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In this paper, we design a new flexible smart software-defined radio access network (Soft-RAN) architecture with traffic awareness for sixth generation (6G) wireless networks. In particular, we consider a hierarchical resource allocation model for the proposed smart soft-RAN model where the software-defined network (SDN) controller is the first and foremost layer of the framework. This unit dynamically monitors the network to select a network operation type on the basis of distributed or centralized resource allocation procedures to intelligently perform decision-making. In this paper, our aim is to make the network more scalable and more flexible in terms of conflicting performance indicators such as achievable data rate, overhead, and complexity indicators. To this end, we introduce a new metric, i.e., throughput-overhead-complexity (TOC), for the proposed machine learning-based algorithm, which supports a trade-off between these performance indicators. In particular, the decision making based on TOC is solved via deep reinforcement learning (DRL) which determines an appropriate resource allocation policy. Furthermore, for the selected algorithm, we employ the soft actor-critic (SAC) method which is more accurate, scalable, and robust than other learning methods. Simulation results demonstrate that the proposed smart network achieves better performance in terms of TOC compared to fixed centralized or distributed resource management schemes that lack dynamism. Moreover, our proposed algorithm outperforms conventional learning methods employed in recent state-of-the-art network designs., Comment: ACCEPTED by IEEE ICC 2023: SAC Machine Learning for Communications and Networking Track. arXiv admin note: substantial text overlap with arXiv:2202.09093

In this work, we adopt the emerging technology of mobile edge computing (MEC) in the Unmanned aerial vehicles (UAVs) for communication-computing systems, to optimize the age of information (AoI) in the network. We assume that tasks are processed jointly on UAVs and BS to enhance edge performance with limited connectivity and computing. Using UAVs and BS jointly with MEC can reduce AoI on the network. To maintain the freshness of the tasks, we formulate the AoI minimization in two-hop communication framework, the first hop at the UAVs and the second hop at the BS. To approach the challenge, we optimize the problem using a deep reinforcement learning (DRL) framework, called federated reinforcement learning (FRL). In our network we have two types of agents with different states and actions but with the same policy. Our FRL enables us to handle the two-step AoI minimization and UAV trajectory problems. In addition, we compare our proposed algorithm, which has a centralized processing unit to update the weights, with fully decentralized multi-agent deep deterministic policy gradient (MADDPG), which enhances the agent's performance. As a result, the suggested algorithm outperforms the MADDPG by about 38\%

As the services and requirements of next-generation wireless networks become increasingly diversified, it is estimated that the current frequency bands of mobile network operators (MNOs) will be unable to cope with the immensity of anticipated demands. Due to spectrum scarcity, there has been a growing trend among stakeholders toward identifying practical solutions to make the most productive use of the exclusively allocated bands on a shared basis through spectrum sharing mechanisms. However, due to the technical complexities of these mechanisms, their design presents challenges, as it requires coordination among multiple entities. To address this challenge, in this paper, we begin with a detailed review of the recent literature on spectrum sharing methods, classifying them on the basis of their operational frequency regime that is, whether they are implemented to operate in licensed bands (e.g., licensed shared access (LSA), spectrum access system (SAS), and dynamic spectrum sharing (DSS)) or unlicensed bands (e.g., LTE-unlicensed (LTE-U), licensed assisted access (LAA), MulteFire, and new radio-unlicensed (NR-U)). Then, in order to narrow the gap between the standardization and vendor-specific implementations, we provide a detailed review of the potential implementation scenarios and necessary amendments to legacy cellular networks from the perspective of telecom vendors and regulatory bodies. Next, we analyze applications of artificial intelligence (AI) and machine learning (ML) techniques for facilitating spectrum sharing mechanisms and leveraging the full potential of autonomous sharing scenarios. Finally, we conclude the paper by presenting open research challenges, which aim to provide insights into prospective research endeavors.

In this paper, we design a new smart softwaredefined radio access network (RAN) architecture with important properties like flexibility and traffic awareness for sixth generation (6G) wireless networks. In particular, we consider a hierarchical resource allocation framework for the proposed smart soft-RAN model, where the software-defined network (SDN) controller is the first and foremost layer of the framework. This unit dynamically monitors the network to select a network operation type on the basis of distributed or centralized resource allocation architectures to perform decision-making intelligently. In this paper, our aim is to make the network more scalable and more flexible in terms of achievable data rate, overhead, and complexity indicators. To this end, we introduce a new metric, throughput overhead complexity (TOC), for the proposed machine learning-based algorithm, which makes a trade-off between these performance indicators. In particular, the decision making based on TOC is solved via deep reinforcement learning (DRL), which determines an appropriate resource allocation policy. Furthermore, for the selected algorithm, we employ the soft actor-critic method, which is more accurate, scalable, and robust than other learning methods. Simulation results demonstrate that the proposed smart network achieves better performance in terms of TOC compared to fixed centralized or distributed resource management schemes that lack dynamism. Moreover, our proposed algorithm outperforms conventional learning methods employed in other state-of-the-art network designs., Comment: Submitted to IEEE for possible publications

In this paper, we propose a new resource allocation framework for unmanned aerial vehicle (UAV) assisted multicast wireless networks in which the network users according to their request are divided into several multicast groups. We adopt power domain non-orthogonal multiple access (PD-NOMA) as the transmission technology using which the dedicated signals of multicast groups are superimposed and transmitted simultaneously as the UAV passes over the communication area for fixed and mobile users. We discuss the proposed scenarios from two perspectives, offline and online mode. In offline mode, we implement the problem for fixed and mobile users whose locations are predictable (the location of users, over the communication time, is known at the beginning of the communication time) and in online mode for mobile users whose locations are unpredictable (the location of users, over the communication time, is unknown at the beginning of the communication time). Also, we proposed a scenario in which the online model the number of mobile users can grow in each time slot. We formulate the problem of joint power allocation and UAV trajectory design as an optimization problem that is non-linear and nonconvex for two proposed scenarios. To solve the problem, we adopt an alternate search method (ASM), successive convex approximation (SCA), and geometric programming (GP). Using simulation results, the performance of the proposed scheme is evaluated for different values of the network parameters.

One of the important applications of molecular communication is the targeted drug delivery process in which the drug molecules are released toward the target (receiver) in a way that the side effects are minimized in the human body. As the total number of released molecules increases, the receiver cannot receive all of the transmitted drug molecules. Therefore, the molecules would be accumulated in the system which results in side effects in the body. In order to diagnose the appropriate transmission rate of the drug molecules, it is important to investigate the reception process of the receiver. In this paper, a reception model is studied using queuing theory. In the proposed model, the rejection rate of the drug molecules due to different reasons, such as random movement of the molecules, as well as the rejection rate due to active receptors are taken into account. Moreover, an interval consisting of the lower and upper bounds for the number of released molecules is presented based on the proposed model in order to determine the range of allowable dosage of the drug molecules. It is shown that the queuing theory can be successfully employed in accurate modeling of the reception process of the receiver in drug delivery applications.

In this paper, we consider unmanned aerial vehicles (UAVs) equipped with a visible light communication (VLC) access point and coordinated multipoint (CoMP) capability that allows users to connect to more than one UAV. UAVs can move in 3-dimensional (3D) at a constant acceleration, where a central server is responsible for synchronization and cooperation among UAVs. The effect of accelerated motion in UAV is necessary to be considered. Unlike most existing works, we examine the effects of variable speed on kinetics and radio resource allocations. For the proposed system model, we define two different time frames. In the frame, the acceleration of each UAV is specified, and in each slot, radio resources are allocated. Our goal is to formulate a multiobjective optimization problem where the total data rate is maximized, and the total communication power consumption is minimized simultaneously. To handle this multiobjective optimization, we first apply the scalarization method and then apply multi-agent deep deterministic policy gradient (MADDPG). We improve this solution method by adding two critic networks together with two-stage resources allocation. Simulation results indicate that the constant acceleration motion of UAVs shows about 8\% better results than conventional motion systems in terms of performance., Comment: 12 pages, 11 figures

In this paper, we propose a joint radio and core resource allocation framework for NFV-enabled networks. In the proposed system model, the goal is to maximize energy efficiency (EE), by guaranteeing end-to-end (E2E) quality of service (QoS) for different service types. To this end, we formulate an optimization problem in which power and spectrum resources are allocated in the radio part. In the core part, the chaining, placement, and scheduling of functions are performed to ensure the QoS of all users. This joint optimization problem is modeled as a Markov decision process (MDP), considering time-varying characteristics of the available resources and wireless channels. A soft actor-critic deep reinforcement learning (SAC-DRL) algorithm based on the maximum entropy framework is subsequently utilized to solve the above MDP. Numerical results reveal that the proposed joint approach based on the SAC-DRL algorithm could significantly reduce energy consumption compared to the case in which R-RA and NFV-RA problems are optimized separately.

Spectrum sharing is a method to solve the problem of frequency spectrum deficiency. This paper studies a novel AI based spectrum sharing and energy harvesting system in which the freshness of information (AoI) is guaranteed. The system includes a primary user with access rights to the spectrum and a secondary user. The secondary user is an energy harvesting sensor that intends to use the primary user spectrum opportunistically. The problem is formulated as partially observable Markov decision processes (POMDPs) and solved using two methods: a deep Q-network (DQN) and dueling double deep Q-Network (D3QN) to achieve the optimal policy. The purpose is to choose the best action adaptively in every time slot based on its situation in both overlay and underlay modes to minimize the average AoI of the secondary user. Finally, simulation experiments are performed to evaluate the effectiveness of the proposed scheme compared to the overlay mode. According to the results, the average AoI in the proposed system is less than that of the existing models, including only overlay mode. The average user access improved from 30% in the overlay mode to 45% in the DQN and 48% in the D3QN.

This paper proposes a novel approach to improve the performance of a heterogeneous network (HetNet) supported by dual connectivity (DC) by adopting multiple unmanned aerial vehicles (UAVs) as passive relays that carry reconfigurable intelligent surfaces (RISs). More specifically, RISs are deployed under the UAVs termed as UAVs-RISs that operate over the micro-wave ($\mu$W) channel in the sky to sustain a strong line-of-sight (LoS) connection with the ground users. The macro-cell operates over the $\mu$W channel based on orthogonal multiple access (OMA), while small base stations (SBSs) operate over the millimeter-wave (mmW) channel based on non-orthogonal multiple access (NOMA). We study the problem of total transmit power minimization by jointly optimizing the trajectory/velocity of each UAV, RISs' phase shifts, subcarrier allocations, and active beamformers at each BS. The underlying problem is highly non-convex and the global optimal solution is intractable. To handle it, we decompose the original problem into two subproblems, i.e., a subproblem which deals with the UAVs' trajectories/velocities, RISs' phase shifts, and subcarrier allocations for $\mu$W; and a subproblem for active beamforming design and subcarrier allocation for mmW. In particular, we solve the first subproblem via the dueling deep Q-Network (DQN) learning approach by developing a distributed algorithm which leads to a better policy evaluation. Then, we solve the active beamforming design and subcarrier allocation for the mmW via the successive convex approximation (SCA) method. Simulation results exhibit the effectiveness of the proposed resource allocation scheme compared to other baseline schemes. In particular, it is revealed that by deploying UAVs-RISs, the transmit power can be reduced by 6 dBm while maintaining similar guaranteed QoS., Comment: This paper has got Major Revision by IEEE TWC

In this paper, we propose a novel joint intelligent trajectory design and resource allocation algorithm based on user's mobility and their requested services for unmanned aerial vehicles (UAVs) assisted networks, where UAVs act as nodes of a network function virtualization (NFV) enabled network. Our objective is to maximize energy efficiency and minimize the average delay on all services by allocating the limited radio and NFV resources. In addition, due to the traffic conditions and mobility of users, we let some Virtual Network Functions (VNFs) to migrate from their current locations to other locations to satisfy the Quality of Service requirements. We formulate our problem to find near-optimal locations of UAVs, transmit power, subcarrier assignment, placement, and scheduling the requested service's functions over the UAVs and perform suitable VNF migration. Then we propose a novel Hierarchical Hybrid Continuous and Discrete Action (HHCDA) deep reinforcement learning method to solve our problem. Finally, the convergence and computational complexity of the proposed algorithm and its performance analyzed for different parameters. Simulation results show that our proposed HHCDA method decreases the request reject rate and average delay by 31.5% and 20% and increases the energy efficiency by 40% compared to DDPG method.

This paper investigates the problem of age of information (AoI) aware radio resource management for a platooning system. Multiple autonomous platoons exploit the cellular wireless vehicle-to-everything (C-V2X) communication technology to disseminate the cooperative awareness messages (CAMs) to their followers while ensuring timely delivery of safety-critical messages to the Road-Side Unit (RSU). Due to the challenges of dynamic channel conditions, centralized resource management schemes that require global information are inefficient and lead to large signaling overheads. Hence, we exploit a distributed resource allocation framework based on multi-agent reinforcement learning (MARL), where each platoon leader (PL) acts as an agent and interacts with the environment to learn its optimal policy. Existing MARL algorithms consider a holistic reward function for the group's collective success, which often ends up with unsatisfactory results and cannot guarantee an optimal policy for each agent. Consequently, motivated by the existing literature in RL, we propose a novel MARL framework that trains two critics with the following goals: A global critic which estimates the global expected reward and motivates the agents toward a cooperating behavior and an exclusive local critic for each agent that estimates the local individual reward. Furthermore, based on the tasks each agent has to accomplish, the individual reward of each agent is decomposed into multiple sub-reward functions where task-wise value functions are learned separately. Numerical results indicate our proposed algorithm's effectiveness compared with the conventional RL methods applied in this area.

In this paper, we propose a Zero-Touch, deep reinforcement learning (DRL)-based Proactive Failure Recovery framework called ZT-PFR for stateful network function virtualization (NFV)-enabled networks. To this end, we formulate a resource-efficient optimization problem minimizing the network cost function including resource cost and wrong decision penalty. As a solution, we propose state-of-the-art DRL-based methods such as soft-actor-critic (SAC) and proximal-policy-optimization (PPO). In addition, to train and test our DRL agents, we propose a novel impending-failure model. Moreover, to keep network status information at an acceptable freshness level for appropriate decision-making, we apply the concept of age of information to strike a balance between the event and scheduling based monitoring. Several key systems and DRL algorithm design insights for ZT-PFR are drawn from our analysis and simulation results. For example, we use a hybrid neural network, consisting long short-term memory layers in the DRL agents structure, to capture impending-failures time dependency., Comment: Accepted by IEEE TNSM for publication Code is available in https://github.com/wildsky95/ZT-PFR

This paper studies a novel approach for successive interference cancellation (SIC) ordering and beamforming in a multiple antennas non-orthogonal multiple access (NOMA) network with multi-carrier multi-user setup. To this end, we formulate a joint beamforming design, subcarrier allocation, user association, and SIC ordering algorithm to maximize the worst-case energy efficiency (EE). The formulated problem is a non-convex mixed integer non-linear programming (MINLP) which is generally difficult to solve. To handle it, we first adopt the linearizion technique as well as relaxing the integer variables, and then we employ the Dinkelbach algorithm to convert it into a more mathematically tractable form. The adopted non-convex optimization problem is transformed into an equivalent rank-constrained semidefinite programming (SDP) and is solved by SDP relaxation and exploiting sequential fractional programming. Furthermore, to strike a balance between complexity and performance, a low complex approach based on alternative optimization is adopted. Numerical results unveil that the proposed SIC ordering method outperforms the conventional existing works addressed in the literature., Comment: Major Revision by IEEE TSP

In this paper, we design a novel scheduling and resource allocation algorithm for a smart mobile edge computing (MEC) assisted radio access network. Different from previous energy efficiency (EE) based or the average age of information (AAoI)-based network designs, we propose a unified metric for simultaneously optimizing ESE and AAoI of the network. To further improve the system capacity, non-orthogonal multiple access (NOMA) is proposed as a candidate for multiple access schemes for future cellular networks. Our main aim is to maximize the long-term objective function under AoI, NOMA, and resource capacity constraints using stochastic optimization. To overcome the complexities and unknown dynamics of the network parameters (e.g., wireless channel and interference), we apply the concept of reinforcement learning and implement a deep Q-network (DQN). Simulation results illustrate the effectiveness of the proposed framework and analyze different parameters impact on network performance. Based on the results, our proposed reward function converges fast with negligible loss value. Also, they illustrate our work outperforms the existing state of the art baselines up to 64% in the objective function and 51% in AAoI, which are stated as examples., Comment: Under review in IEEE for possible publication

In this paper, we propose a novel joint resource allocation and cooperative caching scheme for power-domain non-orthogonal multiple access (PD-NOMA)-based heterogeneous networks (HetNets). In our scheme, the requested content is fetched directly from the edge if it is cached in the storage of one of the base stations (BSs), and otherwise is fetched via the backhaul. Our scheme consists of two phases: 1. Caching phase where the contents are saved in the storage of the BSs, and 2. Delivery phase where the requested contents are delivered to users. We formulate a novel optimization problem over radio resources and content placement variables. We aim to minimize the network cost subject to quality-of-service (QoS), caching, subcarrier assignment, and power allocation constraints. By exploiting advanced optimization methods, such as alternative search method (ASM), Hungarian algorithm, successive convex approximation (SCA), we obtain an efficient sub-optimal solution of the optimization problem. Numerical results illustrate that our ergodic caching policy via the proposed resource management algorithm can achieve a considerable reduction on the total cost on average compared to the most popular caching and random caching policy. Moreover, our cooperative NOMA scheme outperforms orthogonal multiple access (OMA) in terms of the delivery cost in general with an acceptable complexity increase., Comment: 18 Page, Under Revise in IEEE Journal

After freezing the first phase of the fifth generation of wireless networks (5G) standardization, it finally goes live now and the rollout of the commercial launch (most in fixed 5G broadband services) and migration has been started. However, some challenges are arising in the deployment, integration of each technology, and the interoperability in the network of the communication service providers (CSPs). At the same time, the evolution of 5G is not clear and many questions arise such as whether 5G has long-term evolution or when 5G will change to a next-generation one. This paper provides long-term migration options and paths towards 5G considering many key factors such as the cost, local/national data traffic, marketing, and the standardization trends in the radio access network (RAN), the transport network (TN), the core network (CN), and E2E network. Moreover, we outline some 5G evolution road maps emphasizing on the technologies, standards, and service time lines. The proposed migration paths can be the answer to some CSPs concerns about how to do long-term migration to 5G and beyond., Comment: Migration, 5G, evolution, roadmap, option, path, 3 Figure, 4Table

In this paper, we propose a novel resource management scheme that jointly allocates the transmit power and computational resources in a centralized radio access network architecture. The network comprises a set of computing nodes to which the requested tasks of different users are offloaded. The optimization problem minimizes the energy consumption of task offloading while takes the end-to-end latency, i.e., the transmission, execution, and propagation latencies of each task, into account. We aim to allocate the transmit power and computational resources such that the maximum acceptable latency of each task is satisfied. Since the optimization problem is non-convex, we divide it into two sub-problems, one for transmit power allocation and another for task placement and computational resource allocation. Transmit power is allocated via the convex-concave procedure. In addition, a heuristic algorithm is proposed to jointly manage computational resources and task placement. We also propose a feasibility analysis that finds a feasible subset of tasks. Furthermore, a disjoint method that separately allocates the transmit power and the computational resources is proposed as the baseline of comparison. A lower bound on the optimal solution of the optimization problem is also derived based on exhaustive search over task placement decisions and utilizing Karush-Kuhn-Tucker conditions. Simulation results show that the joint method outperforms the disjoint method in terms of acceptance ratio. Simulations also show that the optimality gap of the joint method is less than 5%., Comment: 32 pages, 10 figures

In this paper, we design a drug release mechanism for dynamic time division multiple access (TDMA)-based molecular communication via diffusion (MCvD). In the proposed scheme, the communication frame is divided into several time slots over each of which a transmitter nanomachine is scheduled to convey its information by releasing the molecules into the medium. To optimize the number of released molecules and the time duration of each time slot (symbol duration), we formulate a multi-objective optimization problem whose objective functions are the bit error rate (BER) of each transmitter nanomachine. Based on the number of released molecules and symbol durations, we consider four cases, namely: "static-time static-number of molecules" (STSN), "static-time dynamic-number of molecules" (STDN), "dynamic-time static-number of molecules" (DTSN), and "dynamic-time dynamic-number of molecules" (DTDN). We consider three types of medium in which the molecules are propagated, namely: "mild diffusive environment" (MDE), "moderate diffusive environment" (MODE), and "severe diffusive environment" (SDE). For the channel model, we consider a 3-dimensional (3D) diffusive environment, such as blood, with drift in three directions. Simulation results show that the STSN approach is the least complex one with BER around $\text{10}^{\text{-2}}$, but, the DTDN is the most complex scenario with the BER around $\text{10}^{\text{-8}}$.

In this paper, we propose a new Multiple-Input Multiple-Output (MIMO) Molecular Communication (MC) system where multiple types of molecules are utilized for transmission and reception of information. We call the proposed framework as Multiple-type Transmission and Multiple-type Reception (MTMR). We also obtain the bit error rate (BER) of the system and an optimization problem is formulated to minimize BER by optimizing the drug dosage for designing drug release mechanism. As numerical analysis shows, the BER of MIMO-MTMR in MC is minimized to $\text{3.7}\times\text{10}^{\text{-3}}$ by considering the budget of molecules as 10000. Furthermore, MIMO-MTMR outperforms Single-type Transmission Single-type Reception MIMO from the BER performance point of view approximately 54% for time slot 10s.

To fend off network ossification and support high degrees of flexibility and various services, network virtualization and slicing are introduced for the next-generation wireless cellular networks. These two technologies allow diversifying attributes of the future inter-networking and time-varying workloads based resource management paradigms. In this paper, we propose an end-to-end (E2E) resource allocation framework for future networks considering radio and core network by leveraging network function virtualization (NFV). We aim to minimize the network cost defined based on the utilized energy and spectrum while providing E2E quality of service (QoS) for diverse services with stringent QoS requirements. This goal is realized by formulating a novel optimization problem which performs the power and spectrum allocation in radio, and service function chaining and scheduling in the NFV environment while guaranteeing the distinct QoS constraints of the requested services. The proposed optimization problem is mixed-integer non-linear programming, which is a non-convex and NP-hard problem. To solve it, we adopt an iterative algorithm with novel admission control and a greedy-based heuristic algorithm, which is shown to have a polynomial order of complexity with 13:66% global optimality gap on average for a small scaled network. To validate the proposed framework, simulation results are carried out by considering different values of the network parameters and topologies. Moreover, our proposed framework and solution algorithm are assessed and compared with the existing works. Simulation results demonstrate that the proposed heuristic algorithm and framework outperforms the existing ones by 34% on average in cost reduction., Comment: Under review in IEEE

In this paper, a new queuing model for the Tactile Internet (TI) is proposed for the cloud radio access network (C-RAN) architecture of the next generation wireless networks, e.g., 5G, assisted via orthogonal frequency division multiple access (OFDMA) technology. This model includes both the radio remote head (RRH) and baseband processing unit (BBU) queuing delays and reliability for each end to end (E2E) connection between each pair of tactile users. For this setup, with the aim to minimize the transmit power of users subject to guaranteeing tolerable delay of users, and fronthaul and access limitations, we formulate a resource allocation problem. Since the proposed optimization problem is highly non-convex, to solve it in an efficient manner, we utilize diverse transformation techniques such as successive convex approximation (SCA) and difference of two convex functions (DC). In addition, we propose an admission control (AC) algorithm to make problem feasible. In our proposed system model, we dynamically adjust the fronthaul and access links to minimize the transmit power. Simulation results reveal that by dynamic adjustment of the access and fronthaul delays, transmit power can be saved compared to the case of fixed approach per each transmission session. Moreover, the number of rejected users in the network is significantly reduced and more users are accepted., Comment: arXiv admin note: substantial text overlap with arXiv:1901.09389

In this paper, a novel non-uniform Binary Concentration Shift Keying (BCSK) modulation in the course of molecular communication is introduced. We consider the nutrient limiting as the main reason for avoiding the nanotransmitters to release huge number of molecules at once. The solution of this problem is in the utilization of the BCSK modulation. In this scheme, nanotransmitter releases the information molecules non-uniformly during the time slot. The 3-dimensional diffusion channel with 3-dimensional drift is considered in this paper. To boost the bit error rate (BER) performance, we consider a relay-assisted molecular communication via diffusion. Our computations demonstrate how the pulse shape of BCSK modulation affects the BER, and we also derive the energy consumption of non-uniform BCSK in the closed-form expression. We study the parameters that can affect the BER performance, in particular the distance between the nanotransmitter and the nanoreceiver, the drift velocity of the medium, and the symbol duration. Furthermore, we propose an optimization problem that is designed to find the optimal symbol duration value that maximizes the number of successful received bits. The proposed algorithm to solve the optimization problem is based on the bisection method. The analytical results show that non-uniform BCSK modulation outperforms uniform BCSK modulation in BER performance, when the aggregate energy is fixed.

In this paper, we study the problem of resource allocation as well as pricing in the context of Internet of things (IoT) networks. We provide a novel pricing model for IoT services where all the parties involved in the communication scenario as well as their revenue and cost are determined. We formulate the resource allocation in the considered model as a multi-objective optimization problem where in addition to the resource allocation variables, the price values are also optimization variables. To solve the proposed multi-objective optimization problem, we use the scalarization method which gives different Pareto optimal solutions. We solve the resulting problems using the alternating approach based on the successive convex approximation (SCA) method which converges to a local solution with few iterations. We also consider a conventional approach where each entity tries to maximize its own revenue independently. Simulation results indicate that by applying the proposed joint framework, we can increase the total revenue compared to the conventional case while providing an almost complete fairness among the players. This is while the conventional approach fails to provide such a fairness.

In this paper, we consider a hybrid scheme of coordinated multi-point (CoMP) technology in MISO heterogeneous communication networks based on power domain non-orthogonal multiple access (PD-NOMA). We propose a novel method based on matching game with externalities to realize the hybrid scheme where the number of the cooperative nodes are variable. Moreover, we propose a new matching utility function to manage the interference caused by CoMP and NOMA techniques. We also devise robust beamforming to cope with the channel uncertainty. In this regard, we focus on both no CSI and partial CSI cases to increase the achievable data rate. We provide the complexity analysis of both schemes which shows that the complexity of the partial CSI approach is more than that of the no CSI method. Results evaluate the performance of proposed CoMP scheme and the sensibility of our methods, Index Terms, CoMP technology, hybrid scheme, matching game with externalities, PD-NOMA, robust beamforming, probabilistic constraint, no CSI, partial CSI.

Ultra-low latency is the most important requirement of the Tactile Internet (TI), which is one of the proposed services for the next-generation wireless network (NGWN), e.g., fifth generation (5G) network. In this paper, a new queuing model for the TI is proposed for the cloud radio access network (CRAN) architecture of the NGWN by applying power domain non-orthogonal multiple access (PD-NOMA) technology. In this model, we consider both the radio remote head (RRH) and baseband processing unit (BBU) queuing delays for each end-to-end (E2E) connection between a pair of tactile users. In our setup, to minimize the transmit power of users subject to guaranteeing an acceptable delay of users, and fronthaul and access constraints, we formulate a resource allocation (RA) problem. Furthermore, we dynamically set the fronthaul and access links to minimize the total transmit power. Given that the proposed RA problem is highly non-convex, in order to solve it, we utilize diverse transformation techniques such as successive convex approximation (SCA) and difference of two convex functions (DC). Numerical results show that by dynamic adjustment of the access and fronthaul delays, transmit power reduces in comparison with the fixed approach per each connection. Also, energy efficiency of orthogonal frequency division multiple access (OFDMA) and PD-NOMA are compared for our setup.

Telemedicine refers to the use of information and communication technology to assist with medical information and services. In health care applications, high reliable communication links between the health care provider and the desired destination in the human body play a central role in designing end-to-end (E2E) telemedicine system. In the advanced health care applications, $\text{e.g.}$ drug delivery, molecular communication becomes a major building block in bio-nano-medical applications. In this paper, an E2E communication link consisting of the electromagnetic and the molecular link is investigated. This paradigm is crucial when the body is a part of the communication system. Based on the quality of service (QoS) metrics, we present a closed-form expression for the E2E BER of the combination of molecular and wireless electromagnetic communications. \textcolor{black}{ Next, we formulate an optimization problem with the aim of minimizing the E2E BER of the system to achieve the optimal symbol duration for EC and DMC regarding the imposing delivery time from telemedicine services.} The proposed problem is solved by an iterative algorithm based on the bisection method. Also, we study the impact of the system parameters, including drift velocity, detection threshold at the receiver in molecular communication, on the performance of the system. Numerical results show that the proposed method obtains the minimum E2E bit error probability by selecting an appropriate symbol duration of electromagnetic and molecular communications.