (TCM). The power ratio allotment is examined to investigate the system performance in the bit error rate. The simulation showed that a threshold of power ratio is needed to ensure good performance of signal detection. Further simulation with successive detection had shown effective results for up to three users.

Secure Precoding in MIMO-NOMA: A Deep Learning Approach

A novel signaling design for secure transmission over two-user multiple-input multiple-output non-orthogonal multiple access channel using deep neural networks (DNNs) is proposed. The goal of the DNN is to form the covariance matrix of users' signals such that the message of each user is transmitted reliably while being confidential from its counterpart. The proposed DNN linearly precodes each user's signal before superimposing them and achieves near-optimal performance with significantly lower run time. Simulation results show that the proposed models reach about 98% of the secrecy capacity rates. The spectral efficiency of the DNN precoder is much higher than that of existing analytical linear precoders--e.g., generalized singular value decomposition--and its on-the-fly complexity is several times less than the existing iterative methods.

Deep Neural Network (DNN) for Efficient User Clustering and Power Allocation in Downlink Non-Orthogonal Multiple Access (NOMA) 5G Networks

Non-orthogonal multiple access (NOMA) emerges as a promising candidate for 5G, which radically alters the way users share the spectrum. In the NOMA system, user clustering (UC) becomes another research issue as grouping the users on different subcarriers with different power levels has a significant impact on spectral utilization. In previous literature, plenty of works have been devoted to solving the UC problem. Recently, the artificial neural network (ANN) has gained considerable attention due to the availability of UC datasets, obtained from the Brute-Force search (BF-S) method. In this paper, deep neural network-based UC (DNN-UC) is employed to effectively characterize the nonlinearity between the cluster formation with channel diversity and transmission powers. Compared to the ANN-UC, the DNN-UC is more competent as UC is a non-convex NP-complete problem, which cannot be entirely captured by the ANN model. In this work, the DNN-UC is first trained with the training samples and then validated with the testing samples to examine its mean square error (MSE) and throughput performance in an asymmetrical fading NOMA channel. Unlike the ANN-UC, the DNN-UC model offers greater room for hyper-parameter optimizations to maximize its learning capability. With the optimized hyper-parameters, the DNN-UC can achieve near-optimal throughput performance, approximately 97% of the throughput of the BF-S method.

Uplink and Downlink MIMO-NOMA With Simultaneous Triangularization

In this paper, we consider the uplink and downlink precoder design for two-user power-domain multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) systems. We propose novel uplink and downlink precoding and decoding schemes that lower the decoding complexity at the receiver by decomposing the MIMO-NOMA channels of the users into multiple single-input single-output (SISO)-NOMA channels via simultaneous triangularization (ST) of the MIMO channels of the users and a low-complexity self-interference cancellation at the receiver. The proposed ST MIMO-NOMA schemes avoid channel inversion at transmitter and receiver and take advantage of the null spaces of the MIMO channels of the users, which is beneficial for the ergodic achievable rate performance. We characterize the maximum ergodic achievable rate regions of the proposed uplink and downlink ST MIMO-NOMA schemes, and compare them with respective upper bounds, baseline MIMO-NOMA precoding schemes, and orthogonal multiple access (OMA). Our results illustrate that the proposed schemes significantly outperform the considered baseline MIMO-NOMA precoding schemes and OMA, and have a small gap to the respective upper bounds for most channel conditions and user rates. Moreover, we show that a hybrid scheme, which performs time sharing between the proposed uplink and downlink ST MIMO-NOMA and single-user MIMO, can improve performance even further.

Energy Efficiency of Uplink and Downlink Non-Orthogonal Multiple-Access Channels Under Gaussian-Mixture Interference

In this paper, we analyze the energy efficiency (EE) of uplink and downlink non-orthogonal multiple access (NOMA) channels with successive interference cancellation under Gaussian-mixture aggregate interference. The adopted nonGaussian model is a realistic noise plus interference model to capture the asynchronism in a heterogeneous cellular network. The considered EE is measured via the minimum energy per bit E <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> /N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">min</sub> for reliable communication and the wideband slope of the spectral efficiency as a function of energy per bit E <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> /N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> . For completeness, both Gaussian signaling schemes and practical finite alphabet inputs are examined. To this end, our approach is to calculate E <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> /N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">min</sub> and the wideband slope region via the Kullback-Leibler divergence. Due to the presence of mixture of multiple Gaussian distributions, the Kullback-Leibler divergence cannot be expressed in closed-form. As an alternative, we exploit upper bounds on the divergence, and we show that the bounds are achievable at the limit points, i.e., when the signal-to-noiseratio (SNR) approaches zero. As a result, E <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> /N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">min</sub> of each user can be established in closed-form. Next, we apply Taylor series expressions and exploit the achievability in the previous step to evaluate the second derivative of the rates when SNR goes to zero. It is then shown that the wideband slope region can be found effectively. The proposed method can serve as an important tool for making more accurate throughput and EE evaluation of important wireless/cellular networks.

Cognitive Multi-Hop Multi-Branch Relaying: Spectrum Leasing and Optimal Power Allocation

In this paper, for an overlay cognitive radio network in which the primary user’s transmission is assisted by a cognitive multi-hop multi-branch network, we propose and analyze a spectrum leasing scheme that achieves full diversity in both the primary and secondary networks. Among the available branches, the best one is selected to establish a multi-hop relay link between the primary users. In return, the secondary users of the selected branch are rewarded with a fraction of spectrum to transmit their own signals over a nonorthogonal multiple-access channel. We jointly optimize the branch selection and the spectrum allocation such that the sum-rate of the secondary users is maximized under the constraint that a minimum quality-of-service (QoS) requirement in terms of the achievable rate is satisfied in the primary network. We also optimize the power allocation to the secondary users such that the asymptotic outage probability of the primary network is minimized. The analytical results show that this spectrum leasing scheme guarantees the full diversity of order $M$ in both the primary and secondary networks, where $M$ is the number of cognitive branches. The simulation results also show that optimizing the allocation of power significantly decreases the risk of spectrum leasing failure.

Delay Guarantee and Effective Capacity of Downlink NOMA Fading Channels

Nonorthogonal multiple access (NOMA) is promising for increasing connectivity and capacity. But there has been little consideration on the quality of service of NOMA; let alone that in generic fading channels. This paper establishes closed-form upper bounds for the delay violation probability of downlink Nakagami- $m$ and Rician NOMA channels, by exploiting stochastic network calculus (SNC). The key challenge addressed is to derive the Mellin transforms of the service processes in the NOMA fading channels. The transforms are proved to be stable, and incorporated into the SNC to provide the closed-form upper bounds of the delay violation probability. The paper also applies the Mellin transforms to develop the closed-form expressions for the effective capacity of the NOMA fading channels, which measures the channel capacity under statistical delay guarantees. By further applying the min–max and max–min rules, two new power allocation algorithms are proposed to optimize the closed-form expressions, which can provide the NOMA users fairness in terms of delay violation probability and effective capacity. Simulation results substantiate the derived upper bounds of the delay violation probabilities, and the effective capacity. The proposed power allocation algorithms are also numerically validated.

Interference-aware resource allocation for multi-tier uplink NOMA under feedback constraints

This paper considers interference-aware optimum transmission power control for the maximization of the aggregate communication rate of a desired tier in a multi-tier network under channel state information feedback constraints. The users of the desired tier form a non-orthogonal multiple access channel with their base station while causing interference to other tiers. In our previous works, it was assumed that the channel state information of the interference channels is perfectly known at the desired tier base station. For real-time systems, this assumption becomes rather unpractical. In order to avoid this, the problem is handled under quantized interference channel power coefficients. The optimum power allocation policy is found by maximizing aggregate data rate of the desired tier users under a total interference power constraint at the first tier and also under interference channel state information feedback constraints. It is shown that the optimum power allocation scheme accepts a binary form, i.e., the users are either “on” or “off” up to at most one user with power in-between.

Joint Noisy Network Coding and Decode-Forward Relaying for Non-Orthogonal Multiple Access

We consider a non-orthogonal multiple access channel in which multiple users communicate with a base-station through a dedicated relay. For this setup, we propose a novel joint-noisy network-coded (J-NNC)-decode-and-forward (DF) cooperation scheme that employs NNC relaying with opportunistic DF. The proposed scheme utilizes the DF cooperation when messages from all users are successfully decoded at the relay and the NNC when the relay is unable to decode any one of the users. In the scenario when the relay decodes only a subset of users, the proposed scheme utilizes joint DF and NNC encoding. We derive the information theoretic conditions required for successful message recovery and show that the J-NNC-DF has a strictly better outage performance than the conventional DF-only, NNC-only, or DF-or-NNC schemes. For the two-user case, we derive closed-form expressions for the outage performance under Rayleigh fading while assuming access only to the local channel state information at the relay. For these expressions, let us choose the NNC quantization parameter so as to attain optimum outage performance. The simulation results validate the theoretical findings and show that the J-NNC-DF achieves a performance gain of 0.75, 1.7, and 2.1 dB compared with the DF-or-NNC, DF-only and NNC-only strategies, respectively.

Joint Power and Modulation Optimization in Two-User Non-Orthogonal Multiple Access Channels: A Minimum Error Probability Approach

In this paper, we consider the joint power and modulation optimization in two-user non-orthogonal multiple access (NOMA) channels based on error-rate performance criterion and finite-alphabet inputs. Specifically, with joint maximum-likelihood detection being employed at each user's receiver, the users’ transmission power levels are jointly determined with the 4-QAM constellation of one of the users being rotated such that the probability of dominant pairwise error event in the NOMA channel is minimized. Toward solving the joint power and modulation problem, our results are twofold. First, with $M$ antennas at the receiver, closed-form expressions for the optimal angles as a function of $M$ , the channel coefficients, and the users’ power levels are derived. Second, closed-form expressions are derived for the joint power allocation with optimal rotation by casting the joint optimization as a piecewise convex optimization problem over its sub-domains. Using the proposed joint optimization, the error-rate performance of NOMA improves significantly and it outperforms traditional NOMA power allocation methods for the selected transmission rates. The proposed approach thus represents a rather distinct approach in NOMA optimization, and it is applicable in the uplink NOMA where users share the same time/frequency channel or in the downlink NOMA where users are multiplexed in the power domain.

Deep Learning for an Effective Nonorthogonal Multiple Access Scheme

Nonorthogonal multiple access (NOMA) has been considered as an essential multiple access technique for enhancing system capacity and spectral efficiency in future communication scenarios. However, the existing NOMA systems have a fundamental limit: high computational complexity and a sharply changing wireless channel make exploiting the characteristics of the channel and deriving the ideal allocation methods very difficult tasks. To break this fundamental limit, in this paper, we propose a novel and effective deep learning (DL)-aided NOMA system, in which several NOMA users with random deployment are served by one base station. Since DL is advantageous in that it allows training the input signals and detecting sharply changing channel conditions, we exploit it to address wireless NOMA channels in an end-to-end manner. Specifically, it is employed in the proposed NOMA system to learn a completely unknown channel environment. A long short-term memory (LSTM) network based on DL is incorporated into a typical NOMA system, enabling the proposed scheme to detect the channel characteristics automatically. In the proposed strategy, the LSTM is first trained by simulated data under different channel conditions via offline learning, and then the corresponding output data can be obtained based on the current input data used during the online learning process. In general, we build, train and test the proposed cooperative framework to realize automatic encoding, decoding and channel detection in an additive white Gaussian noise channel. Furthermore, we regard one conventional user activity and data detection scheme as an unknown nonlinear mapping operation and use LSTM to approximate it to evaluate the data detection capacity of DL based on NOMA. Simulation results demonstrate that the proposed scheme is robust and efficient compared with conventional approaches. In addition, the accuracy of the LSTM-aided NOMA scheme is studied by introducing the well-known tenfold cross-validation procedure.

Fundamental Limits of Low-Density Spreading NOMA With Fading

Spectral efficiency of low-density spreading non-orthogonal multiple access channels in the presence of fading is derived for linear detection with independent decoding and optimum decoding. The large system limit, where both the number of users and number of signal dimensions grow with fixed ratio, called load, is considered. In the case of optimum decoding, it is found that low-density spreading underperforms dense spreading for all loads. Conversely, linear detection is characterized by different behaviors in the underloaded versus overloaded regimes. In particular, it is shown that spectral efficiency changes smoothly as load increases. However, in the overloaded regime, the spectral efficiency of low-density spreading is higher than that of dense spreading.

A Distortion Outer Bound for Communicating Correlated Gaussian Sources Over a Gaussian MAC

In this letter, we consider two problems of sending a bivariate Gaussian source through a two-user Gaussian non-orthogonal multiple-access channel. We provide a new distortion outer bound for the considered joint source-channel coding problems, which significantly reduces the gap between inner and outer bounds of the distortion for correlation coefficients below a threshold. Furthermore, numerical results show, that under some conditions, our proposed outer bound matches with the inner bound obtained via the separate source-channel coding scheme, which proves the optimality of this scheme.

Polar-Coded Non-Orthogonal Multiple Access

Non-orthogonal multiple access (NOMA) is one of the key techniques to address the high spectral efficiency and massive connectivity requirements for the fifth generation (5G) wireless system. To efficiently realize NOMA, we propose a joint design framework combining the polar coding and the NOMA transmission, which deeply mines the generalized polarization effect among the users. In this polar coded NOMA (PC-NOMA) framework, the original NOMA channel is decomposed into multiple bit polarized channels by using a three-stage channel transform, that is, user$\to$signal$\to$bit partitions. Specifically, for the first-stage channel transform, we design two schemes, namely sequential user partition (SUP) and parallel user partition (PUP). For the SUP, a joint successive cancellation detecting and decoding scheme is developed, and a search algorithm is proposed to schedule the NOMA detecting order which improves the system performance by enhanced polarization among the user synthesized channels. The "worst-goes-first" idea is employed in the scheduling strategy, and its theoretic performance is analyzed by using the polarization principle. For the PUP, a corresponding parallel detecting scheme is exploited to reduce the latency. The block error ratio performances over the additive white Gaussian noise channel and the Rayleigh fading channel indicate that the proposed PC-NOMA obviously outperforms the state-of-the-art turbo coded NOMA scheme due to the advantages of joint design between the polar coding and NOMA.

Design of Massive-MIMO-NOMA With Limited Feedback

In this letter, a low-feedback non-orthogonal multiple access (NOMA) scheme using massive multiple-input multiple-output (MIMO) transmission is proposed. In particular, the proposed scheme can decompose a massive-MIMO-NOMA system into multiple separated single-input single-output NOMA channels, and analytical results are developed to evaluate the performance of the proposed scheme for two scenarios, with perfect user ordering and with one-bit feedback, respectively.

Non-Orthogonal Multiple Access in Downlink Coordinated Two-Point Systems

In order to improve the spectral efficiency, non-orthogonal multiple access (NOMA) with superposition coding is employed in a coordinated system where downlink signals to users near to base stations (BSs) and cell-edge users are transmitted simultaneously. To support a cell-edge user in a NOMA channel, two coordinated BSs use Alamouti code. In order to see the performance, we derive expressions for the transmission rates to near and cell-edge users. It is shown that the proposed coordinated superposition coding (CSC) scheme can provide a reasonable transmission rate to a cell-edge user without degrading the rates to users near to BSs.

Maximal Lifetime Power and Rate Allocation for Wireless Sensor Systems With Data Distortion Constraints

We address a lifetime maximization problem for a single-hop wireless sensor system (also known as a Gaussian sensor network) where multiple sensors encode and communicate their measurements of a Gaussian random source to a fusion center (FC). The FC is required to reconstruct the source within a prescribed distortion threshold. The lifetime optimization problem is formulated as a joint power, rate, and timeslot [for time-division multiple access (TDMA)] allocation problem under the constraints of the well-known rate distortion constraints for the Gaussian CEO problem, the capacity constraints of the wireless links, the energy constraints of the sensor nodes and the strict delay constraint within which the encoded sensor data must arrive at the FC. We study the performances of TDMA and an interference limited nonorthogonal multiple access (NOMA) (with single-user decoding)-based protocols and compare them against recently reported simple uncoded amplify and forward schemes under a nonorthogonal multiple access channel with complete phase synchronization. Since computing the exact capacity region for correlated sources in a multiaccess channel is difficult, we simply consider the Gaussian multiaccess capacity constraints pretending that the sensor data are independent (although they are clearly not). We show that the optimal lifetime achieved under these capacity constraints provides an upper bound on the optimal lifetime achieved by the TDMA and NOMA protocols. While the constrained nonlinear optimization problems for the TDMA and the Gaussian multiaccess cases are convex, the NOMA case results in a nonlinear nonconvex difference of convex functions (D.C.) programming problem. We provide a simple successive convex approximation based algorithm for the NOMA case that converges fast to a suboptimal lifetime performance that compares favorably against the upper bound provided by the Gaussian multiaccess case. Extensive numerical studies are presented for both static and slow fading wireless environments with full channel state information at the fusion center.