Computationally Efficient Algorithm For Sub-carrier, Bit, and Power Allocation

The increasing use of relays in wireless communication systems is a driving force to explore innovative techniques that can improve the quality of service as well as enhance the coverage of wireless systems. Orthogonal frequency division multiple access (OFDMA) is widely deployed in broadband wireless systems to effectively combat the frequency selectivity of the wireless fading channel. Wireless relays are used in practical OFDM-based wireless systems such as WiMAX to increase the capacity and diversity order of the system. To fully exploit the benefits of OFDMA, relaying systems require efficient management of resources, including power and subcarriers. The difficulty in resource allocation in a relaying system is the nonconvexity of the optimization problem caused by the inter-relationship of all the resources through the relay station. Therefore, suboptimal and heuristic algorithms are proposed to assign power and subcarriers to the users in the relaying system while maintaining flexible fairness in resource allocation. In this dissertation, we improve the efficiency of subcarrier and power allocation algorithms with flexible fairness in the downlink of an amplify-and-forward (AF) OFDM relaying system. We propose an asymptotically fair subcarrier allocation algorithm that provides flexibility in the fairness index for possible improvement in the total throughput of the system by using a group-based approach. We investigate the problem of power allocation for an AF relaying scheme by considering three different objective functions: average SNR, average BER, and outage probability. The results are presented for various relay locations and different total power in the system. It is shown that the solution to the power allocation problem does not depend on the objective function when the relay is closer to the mobile user for different objective functions. By deriving the capacity of the AF relaying, a new parameter, called cooperation coefficient, is introduced to quantify the cooperation level from the relaying links in the data rate of the the mobile station. Assuming flat power allocation and using the cooperation coefficient parameter, a cooperative subcarrier allocation algorithm is proposed that increases the total throughput of a multi-user OFDM relaying system by approximately 15%. We also propose a two-step solution for subcarrier and power allocation of an AF relaying system. In the first step, the total power is allocated to the users and relays based on the power ratio found for the single-relay system. In the second step, subcarriers are assigned to the users based on the cooperative subcarrier allocation algorithms. Based on our finding, the total throughput of the system improves by 25% using the two-step procedure. By applying the proposed asymptotically fair idea to the two-step subcarrier and power allocation, we maintain a flexible fairness and further increase the total throughput in an OFDM relaying system.

A novel multi-stream carrier aggregation (MSCA) enabled coordinated multi-point (CoMP) framework in heterogeneous cellular networks (HetNets) is proposed. Our objective is to maximize the energy efficiency (EE) of the proposed framework by jointly optimizing base station (BS) clustering, user association, sub-carrier assignment and power allocation. The formulated problem is proved to be NP-hard. By analyzing the structure of the original problem, we decouple it into power allocation sub-problem and sub-carrier assignment sub-problem. For the power allocation sub-problem, we propose sequential minimization technique and Zoutendijk feasible direction method to find the optimal power allocation with fixed sub-carrier assignment. For the sub-carrier assignment sub-problem, we propose genetic algorithm (GA) to represent the BS clustering and sub-carrier assignment, and design two GA based joint optimization frameworks. One is using the power allocation algorithm for fitness evaluation and guiding the evolution of GA. Another is optimizing these two sub-problems in an iterative manner, whereas the optimal sub-carrier assignment is updated timely as any change of the power allocation. Numerical results validate the feasibility, fast convergence, and flexibility of the proposed algorithm, and is superior to conventional MSCA or CoMP enabled HetNets.

Orthogonal frequency division multiplexing (OFDM) is an optimal technique for high-speed transmission. In this paper, a multicell multiuser OFDM system with adaptive subcarrier allocation and adaptive modulation is considered. We propose an adaptive subcarrier, bit and power allocation algorithm to maximum the total throughput of the multicell system in the presence of cochannel interference (CCI), frequency selective Rayleigh fading and additive white Gaussian noise (AWGN). Its performance in a multicell system with different number of users is investigated through simulation. It is shown from the simulation that this adaptive allocation algorithm enhances the throughput of OFDM systems as compared to equal power allocation and is suitable for multiuser system.

In this paper we propose a resource allocation algorithm for uplink sparse code multiple access (SCMA) networks to maximize the energy efficiency (EE). Due to the joint optimization of factor graph matrix and power allocation matrix, the EE maximization is a non-convex mixed-integer non-linear programming (MINLP) problem. After transforming the non-convex form of the uplink sum rate to a convex one and separating subcarrier assignment and power allocation, we propose an energy efficient subcarrier assignment algorithm. By applying the fractional programming theory based on the Dinkelbach method, we then propose power allocation algorithm to maximize EE. Finally, the simulation results show that the proposed resource allocation algorithm can significantly increase the EE of the uplink SCMA network.

When implementing an appropriate windowing, the interference from a Cognitive Radio (CR) system to licensed systems (primary users) will be significantly reduced. Consequently, power allocated to subcarriers can be increased, especially subcarriers having far spectral distance to primary user bands can be allocated full of its maximum possible power. In this paper, we propose a new class of sub-optimal subcarrier power allocation algorithm that significantly reduces complexity of OFDMA-based CR systems. Two sub-optimal proposals, called Pre-set Filling Range (PFR) and Maximum Filling Range (MFR) are studied. Investigations show that this new power allocating algorithm allows CR systems obtain high throughput while retaining low complexity.

In this paper, a joint subcarrier and power allocation algorithm is proposed to improve the physical layer security in cooperative orthogonal frequency division multiple access (OFDMA) networks, where several source-destination pairs and one untrusted relay are involved. The relay is friendly and intends to help these pairs to enhance their communications. However, it may be overheard by a malicious eavesdropper at the same time. To optimize the joint subcarrier and power allocation with low complexity, we divide the optimization problem into two simpler subproblems. Firstly, the subcarriers are assigned to the source-destination pairs by employing the dual approach under the assumption that the relay power is equally allocated to all the subcarriers. Then, the relay power is allocated to the subcarriers based on the alternative ascending clock auction mechanism. In addition, we prove that the two subproblems can converge after a finite number of iterations. We also find that the proposed auction is cheat-proof and, thus, can avoid the cheating behaviors in the auction process. Numerical results demonstrate that our proposed algorithm can effectively improve the system sum secrecy rate, and the convergence performance is also desirable.

Mobile communication systems are continuously evolving and the search for new technologies is a never-ending activity in industry and academy. Rate-Splitting Multiple Access (RSMA) is a recent multiple access scheme for multi-antenna wireless networks that enjoys the advantages of both Space Division Multiple Access (SDMA) and non-orthogonal multiple access (NOMA). Considering a multicarrier RSMA-based system, this work studies the user matching, subcarrier assignment and power allocation among subcarriers with the objective of maximizing the weighted sum rate. So as to solve this problem, we propose a two-step solution. In the first part, assuming equal power allocation, we solve the Integer Linear Programming (ILP) problem for the user matching and subcarrier assignment by converting it into a Linear Programming (LP) problem by leveraging the properties of unimodular matrices in problem constraints. In the second part, we optimize the power allocation among subcarriers using a gradient descent method. Simulations results show the impact of the number of subcarriers and number of users in the sum rate, how the weights can be used to ensure user fairness, and the advantages of our proposed solution over benchmarking solutions.

We consider multi-hop OFDM relaying systems employing either decode-and-forward (DF) or amplify-and-forward (AF) relaying protocols in this paper. We propose adaptive power allocation (PA) algorithms under joint transmit power constraint at source and relays in order to maximize system capacity with channel state information (CSI) known at all nodes. The paring of subcarriers is also discussed. Simulation results show that the proposed adaptive PA algorithms achieve higher system capacity than uniform PA algorithms. If paring techniques are employed, system capacity can be further enhanced.

In this article, a novel multiple-input multiple-out (MIMO) transmission scheme, called generalized polarized enhanced spatial modulation (GPESM), is proposed for dual-polarized land mobile satellite (LMS) communications. We first introduce the enhanced spatial modulation (ESM) technique for dual-polarized LMS communications, in which polarization dimension, spatial dimension and multiple signal constellations are used to transmit information and obtain substantial performance gain. Meanwhile, the theoretical upper bound for the average bit error probability (ABEP) of the proposed GPESM scheme is derived. In order to further improve the reliability of the system, we also propose two novel power allocation (PA) algorithms for GPESM system, which are the optimization-driven approximated max-min distance (AMMD)-based PA algorithm and the data-driven deep neural network (DNN)-based PA algorithm. To achieve an enhanced spatial diversity gain, we consider to apply a reconfigurable intelligent surface (RIS) to the GPESM system as a relay to assist in transmitting information. In this way, the user can receive the information transmitted by the satellite on one hand, and the information sent by the satellite via the RIS relay on the other hand. We also extend the above-mentioned two PA algorithms to the RIS-assisted GPESM systems. Our simulation results show that the RIS-assisted GPESM systems are capable of obtaining high bit error rate (BER) performance gain (up to 10 dB) compared to the standard GPESM system and two PA algorithms can further improve the performance to the systems.

Recently, nonorthogonal multiple access (NOMA) has been proposed as a key enabler and candidate to overcome the spectral‐inefficiency of the conventional orthogonal multiple‐access (OMA) techniques, and meet the pressing demands for massive connectivity in 5G and beyond cellular networks. Despite the numerous merits of NOMA, power allocation must be optimized. In addition, the scarcity of the frequency spectrum is still a major bottleneck, and thus, bandwidth must also be carefully utilized. This paper investigates the problem of joint power allocation and subcarrier assignment (J‐PA‐SA) in uplink clustered multicarrier NOMA networks. Specifically, the objective is to maximize the network sum‐rate subject to users' minimum rate requirements. Two novel network‐coded single and multiple subcarrier assignment schemes are proposed to improve bandwidth utilization over the conventional single and multiple subcarrier assignment schemes. In general, the J‐PA‐SA problem is nonconvex and NP‐hard (i.e., excessively complex). To effectively solve it, it is decomposed into two subproblems: (1) power allocation per (user cluster, subcarrier) combination, and (2) subcarrier assignment. Solution procedures are proposed to solve the J‐PA‐SA problem for the conventional and network‐coded SA schemes. Simulation results revealed that the proposed solution procedures yield the optimal network sum‐rate solutions with low‐complexity, while satisfying the users' minimum rate requirement. Lastly, the network‐coded SA schemes are shown to significantly outperform to the conventional SA schemes for the NOMA and OMA networks.

Cache is becoming an effective method to improve the transmission rate and reduce the backhaul load. Nevertheless, owing to the limited capacity of backhaul, less power should be allocated to the users whose requested contents do not exist in the local caches to maximize the performance of the caching users. Thus, in this paper, power allocation (PA) is considered to further improve the performance of cache-aided small-cell networks with limited backhaul, where interference alignment is utilized to manage interferences among users. Specially, three PA algorithms are proposed. First, we come up with a PA algorithm to maximize the sum rate of the network, considering the limitation of backhaul. Then, in order to make more users meet their rate requirement, a PA algorithm to minimize the average outage probability is proposed. In addition, in order to further improve the users' experience, a PA algorithm to maximize the average satisfaction of all the users is also designed. Plenty of simulation results are provided to show the effectiveness of the three proposed PA algorithms.

Cooperative communication with spectrum sharing has been proved to be an efficient way to reduce energy consumption. In this work, an optimal power allocation algorithm is proposed for mobile cooperative beamforming networks, where several secondary users (SUs) collaboratively act as relay nodes to assist the transmission of a primary user (PU) in reward of transmitting their own information. The optimal power allocation scheme is obtained by solving a convex optimization problem aiming at minimizing network energy consumption with guaranteed quality of service (QoS) for both PU and SUs. Simulation results show that with the consideration of overheads, circuit power, and energy costs for message sharing, a cooperative beamforming network augmented with our proposed power allocation algorithm performs better in scenarios of more SUs in terms of higher energy efficiency and less power consumption per unit throughput. Additionally, the proposed scheme outperforms the conventional best-relay selection approach in the aspects of power consumption per unit throughput, energy efficiency, and network power consumption. Furthermore, simulations also demonstrate that the derived cooperative beamforming algorithm exhibits superior performance in energy saving than conventional methods in high-speed scenarios.

In this work, we investigate the uplink resource allocation problem for virtualized wireless networks (VWNs) supported by multi-carrier non-orthogonal multiple access (MC-NOMA) and present a sensitivity analysis of such a system to imperfect successive interference cancellation (SIC) and various system parameters. The proposed algorithm for power and sub-carrier allocation is derived from the non-convex optimization minimizing power subject to rate and sub-carrier reservations, for which an optimal solution is NP-hard. To develop an efficient solution, we decompose the optimization into separate power and sub-carrier allocation problems and propose an iterative algorithm based on successive convex approximation and complementary geometric programming. Simulation results demonstrate that compared to orthogonal multiple access, for imperfect SIC with residual interference even up to 10%, the proposed algorithm for MC-NOMA can offer significant improvement in spectrum and power efficiency.

This paper studies the challenge of efficient resource allocation in wireless networks by investigating the potential benefits of cognitive radio networks (CRN) and non‐orthogonal multiple access (NOMA) techniques. Specifically, the purpose of this paper is to explore user pairing and power allocation in a multiuser cooperative CRN‐NOMA network. To achieve the goal, a novel user pairing algorithm with reinforcement learning (RL) is proposed to adaptively select the optimal group for pairing according to the factors such as channel gain, distances, and angles. A user group is consisted of a primary user (PU) and up to three secondary users (SUs). The key findings of this paper highlight the effectiveness of RL in optimizing power allocation while considering various constraints. As a result, significant improvements in network throughput and user fairness are achieved. Compared with the classical power allocation algorithms (i.e., equal power allocation, proportional power allocation, iterative power allocation, and iterative water‐filling power allocation), the proposed power allocation based on RL algorithm and iterative water‐filling power allocation algorithm have better performance in terms of throughput, fairness, spectrum efficiency, and energy efficiency. In addition, compared with the iterative water‐filling power algorithm, the proposed algorithm improves the throughput by 31.71% and obtains twice the spectrum efficiency and energy efficiency. The simulation results consistently show that the proposed method has advantages in enhancing PU capacity. Further advancements in this field of research are encouraged based on the promising outcomes of this study.

An intelligent reflecting surface (IRS), which comprises numerous passive elements, is considered a promising technology for smart wireless communication. However, the passive characteristics of an IRS render the explicit estimation of the channel state information to appropriately adjust its reflection coefficient challenging. This study proposes a deep reinforcement learning-based algorithm that learns the precoding vector of the base station (BS) and the IRS phase shift from the wireless environment to address this problem. We develop a beam-pattern-based learning framework for this algorithm that indirectly maps the wireless environment to the phase shift to manage the large state-action space caused by the multiple elements of the BS and IRS. Based on the simulation results, the proposed algorithm can learn from the environment and establish a transmission strategy that improves the user's transmission rate. Furthermore, the results validate that the proposed algorithm based on the beam pattern learning framework is more efficient and scalable to the number of IRS elements compared to the method that directly builds a mapping to the phase shift.