In this study, we propose an innovative method for optimal spectrum partitioning and resource allocation in unmanned aerial vehicle (UAV)-assisted heterogeneous networks (HetNets). We divide the HetNets architecture into two communication tiers: the uplink for UAVs and IoT devices, and the downlink for the macro base station (MBS) and user equipment (UE). Our unified resource allocation framework includes power allocation, subchannel association along with introducing a novel spectrum partitioning parameter that are jointly optimized for both tiers. We introduce a new utility function as a multi-objective optimization (MOO) problem which simultaneously maximize spectral efficiency (SE) in MBS-UE networks and minimize transmission delay in UAV-IoT networks while maintaining quality of service (QoS). Due to the nonconvex nature and inherent complexity of the problem which is complex and intractable, we transform the multi-objective formulation into a single-objective one using a scalarization method. We then develop two sub optimal effective algorithms: an analyticalbased approach and a learning-based approach. In the analytical-based approach, we decompose the multiobjective problem into several subproblems using block coordinate descent (BCD); each nonconvex subproblem is subsequently transformed into a convex one via the successive convex approximation (SCA) algorithm. In the learning-based approach, we propose a lowcomplexity framework that leverages both supervised and unsupervised deep learning strategies. Simulation results indicate that both algorithms outperform baseline schemes, with the learning-based approach showing particularly promising performance compared to the analytical-based approach.

In this paper, the decoding order error of successive interference cancellation (SIC) of multicarrier nonorthogonal multiple access (NOMA) due to the random walk of the users and position estimation deviation is considered in resource allocation. This factor extremely degrades the performance of NOMA in terms of sum rate and outage probability. Therefore, two optimal power allocation strategies for users are derived that maximize the sum rate and minimize the outage probability. The simulation results show that by considering the decoding order error in resource allocation, better performance can be achieved compared to the previous power allocation algorithms without considering this fact, which are a well-known water filling algorithm and a power allocation that maximizes the rate with minimum rate constraint.

Resource allocation has always been one of the most critical aspects of cellular communications. With the advent of new generations of telecommunications, the exponential increase in the number of users and the demand for higher bandwidth and improved user experience have significantly heightened the importance of resource allocation techniques. Non-Orthogonal Multiple Access (NOMA) is considered one of the most efficient user access methods. It remains a focus of interest due to its relatively straightforward implementation compared to newer access methods. In this paper, we address the optimization problem using one of the most promising optimization techniques, reinforcement learning (RL), which outperforms traditional methods regarding computational efficiency and accuracy. Specifically, we employ the dueling architecture and demonstrate its efficacy in calculating the users’ data rate. In the simulation section, we also compute the spectrum efficiency (SE) for users at various power levels and showcase its performance. We have compared the performance of our proposed model with the exhaustive search method, and the results validate its effectiveness.

This study presents an innovative fractional order Rayleigh fading model that can be used for channel capacity estimation in the presence of additive white generalized Gaussian noise. The proposed model assumes that the real and imaginary parts of channel gains are generalized Gaussian random variables, which makes it possible to consider the traditional Rayleigh fading model as a special case of fractional order Rayleigh fading. Compared to the Rayleigh model, the fractional order Rayleigh fading model offers a more precise representation of new real‐world communication, such as integrating terrestrial and underwater networks in sixth‐generation communications channels. The probability density function of the channel gain with additive white generalized Gaussian noise is analyzed here. Furthermore, the ergodic and outage capacities of the channel are determined, taking into account the assumption that the channel state information is only available at the receiver. The ergodic capacity is calculated using Meijer's G‐functions, resulting in a closed‐form expression. Numerical simulations demonstrate the superiority of the fractional order Rayleigh fading model over the Rayleigh channel. Moreover, the impact of ergodic and outage capacities under diverse channel characteristics is assessed.

With the development of the industry and the increase of smart devices in recent years, the Internet of Things has expanded as one of the solutions to meet human needs in smart societies. However, this emerging technology suffers from security vulnerabilities. In recent years, blockchain has attracted much attention due to its decentralization. However, blockchain is computationally expensive, with limited scalability and high overhead costs. Also, designing a secure and robust blockchain for use in the Internet of Things is challenging. In this article, we provide solutions to improve the sharding method to achieve a lightweight and scalable blockchain and enhance the efficiency of the Internet of Things. To assign nodes to shards in the first round, we use the Verifiable Random Function (VRF), and after a few rounds, nodes are assigned to shards according to their point and performance in the network. We use the IBFT consensus algorithm to process the transaction in shards. Unlike other consensus algorithms based on Byzantine fault tolerance, this algorithm requires 2f+1 replicas instead of 3f+1 for fault tolerance. Also, in this method, the team leader’s shard is considered, so nodes with the highest performance assign to this shard. If the malicious behavior of the team leader is detected, one of the nodes in the team leader’s shard is replaced by it. For cross-transaction processing, we present an improved two-phase commit method that solves many problems with existing two-phase commit methods. Finally, we analyze our design for efficiency and security. Our qualitative arguments show that this design is resistant to several security attacks and that the scalability and throughput of IoT are increased compared to previous designs.

Abstract The Non Orthogonal Multiple Access (NOMA) is a popular candidate for the next generation of wireless networks. Two advantages of NOMA are that it can achieve higher rate and support more users compared to the orthogonal multiple access (OMA). To support more users in NOMA system, we can assume there are infinite users in the system wanting to share one subchannel to derive the upper bound of the achievable rate of NOMA. In this article, the optimal power allocation function of infinite users in NOMA is derived which maximizes the average achievable rate of the system under the maximum power constraint. The performances of the proposed power allocation strategy are compared with simple case with only two users in NOMA. The simulation results show the gap between the average achievable rate and the outage probability of infinite users and two users in NOMA system.

Utilizing unmanned aerial vehicles (UAVs) as aerial base stations is a new and promising technology that enables the connectivity of a large volume of devices such as sensors and machines, referred to as massive Internet of Things (mIoT). This article aims to analyze and optimize the area spectral efficiency (ASE) by investigating the efficient deployment of UAVs to ensure reliable uplink communication from ground IoT devices to UAVs. We utilize the tools from stochastic geometry to derive the closed form expression of the ASE in the interference‐limited regime. We propose a novel framework for maximizing the ASE of UAV‐enabled networks by simultaneous optimization of UAVs' altitude and density. The simulation results demonstrate that deploying UAVs with combined optimal density and altitude outperforms more conventional deployment strategies in terms of the ASE.

Generalized frequency division multiplexing (GFDM) is a flexible non-orthogonal waveform candidate for 5G which can offer some advantages such as low out-of-band emission and high spectral efficiency. This paper investigates the effects of nonlinear behavior of practical power amplifier (PA) on the GFDM spectrum. A closed form expression for power spectral density (PSD) of GFDM signal is extracted. Then, PSD at the output of PA as a function of input power and the coefficients of nonlinear polynomial PA model is derived. In addition, the adjacent channel power (ACP) and ACP ratio, as two important performance metrics, are evaluated. The simulation results confirm the accuracy of derived analytical expressions. Moreover, to validate the performance of GFDM modulation after nonlinear PA, it is compared with orthogonal frequency division multiplexing modulation.

Minimizing the average achievable distortion (AAD) of a Gaussian source at the destination of a two-hop block fading relay channel is studied in this paper. The communication is carried out through the use of a Decode and Forward (DF) relay with no source-destination direct links. The associated receivers of both hops are assumed to be aware of the corresponding channel state information (CSI), while the transmitters are unaware of their corresponding CSI. The current paper explores the effectiveness of incorporating the successive refinement source coding together with multi-layer channel coding in minimizing the AAD. In this regard, the closed form and optimal power allocation policy across code layers of the second hop is derived, and using a proper curve fitting approach, a close-to-optimal power allocation policy associated with the first hop is devised. It is numerically shown that the DF strategy closely follows the Amplify and Forward (AF) relaying, while there is a sizable gap between the AAD of multi-layer coding and single-layer source coding.

Mobile satellite communication experiences various channel state conditions. These channel impairments degrade overall system reliability and bandwidth efficiency. Dynamic link adaptation considers channel variations and adapts the transmission parameters respectively. This paper investigates link adaptation in mobile satellite communications through adaptive coding and modulation scheme. Average spectral efficiency improvement has been obtained by adaptation algorithms while the error probability constraints are met. To further extend our scenario in real world satellite systems, power amplifier nonlinearity is taken into account. Power amplifier nonlinear performance introduces distortion and signal to noise ratio (SNR) degradation. Hence, an optimized adapting procedure is proposed to overcome the resulting impairments. Moreover, propagation delay in satellite links are significantly large which outdates the channel state information (CSI) used for link adaptation decision. Channel states and fading conditions would change considerably in this long round trip time, especially in mobile user scenario. As a result, deploying a prediction method to predict time varying channel for reliable modulation and coding selection is required. The accuracy and performance of physical layer adaptation were improved by implementing channel power prediction, mitigating large round trip time and fast channel variations. Results indicate satisfactory link availability even in severe shadowing states of the channel.

In this paper, the optimum power allocation for subcarriers in orthogonal frequency division multiple system is derived which maximizes the total rate of system under nonlinearity of practical power amplifier. The rate of system by using analytical signal to interference ratio formula is optimized to derive optimal power scales for subcarriers. The problem is nonconvex and a comprehensive learning particle swarm optimization method is designed to solve the problem. The simulation results demonstrate that by considering nonlinearity in power allocation, better rate can be achieved compared to conventional power allocation methods.

In this paper, a novel framework for dynamic multiple access technology selection among orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) techniques is proposed. For this setup, a joint resource allocation problem is formulated in which a new set of access technology selection parameters along with power and subcarrier are allocated for each user based on each user's channel state information. Here, a novel utility function is defined to take into account the rate and costs of access technologies. This cost reflects both the complexity of performing successive interference cancellation and the complexity incurred to guarantee a desired bit error rate. This utility function can inherently capture the tradeoff between OMA and NOMA. Due to the non-convexity of the proposed resource allocation problem, a successive convex approximation is developed in which a two-step iterative algorithm is applied. In the first step, called access technology selection, the problem is transformed into a linear integer programming problem, and then, in the second step, a nonconvex problem, referred to power allocation problem, is solved via the difference-of-convex-functions (DC) programming. Moreover, the closed-form solution for power allocation in the second step is derived. For diverse network performance criteria such as rate, simulation results show that the proposed new dynamic access technology selection outperforms single-technology OMA or NOMA multiple access solutions.

Single carrier-frequency division multiple access (SC-FDMA) is a multiple access technique in broadband wireless networks which has been adapted by 3GPP for uplink transmission in 4G mobile communications. In this paper, the nonlinear effect of practical power amplifier (PA) is studied on the power allocated SC-FDMA signals. The interference power on the estimated symbols of all users are derived by two approaches based on the polynomial model of nonlinear PA and allocated power of subcarriers. In the first approach, an accurate analysis is followed.An approximation of the accurate result is presented in the second approach to provide a closed form formula. A simulation study is conducted to verify the analytical outcomes. The simulation and the exact analytical results are significantly matched. Conversely, the approximate relations are extremely suitable for allocating power in system design due to their closed form nature where they provide acceptable accuracy for practical applications.

Generalized frequency division multiplexing (GFDM) is suitable for cognitive radio (CR) networks due to its low out-of-band (OOB) emission and high spectral efficiency. In this paper, we thus consider the use of GFDM to allow an unlicensed secondary user (SU) to access a spectrum hole. However, in an extremely congested spectrum scenario, both active incumbent primary users (PUs) on the left and right channels of the spectrum hole will experience OOB interference. While constraining this interference, we thus investigate the problem of power allocation to the SU transmit subcarriers in order to maximize the overall data rate where the SU receiver is employing Matched filter (MF) and zero-forcing (ZF) structures. The power allocation problem is thus solved as a classic convex optimization problem. Finally, total transmission rate of GFDM is compared with that of orthogonal frequency division multiplexing (OFDM). For instance, when right and left interference temperature should be below 10 dBm, the capacity gain of GFDM over OFDM is 400

In this paper, a multi-objective resource allocation algorithm in a novel density-aware design of virtualized software-defined cloud radio access network (C-RAN) is proposed. We consider two design modes based on the average density of users: 1) high-density mode when a large number of low-cost remote radio heads (RRHs) without baseband processing capability are controlled by one single base station and 2) low-density mode when a small number of RRHs with baseband processing capability are deployed. In high-density mode, the challenge of front-haul capacity limitation is tackled via separating control plane and data plane in a heterogeneous structure. Besides, the fully centralized processing and management, and energy-efficient use of infrastructure in low traffic time by turning off RRHs are achieved. In the low-density mode, the transmission delay due to the large distance between the sparse RRHs and cloud unit, is more critical. This practical issue is handled by sharing the baseband processing and resource management among these units in a hierarchical structure. This resulting heterogeneous /hierarchical virtualized software-defined cloud-RAN (HVSD-CRAN) offers various tradeoffs in resource management objectives such as throughput and delay versus power and cost. Consequently, we resort to multi-objective optimization theory to propose a resource allocation framework in HVSD-CRAN.

In this paper, facilitated via the flexible software defined structure of the radio access units in 5G, we propose a novel dynamic multiple access technology selection among orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) techniques for each subcarrier. For this setup, we formulate a joint resource allocation problem where a new set of access technology selection parameters along with power and subcarrier are allocated for each user based on each user's channel state information. Here, we define a novel utility function taking into account the rate and costs of access technologies. This cost reflects both the complexity of performing successive interference cancellation and the complexity incurred to guarantee a desired bit error rate. This utility function can inherently demonstrate the trade-off between OMA and NOMA. Due to non-convexity of our proposed resource allocation problem, we resort to successive convex approximation where a two-step iterative algorithm is applied in which a problem of the first step, called access technology selection, is transformed into a linear integer programming problem, and the nonconvex problem of the second step, referred to power allocation problem, is solved via the difference-of-convex-functions (DC) programming. Moreover, the closed-form solution for power allocation in the second step is derived. For diverse network performance criteria such as rate, simulation results show that the proposed new dynamic access technology selection outperforms single-technology OMA or NOMA multiple access solutions.

We investigate the problem of the rate maximization of a generalized frequency division multiplexing (GFDM) based secondary user (SU) link operating over a spectrum hole where adjacent channel interference (ACI) on two active primary users (PUs) in the right and left adjacent channels must be below a threshold. The SU transmitter has nonlinear power amplifier (PA) distortions. We consider a zero forcing (ZF) receiver which removes self-generated interference of GFDM. For a third-order nonlinear PA, we derive the signal-to-interference-plus noise ratio (SINR) and ACI. By using successive convex approximations, we develop a power allocation algorithm to maximize the SU rate subject to the ACI limits on adjacent PUs. Finally, we show that the proposed algorithm improves the SU data rate and that GFDM achieves a higher SU data rate than orthogonal frequency division multiplexing (OFDM).

In this paper, the nonlinear distortion effects of power amplifiers are studied, in particular from uplink subcarrier and power allocation perspectives in multiuser multicarrier cognitive networks. The out‐of‐band emissions of a nonlinear power amplifier create interference to the other users. The target is then to maximize the achievable uplink rate in a multiuser multicarrier cognitive network where a cognitive user should not introduce interference to the other users more than a specified threshold level, called interference power constraint. This task is formulated as a convex optimization problem with interference power constraints. Obtaining a closed‐form solution for this problem is, however, not feasible due to its nonlinear nature. Accordingly, the problem is solved numerically, and extensive simulations are conducted to obtain performance results. The obtained results are also compared with a more simple heuristic approach. The results show that the proposed scheme provides a maximum rate while at the same time also guaranteeing that the interference levels are lower than the specified interference power constraint. The results also indicate that the maximum rate can be closely approximated by using the assisted or optimization‐directed heuristic approach.

This paper considers the uplink dynamic resource allocation in a cloud radio access network (C-RAN) serving users belonging to different service providers (called slices) to form virtualized wireless networks (VWN). In particular, the C-RAN supports a pool of base-station (BS) baseband units (BBUs), which are connected to BS radio remote heads (RRHs) equipped with massive massive multiple input multiple output (MIMO), via fronthaul links with limited capacity. Assuming that each user can be assigned to a single RRH-BBU pair, we formulate a resource allocation problem aiming to maximize the total system rate, constrained on the minimum rates required by the slices and the maximum number of antennas and power allocated to each user. The effects of pilot contamination error on the VWN performance are investigated and pilot duration is considered as a new optimization variable in resource allocation. This problem is inherently nonconvex, NP-hard and, thus, computationally inefficient. By applying the successive convex approximation and complementary geometric programming approach, we propose a two-step iterative algorithm: one to adjust the RRH, BBU, and fronthaul parameters, and the other for power and antenna allocation to users. Simulation results illustrate the performance of the developed algorithm for VWNs in a massive-MIMO-aided and fronthaul-limited C-RAN, and demonstrate the effects of imperfect channel state information estimation due to pilot contamination error, and the optimal pilot duration.

In this study, the physical layer resource allocation in orthogonal frequency-division multiple access (OFDMA) systems is studied by considering the non-linear effects of power amplifier (PA). The non-linearity produces cross-correlation among the subcarriers and results in deteriorating the OFDMA subcarriers orthogonality. Hence, the signal-to-interference ratio (SIR) of the output signal of PA for OFDMA signal with different power scaling factors for subcarriers is derived. Then, the theoretical SIR is verified by simulation result. Next, the optimisation problem is designed to maximise the total achievable downlink rate of all users by assigning subcarriers and power scaling factors. This non-convex problem is solved by comprehensive learning particle swarm optimisation. The simulation results show that when PA is non-linear, better total rate is achieved by considering non-linearity in resource allocation compared with the conventional resource allocation techniques.