Exploring Active Inference for Efficient Resource Allocation in AAV-Enabled Cognitive NOMA Uplink

A non-orthogonal multiple access (NOMA)-based spatial modulation system operating over multiple subchannels is investigated. For scheduled users of each subchannel, a mixed multicast and unicast transmission is delivered. The multicast content is transmitted via the transmit antenna domain, while unicast contents are transmitted through the amplitude-phase modulated symbols using NOMA via the active antenna. Firstly, the unicast rate for each user and an upper bound for the multicast rate are derived. Secondly, a joint subchannel and power allocation problem for weighted sum rate maximization is formulated. To solve this challenging mixed-integer non-linear problem, we decompose it into three subproblems, namely the decoding order design, the subchannel assignment, and the power allocation. A heuristic scheme is developed to solve the first one by investigating the characteristics of the decoding order constraint. To avoid the high complexity caused by exhaustive search, the subchannel assignment is reformulated as a many-to-one matching with peer effect, and the Gale-Shapley method and swap operation are designed to solve it. The power allocation is solved by employing the successive convex approximation. Moreover, a joint subchannel and power allocation algorithm is proposed to further boost the performance, and a robust power allocation algorithm is proposed under channel uncertainties.

In this paper, user association and power allocation are investigated in a non-orthogonal multiple access (NOMA)-based multi-cell network. In order to perform successive interference cancellation (SIC) techniques for removing the intra-base station (BS) interference, the optimal decoding order is derived for all users associated with the same BS. In an effort to improve the system, a sum rate maximization problem is formulated by jointly designing user association and power allocation. Two game theory based algorithms are proposed to obtain the stable user structure by dividing users into different BSs' clusters, where the sub-optimal and global optimal solutions can be achieved. The properties of the proposed algorithms, including complexity, convergence, stability and optimality, are analyzed. Based on the quality-of-service (QoS) constraint, the closed-from solutions for power allocation are derived, and thus the expressions for the sum rate of all users in each cluster is obtained. Moreover, the case that the QoS threshold cannot be achieved by all users in each cluster is considered. Simulation results demonstrate that: i) the proposed user association algorithms and the closed-form solutions for power allocation can significantly enhance the sum rate and outage probability; and ii) the proposed NOMA-based system is capable of achieving promising gains over the conventional orthogonal multiple access (OMA)-based framework in the multi-cell scenario.

As a technology that can accommodate more users and significantly improve spectral efficiency, non-orthogonal multiple access (NOMA) has attracted the attention of many scholars in recent years. The basic idea of NOMA is to implement multiple access in the power domain and decode the desired signal via successive interference cancellation (SIC). However, the resource allocation problem in such NOMA system is non-convex. It is difficult to directly solve this optimization problem through conventional methods. As such, we propose to apply a reinforcement learning (RL) approach based on cooperative Q-learning to solve the resource allocation problem in multi-antenna downlink NOMA systems. First, we formulate the resource allocation process as a sum rate maximization problem, subject to the power budget constraints and quality of service (QoS) condition. Second, we design a reward function to improve the sum rate while meeting the power and capacity constraints. Multiple Q-tables are created and cooperatively updated to get the optimal beamforming matrix. Then, we analyze the convergence of our proposed RL based power allocation method. Our simulations show that the proposed power allocation scheme yields excellent performance in terms of sum rate, energy efficiency, and spectral efficiency.

In this paper, we consider the problem of sum rate maximization in a bidirectional relay network with fading. Hereby, user 1 and user 2 communicate with each other only through a relay, i.e., a direct link between user 1 and user 2 is not present. In this network, there exist six possible transmission modes: four point-to-point modes (user 1-to-relay, user 2-to-relay, relay-to-user 1, relay-to-user 2), a multiple access mode (both users to the relay), and a broadcast mode (the relay to both users). Most existing protocols assume a fixed schedule of using a subset of the aforementioned transmission modes, as a result, the sum rate is limited by the capacity of the weakest link associated with the relay in each time slot. Motivated by this limitation, we develop a protocol which is not restricted to adhere to a predefined schedule for using the transmission modes. Therefore, all transmission modes of the bidirectional relay network can be used adaptively based on the instantaneous channel state information (CSI) of the involved links. To this end, the relay has to be equipped with two buffers for the storage of the information received from users 1 and 2, respectively. For the considered network, given a total average power budget for all nodes, we jointly optimize the transmission mode selection and power allocation based on the instantaneous CSI in each time slot for sum rate maximization. Simulation results show that the proposed protocol outperforms existing protocols for all signal-to-noise ratios (SNRs). Specifically, we obtain a considerable gain at low SNRs due to the adaptive power allocation and at high SNRs due to the adaptive mode selection.

This letter studies the resource allocation problem of sum rate maximization in wireless powered communication networks (WPCNs) with non-orthogonal multiple access (NOMA) that satisfy minimum data rate requirements per user. Firstly, we derive closed-form solutions for the time-splitting ratio and power allocation coefficients that maximize the approximated sum rate NOMA expression. Then, we use the solution of the approximated problem as the starting point for solving the original problem through Newton’s method. Simulation results show the effectiveness of the proposed approximation as well as the fast convergence of the optimal solution.

This letter investigates a power allocation problem in a downlink single-input single-output non-orthogonal multiple access (NOMA) system. Our goal is to maximize the sum rate of users subject to minimum user rate requirements. We rigorously prove the optimal user decoding order, and show that the sum rate maximization problem is convex, which guarantees the globally optimal solution. Numerical results validate the performance gain by the proposed NOMA compared with conventional schemes.

Unmanned aerial vehicle (UAV) network based on non-orthogonal multiple access (NOMA) jointly optimizes the location and power allocation (PA) of UAV to achieve better sum rate. However, this non-convex sum rate maximization problem is difficult to solve. Therefore, we decompose this non-convex problem into two sub-problems. First, genetic algorithm (GA) is used to solve the position optimization problem, which is equivalent to sum rate maximization problem. Then, the closed-form PA algorithm is used to further maximize the sum rate under a fixed UAV’s location obtained by GA. Compared with PA algorithm with geometric center placement, numerical results demonstrate the effectiveness of the proposed algorithm.

Non-orthogonal multiple access (NOMA) can improve the spectrum efficiency and enable massive connectivity in future wireless communications systems by multiplexing multiple users in a non-orthogonal manner. Many previous works in power-domain NOMA addressed research problems from the perspective of the channel capacity, assumed perfect successive interference cancellation (SIC) and considered the pairing of users with very distinct channel conditions. This can yield inefficient power allocations in terms of sum-rate. Further, the assumption of perfect SIC is not realistic in practical systems, where SIC error propagation greatly impacts the achievable bit error rate (BER) at the receivers. By applying NOMA to multicarrier-based schemes, the capabilities of both can be enhanced through resource allocation, i.e. the assignment of radio resources to users under an optimization objective. However, resource allocation in multicarrier NOMA systems may lead to a nondeterministic polynomial time (NP)-hard problem requiring exhaustive search, which has prohibitive computational complexity. Instead, efficient algorithms that provide a good trade-off between system performance and implementation practicality are needed.The contributions presented in this thesis are two-fold. First, new performance bounds on the BER of NOMA systems are provided. And second, a novel resource allocation scheme is presented, which can achieve a performance close to optimal with low computational complexity. The contributions are summarized as follows.First, theoretical BER expressions are presented for multi-layer, multi-level quadrature amplitude modulation (QAM) in NOMA. To the best of the author's knowledge, this work represents the first attempt in developing such expressions. The optimal value of the power allocation factor in terms of BER is analytically derived. Further, the theoretical BER expressions are used for calculating the ratios of users' channel gains that maximize the sum-rate. Unlike previous research in NOMA, it is demonstrated that, in NOMA systems with QAM, the channel gains of two NOMA users must be of approximately the same order of magnitude in order to guarantee that inter-user interference can be overcome at the receivers. Additionally, accurate BER approximations are presented in the form of exponential functions. These are used for finding numerical boundaries for the values of the channel gain ratios of NOMA users that fulfill the BER constraints.Second, the contributions on BER boundaries are applied to develop of a novel resource allocation scheme for multicarrier NOMA. A user pairing algorithm of quasi-linear complexity with respect to the number of users is proposed, based on the findings about NOMA optimal channel gain ratios and channel gain gaps. In contrast, the complexity of exhaustive search is of the order of the squared number of users. The problem of power and data rate allocation is solved by applying a Lagrangian optimization method based on the previously derived BER exponential approximations. The optimization result is applied to propose a novel iterative resource allocation (IRA)-data rate selection (DRS) algorithm. Unlike existing works, continuous power levels and discrete modulation schemes are considered. Numerical simulations demonstrate that IRA-DRS yields a sum-rate performance close to optimal, providing an excellent trade-off between computational complexity and performance. IRA-DRS benefits from multi-user diversity in terms of achievable sum-rate, number of iterations required for convergence, and degrees of freedom in choosing different combinations of modulation levels.

Non-orthogonal multiple access (NOMA) has been seen as a promising technology for 5G communication. The performance optimization of NOMA systems depends on both power allocation (PA) and user pairing (UP). Most existing researches provide sub-optimal solutions with high computational complexity for PA problem and mainly focuses on maximizing the sum rate (capacity) without considering the fairness performance. Also, the joint optimization of PA and UP needs an exhaustive search. The main contribution of this paper is the proposing of a novel capacity maximization-based fair power allocation (CMFPA) with low-complexity in downlink NOMA. Extensive investigation and analysis of the joint impact of signal to noise ratio (SNR) per subcarrier and the channel gains of the paired users on the performance of NOMA in terms of the capacity and the user fairness is presented. Next, a closed-form equation for the power allocation coefficient of CMFPA as a function of SNR, and the channel gains of the paired users is provided. In addition, to jointly optimize UP and PA in NOMA systems an efficient low-complexity UP (ELCUP) method is proposed to be incorporated with the proposed CMFPA to compromise the proposed joint resource allocation (JRA). Simulation results demonstrate that the proposed CMFPA can improve the capacity and fairness performance of existing UP methods, such as conventional UP, and random UP methods. Furthermore, the simulation results show that the proposed JRA significantly outperforms the existing schemes and gives a near-optimal performance.

A graph-theoretic framework is proposed to efficiently solve the sum rate maximization problems for non-orthogonal multiple-access (NOMA) aided distributed millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. We reveal that this sum rate maximization problem can be decoupled into two sub-problems, namely a user-access point (AP) association/clustering sub-problem and a pilot resource allocation sub-problem. This decoupling reduces the computational complexity compared to the optimal solution, which can only be attained via an exhaustive search having a factorial-time complexity. In the first sub-problem, the APs optimally select a set of users possessing the highest average channel power gains. Thereby, the analog precoders are designed at each AP by exploiting the angular information of the selected set of users. In the second sub-problem, a set of limited orthogonal pilots is optimally reused/assigned among concurrently served users such that the pilot contamination is minimized. We propose a graph-theoretic solution to find practically-viable and computationally-efficient solutions to both these sub-problems having an overall polynomial-time complexity. We model the joint user-AP association and pilot resource allocation sub-problems as a bipartite graph matching problem and a vertex coloring problem, respectively. Thereby, we propose an algorithm to compute the minimum number of orthogonal pilots required for a given user-AP association/clustering. If the size of available pilot set is larger than the minimum required number of pilots, then it is always possible to assign pilots such that no two users, which are associated with the same AP, share the same pilot. Otherwise, the users, which have been assigned to same beam, are allowed share the pilots. By leveraging the benefits of graph-theoretic approach, we propose a pragmatic solution to the coexistence of NOMA and orthogonal multiple-access schemes to boost the achievable sum rate of distributed mmWave massive MIMO NOMA systems.

Non-orthogonal multiple access (NOMA) is a promising technique for the fifth generation mobile communication due to its high spectrum efficiency. By applying superposition coding and successive interference cancellation techniques, multiple users can be multiplexed on the same subchannel in NOMA systems. Previous works focus on subchannel and power allocation to maximize the sum rate; however, the energy-efficient resource allocation problem has not been studied for NOMA systems. In this paper, we aim to optimize subchannel assignment and power allocation to maximize the energy efficiency for the downlink NOMA network. Assuming perfect knowledge of the channel state information at base station, we propose low-complexity suboptimal algorithms which include subchannel assignment and power allocation for subchannel users. In the power allocation scheme, difference of convex functions programming approach is exploited to transform and approximate the original optimal problem into a convex optimization problem. Simulation results show that our proposed algorithms yield much better improvements than orthogonal frequency division multiple in terms of sum rate and energy efficiency.

Both reconfigurable intelligent surface (RIS) and non-orthogonal multiple access (NOMA) are treated as promising technologies for future wireless communications. However, the performance gain achieved by the traditional passive RIS-aided NOMA system is limited due to the double-fading effect for the base station (BS)-RIS-user channel. In this letter, a sum rate maximization problem for active RIS-aided uplink multi-antenna NOMA system is studied, in which the reflecting elements (REs) of active RIS can manipulate the phase shifts and amplify the amplitudes of incident signals. To tackle the formulated sum rate maximization problem, the original optimization problem is decomposed into three subproblems, i.e., the equalizer optimization, the power allocation, and the active RIS beamforming design. Simulation results show that the proposed active RIS-aided NOMA system can effectively improve sum rate compared with several benchmark schemes.

This paper studies the non-orthogonal multiple access (NOMA)-based spatial modulation (SM) systems with multiple subchannels. A mixed multicast and unicast transmission is considered in each channel, in which a common content is multicasted in the transmit antenna (TA) domain to all the users, and the unicast contents are transmitted as amplitude- phase modulated (APM) symbols in the classical signal domain using NOMA via the active antenna. First, we obtain the achievable unicast rate for each user and an upper bound for the achievable multicast rate in the TA domain. Then, the subchannel assignment and power allocation schemes are designed to maximize the system sum rate. Specifically, the subchannel assignment is formulated as a many-to-one matching with peer effect, and we propose a suboptimal but efficient algorithm incorporating the swap operation to solve it. We then optimize the power allocation subproblem by employing the successive convex approximation approach, which iteratively approximates the original nonconvex problem to a convex one. Finally, numerical results are presented to demonstrate the effectiveness of the proposed schemes.

Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) has received substantial attentions due to its capability of constructing the intelligent wireless communication environment. This letter investigates cooperative STAR-RISs assisted non-orthogonal multiple access (NOMA) system under near-field and far-field scenarios. Specifically, a sum rate maximization problem is constructed by optimizing the phase shifts, amplitude coefficients, and power allocation. Furthermore, a low complexity algorithm is proposed to optimize the phase shifts and amplitude coefficients individually. The amplitude coefficients of reconfigurable elements are obtained by transforming the original problem to a convex problem. A geometry programming (GP) method is invoked to solve the power allocation problem. Simulation results show that the proposed cooperative-STAR-RISs aided NOMA method has the capability of enhancing sum rate compared with various benchmark schemes.

A novel coexisting passive reconfigurable intelligent surface (RIS) and active decode-and-forward (DF) relay assisted non-orthogonal multiple access (NOMA) transmission framework is proposed. In particular, two communication protocols are conceived, namely Hybrid NOMA (H-NOMA) and Full NOMA (F-NOMA). Based on the proposed two protocols, both the sum rate maximization and max-min rate fairness problems are formulated for jointly optimizing the power allocation at the access point and relay as well as the passive beamforming design at the RIS. To tackle the non-convex problems, an alternating optimization (AO) based algorithm is first developed, where the transmit power and the RIS phase-shift are alternatingly optimized by leveraging the two-dimensional search and rank-relaxed difference-of-convex (DC) programming, respectively. Then, a two-layer penalty based joint optimization (JO) algorithm is developed to jointly optimize the resource allocation coefficients within each iteration. Finally, numerical results demonstrate that: i) the proposed coexisting RIS and relay assisted transmission framework is capable of achieving a significant user performance improvement than conventional schemes without RIS or relay; ii) compared with the AO algorithm, the JO algorithm requires less execution time at the cost of a slight performance loss; and iii) the H-NOMA and F-NOMA protocols are generally preferable for ensuring user rate fairness and enhancing user sum rate, respectively.