Aerial base stations (ABSs) extend the coverage of internet of things smart devices (ISDs) beyond terrestrial networks; however, ultra-reliable and low-latency communication (URLLC) is constrained by limited battery life and computational resources. To address this, we propose an aerial-terrestrial non-orthogonal multiple access (NOMA) framework that decouples the non-convex problem into feasible sub-problems: (i) optimal clustering via k-means with elbow method and F-test method, alongside a modified pathloss model, (ii) reinforcement learning based ABS placement, and (iii) hybrid deep-learning and fractional transmit power allocation (PA) for power efficiency and fairness. We also derive a closed-form expression for PA among multiplexed devices based on their QoS requirements. Results show that the proposed scheme outperforms benchmark schemes, i.e., the sum-rate for NOMA-DeepFusion-PA [Optimal UAV position] can be increased by 28.5762% than NOMA with a fixed PA method, namely: NOMA-FPA [Optimal UAV position], and 38.3119% higher than orthogonal multiple access (OMA) [Optimal UAV position] for different transmit powers.

This paper investigates the synergistic potential of reconfigurable intelligent surfaces (RIS) and non-orthogonal multiple access (NOMA) to enhance the energy efficiency and performance of next-generation wireless networks. We delve into the design of energy-efficient passive beamforming (PBF) strategies within RIS-assisted coordinated multi-point (CoMP)-NOMA networks. Two distinct RIS configurations, namely, enhancementonly PBF (EO) and enhancement & cancellation PBF (EC), are proposed and analyzed. Our findings demonstrate that RISassisted CoMP-NOMA networks offer significant efficiency gains compared to traditional CoMP-NOMA systems. Furthermore, we formulate a PBF design problem to optimize the RIS phase shifts for maximizing energy efficiency. Our results reveal that the optimal PBF design is contingent upon several factors, including the number of cooperating base stations (BSs), the number of RIS elements deployed, and the RIS configuration. This study underscores the potential of RIS-assisted CoMP-NOMA networks as a promising solution for achieving superior energy efficiency and overall performance in future wireless networks.

Towards addressing emerging energy challenges posed by unfair heterogeneous Semantic Communication (SC) codec updates within future wireless networks, this paper presents a novel Anti-discrimination Federated learning (AdFed) approach. Inspired by the economics of discrimination, unique fairness-associated energy concerns in SC systems are formulated as model discrimination challenges, with the SC-deployed wireless network conceptualized as an anti-discrimination labor market. A novel “affirmative action” strategy, based on training epochs, is proposed and adopted according to historical training unfairness results. To address the reverse discrimination issues in “affirmative action” caused by quota fairness impacting training energy cost, we formulate this problem as a coupled integer non-linear programming problem. Moreover, a new quota trade-off mechanism based on the Rubinstein bargaining game is also designed. Simulation results verify that AdFed outperforms SC training baselines, effectively addressing the unique model discrimination challenges of SC codec model heterogeneity updating. The efficacy of the game theoretical trade-off mechanism is demonstrated in achieving optimal outcomes.

As Beyond 5thGeneration (B5G) networks expand, securing communication links remains a challenge. Physical Layer Security (PLS) is a promising paradigm, and this work explores wireless channel-based Secret Key Generation (SKG) by proposing an Extended Huffman Coding (EHC) framework to enhance quantization stage. While non-uniform coding is common in Analog-to-Digital (A2D) conversion, it remains largely unexplored in SKG due to key mismatch risks. To address this, we propose a Huffman Coding (HC)-inspired framework that generates binary codes using reversed HC principles and extends them by sequentially altering the Least Significant Bit (LSB), ensuring equal-length unique codes for quantization levels while enhancing generation rate, agreement, and randomness. Additionally, histogram/ Probability Density Function (PDF) matching aligns quantization level probabilities at legitimate nodes to minimize mismatches while exchanging minimal statistical information without compromising secrecy. Unlike conventional quantization, which relies on predetermined codes vulnerable to eavesdropping, our scheme dynamically computes quantization codes at runtime, adding an extra layer of security. Moreover, while conventional HC avoids codes of a level to be a subcode of another level, SKG benefits when one code is a subcode of another, making detection harder. Instead of compression, as desired in conventional A2D conversion, SKG requires longer and random keys that reliably match at legitimate nodes, which our scheme ensures. Simulations are conducted under Rice-fading conditions that evaluate the impact of variations in the Rician K-factor on SKG performance. The proposed method is assessed using key SKG metrics named: Key Agreement Rate (KAR), Key Generation Rate (KGR), and Key Randomness Rate (KRR) (employing the NIST test suite). The obtained results demonstrate the potential of our proposed scheme for secure and scalable SKG solutions in future networks.

To navigate the carbon emission and functional challenges associated with edge caching within heterogeneous Industrial Internet of Things (IIoT) spanning energy use, cache hit rate, and bandwidth usage, this paper proposes a novel Game Theory Empowered Carbon-Intelligent Federated Multi-Edge Caching framework (GT-FMC). The proposed framework enables distributed collaborative caching by intelligently coordinating edge nodes to optimize content decisions while efficiently integrating content providers (CPs), edge nodes, and users with energy-aware strategies. In GT-FMC, a lightweight federated content popularity prediction method based on Temporal Convolutional Networks (TCN) is introduced to collaboratively learn global content popularity while reducing prediction energy cost. The energy-aware utilities of the three involved parties are jointly formulated as a coupled non-linear optimization problem. To address this challenge, a two-stage game-theoretic algorithm is designed. Experimental results on a real-world testbed show that GT-FMC achieves up to 77.9% of Oracle in cache hit rate and 10.6%–32.4% reduction in transmission energy consumption compared to baseline methods. Complementary evaluations also validate the game-theoretic design’s effectiveness.

In mobile crowd sensing (MCS), complex tasks often require collaboration among multiple workers with diverse expertise and sensors. However, few studies consider the sensing time redundancy of multiple workers to complete a task collaboratively, and the subjective and objective collaboration willingness of participating workers in forming collaboration groups for different tasks. If solely focusing on enhancing workers’ willingness to collaborate, it cannot guarantee the minimum time redundancy within the collaboration group, resulting in a decrease in the group’s efficiency. Similarly, if only aiming to reduce sensing time redundancy among the workers in the collaboration group, it may lead to a loss of workers’ willingness to collaborate, and the diminished motivation among workers will consequently reduce the group’s efficiency. To address these challenges, this paper proposes EGC-STRO, a method for forming efficient collaboration groups in MCS that optimizes sensing time redundancy while balancing the workers’ cooperation willingness as constraints. First, this method proposes an evaluation indicator to select workers who meet their reward expectations, i.e., objective collaboration willingness, and uses an incentive mechanism based on bargaining game to maximize the overall interests. Furthermore, subjective collaboration willingness is defined and a collaboration worker selection algorithm is designed. The algorithm adds workers who meet both subjective and objective willingness requirements to the candidate set and selects workers with the smallest sensing redundancy time in the worker candidate set to join the final collaboration group. Simulation results demonstrate that compared with the baseline methods, our proposed EGC-STRO increases the worker engagement by about 5%-20%, increases the task coverage by 6%-25%, increases the platform utility by 17%-50%, and increases the worker utility by 20%-60%.

Machine learning-based semantic communication is a promising enabler for future-generation wireless network systems such as 6G networks. In practice, effective semantic communication requires online training for unknown content. In highly mobile vehicular networks, however, reliable, and efficient model training becomes significantly challenging. The existing distributed learning approaches are also unable to effectively operate in highly dynamic vehicular semantic communication networks. To address these challenges, we propose a novel mobility-aware split-federated with transfer learning (MSFTL) framework based on vehicle task offloading scenarios in this paper. To enable adaptation to the complex vehicle semantic communication, the proposed framework divides the training of the model into four parts and uses the proposed new splitfederated learning. Furthermore, to improve training efficiency, model accuracy, and the ability to adapt in highly mobile environments, we also present a new transfer learning approach integrated into the proposed framework. Particularly, we propose a high-mobility training resource optimisation mechanism based on a Stackelberg game for MSFTL to further reduce training costs and adapt vehicle mobility scenarios. We also investigate the performance of the proposed schemes through extensive simulations. The results validate the proposed approach and indicate its superiority compared to the conventional learning frameworks for semantic communication in vehicular networks.

The low earth orbit (LEO) satellite-borne edge cloud (SEC) and machine learning (ML) based semantic communication (SemCom) are both enabling technologies for 6G systems facilitating computation offloading. Nevertheless, integrating SemCom into the SEC networks for user computation offloading introduces semantic coder updating requirements as well as additional semantic extraction costs. Offloading user computation in SEC networks via SemCom also results in new functional challenges considering, e.g., latency, energy, and privacy. In this paper, we present a novel SemCom-assisted SEC (SemCom-SEC) framework for computation offloading of resource-limited users. We then propose an adaptive pruning-split federated learning (PSFed) method for updating the semantic coder in SemCom-SEC. We further show that the proposed method guarantees training convergence speed and accuracy. This method also improves the privacy of the semantic coder while reducing training delay and energy consumption. In the case of trained semantic coders in service, for the users processing computational tasks, the main objective is to minimise the users’ delay and energy consumption, subject to sustaining users’ privacy and fairness amongst them. This problem is then formulated as an incomplete information mixed integer nonlinear programming (MINLP) problem. A new computational task processing scheduling (CTPS) mechanism is also proposed based on the Rubinstein bargaining game. Simulation results demonstrate the proposed PSFed and game theoretical CTPS mechanism outperforms the baseline solutions reducing delay and energy consumption while enhancing users’ privacy.

Network performance and throughput is affected by network congestion, which is caused by unnecessary bandwidth over-utilisation, expanding transmission delays, and increase in cost. Fog computing has emerged as a promising solution to overcome these shortcomings by provisioning computational resources to the network’s edge. However, selecting suitable fog nodes can pose challenges due to increased latency and high energy consumption, leading to unnecessary bandwidth utilisation. This study proposes a deep learning mechanism called gated graph convolution neural networks (GGCNs) for resource scheduling management in fog computing to improve the average loop delay and the total network usage of the system. Our deep learning mechanism promotes energy-efficient collaborative intelligence among IoT devices while optimising resource utilisation. Reducing energy consumption not only promotes but also enhances sustainability and scalability in IoT networks. Our proposed mechanism shows improved results compared with several benchmark algorithms, such as first come first serve, shortest job first, and particle swarm optimisation. Our results demonstrate that the proposed model will resolve the problem of application placement and present a noticeable reduction in delay and bandwidth. The results can prove to be a standard benchmark in the IoT-Fog computing discipline and used to enhance the quality of service in wide-ranging heterogeneous applications located at distributed locations.

Spectrum sensing is a key technique for dynamically detecting available spectrum in cognitive radio networks (CRNs), which can introduce high resource demands such as energy consumption. In this paper, we propose a novel cluster-based cooperative sensing-after-prediction scheme where a learning cluster and a sensing cluster are jointly considered to perform cooperative prediction and sensing efficiently. This enables us to skip the complex physical sensing to reduce the demands when the spectrum availability can be simply predicted using cooperative prediction. Furthermore, the clustering is flexible, in order to meet different performance requirements. We then formulate two optimization problems to minimize the total number of users in the two clusters or to minimize the total energy consumption, to meet different performance requirements, while in both cases guaranteeing the system accuracy requirement and individual energy constraints. To solve the two challenging integer programming problems, the unconstrained problems are mathematically solved first by relaxing the integer variable and fixing the cluster size. Such analytical solutions serve as a foundation for solving the original optimization problems. Then, two low-complexity search algorithms are proposed to achieve the global optimum, as they can obtain the same performance with exhaustive search. Simulation results validate the accuracy of the derived analytical expressions and demonstrate that the total energy consumption and the number of users contributing to learning and sensing can be greatly reduced by applying our optimized clustered sensing-after-prediction scheme.

Beyond 5G networks require low latency, high throughput and high data rates while maintaining appreciable coverage. To achieve this, a wider bandwidth is required. Terahertz (THz) band can provide such large bandwidth, however, it is not as reliable as the sub 6 GHz band due to absorption effects. Hence, we need infrastructure level approaches such as heterogeneous networks (HetNet) that provide backwards compatibility to increase coverage and reliability. In this paper, we consider a HetNet comprised of small base stations at THz and mmWave frequencies, and macro base station at sub-6 GHz frequency at different building densities based on multiple cities around the world. For quality of service (QoS) performance metrics, we take data rate coverage and power efficiency. Different system parameters are varied for six different locations to analyze the effectiveness of the proposed HetNet. Our results show that B5G networks are considerably more effective in environments with low building densities.

In the last decade, a sharp surge in the number of user proximity wireless devices (UPWDs) has been observed. This has increased the level of electromagnetic field (EMF) exposure of the users substantially and hence, the possible physiological effects. Ambient backscatter communications (ABC) has appeared to be a promising solution to reduce the power consumption of UPWDs by converting ambient radio frequency (RF) signals into useful signals while non-orthogonal multiple access (NOMA) is a compelling multiplexing scheme for enhanced spectral efficiency. This paper utilises a novel combination of ABC and NOMA to reduce the EMF in the uplink of wireless communication systems. This contemporary approach of EMF-aware resource optimization is based on k-medoids and Silhouette analysis. To curtail the uplink EMF, a power allocation strategy is also derived by converting a non-convex problem to a convex one and solving accordingly. The numerical results exhibit that the proposed ABC, NOMA, and unsupervised learning based scheme achieves a reduction in the EMF by at least 75% in comparison to the existing solutions.

Non-orthogonal multiple access (NOMA) is a promising radio access technique that enables massive connectivity and increased spectral efficiency. The deployment of aerial base stations (ABSs) as a relay is also an optimistic goal that fairly serves a large number of internet of things (IoT) devices. On one side, ABS-assisted communication leverages effective communication services for secondary IoT devices in smart cities. On the other hand, NOMA allows several IoT devices to concurrently acquire the same frequency-time resource. To this end, weighted sum-rate (WSR) is an essential goal because it allows numerous trade-offs between user fairness and sum-rate efficiency. Therefore, this work aims to investigate the WSR for an integrated aerial terrestrial network subject to cellular power and delay constraints in downlink NOMA. Herein, a theoretical insight-based low-complexity iterative solution is provided for optimal power and blocklength allocation to achieve maximum sum-rate. For this purpose, the mixed-integer non-linear problem is formulated and a low-complexity near-optimal solution is proposed. Numerical results show that the proposed scheme achieves a near-optimal solution and outperforms baseline techniques, i.e., the performance gain of 5.18% over the legacy OMA system for NOMA with two IoT devices per subcarrier.

Fog computing-based radio access network (Fog-RAN) leveraging the software-defined networking (SDN) and network function virtualization (NFV) is the most promising solution to offer real-time support for the massive number of connected devices in the industrial Internet of Things (IIoT) networks. However, designing an optimal dynamic radio resource allocation to handle the fluctuating traffic loads is critical. In this article, a novel architectural design of an SDN-based virtual Fog-RAN is proposed, in which we jointly study radio resource allocation and transmit beamforming to improve resource utilization and IIoT users’ satisfaction, by minimizing the network power consumption (NPC) and maximizing the achievable sum-rate (ASR), simultaneously. To this end, we first formulate a mixed-integer nonlinear problem to optimize the physical resource block allocation, the assignment of user equipments, and radio unit, and the downlink transmit beamforming, by considering imperfect channel state information. To solve the ntractable MINLP, we exploit the successive convex approximation approach. Then, we formulate a multiple knapsack problem (MKP) to optimize the assignment between RUs and virtual baseband units, by exploiting the set of active RUs minimized in the previous problem. We solve the formulated MKP by decomposing the dual problems and solving them through the dual descent method. Through performance analysis, we show the proposed approach provides a high users’ satisfaction rate, maximizes the ASR and minimizes the NPC, and provides better savings, in terms of the number of radio and baseband resources utilized, than its counterparts.

Heterogeneous backscatter networks are emerging as a promising solution to address the proliferating coverage and capacity demands of next-generation vehicular networks. However, despite its rapid evolution and significance, the optimization aspect of such networks has been overlooked due to their complexity and scale. Motivated by this discrepancy in the literature, this work sheds light on a novel learning-based optimization framework for heterogeneous backscatter vehicular networks. More specifically, the article presents a resource allocation and user association scheme for large-scale heterogeneous backscatter vehicular networks by considering a collaboration centric spectrum sharing mechanism. In the considered network setup, multiple network service providers (NSPs) own the resources to serve several legacy and backscatter vehicular users in the network. For each NSP, the legacy vehicle user operates under the macro cell, whereas, the backscatter vehicle user operates under small private cells using leased spectrum resources. A joint power allocation, user association, and spectrum sharing problem has been formulated with an objective to maximize the utility of NSPs. In order to overcome challenges of high dimensionality and non-convexity, the problem is divided into two subproblems. Subsequently, a reinforcement learning and a supervised deep learning approach have been used to solve both subproblems in an efficient and effective manner. To evaluate the benefits of the proposed scheme, extensive simulation studies are conducted and a comparison is provided with benchmark techniques. The performance evaluation demonstrates the utility of the presented system architecture and learning-based optimization framework.

Heterogeneous networks (HetNets) are becoming a promising solution for future wireless systems to satisfy the high data rate requirements. This paper introduces a stochastic geometry framework for the analysis of the downlink coverage probability in a multi-tier HetNet consisting of a macro-base station (MBS) operating at sub-6 GHz, millimeter wave (mmWave)-enabled unmanned aerial vehicles (UAVs) operating at 28 GHz, and small BSs operating both at mmWave and THz frequencies. The analytical expressions for the coverage probability for each tier have been derived in the paper. Monte Carlo simulations are then performed to validate the analytical expressions. The effectiveness of the HetNet is analyzed on various performance metrics including association and coverage probabilities for different network parameters. We show that the mmWave and THz-enabled cells provide significant improvement in the achievable data rates because of their high available bandwidths, however, they have a degrading effect on the coverage probability due to their high propagation losses.

Software-defined networking (SDN)-based virtual Fog computing radio access network (Fog-RAN) architecture is known as a potential solution to cope with massive traffic loads in 5G and beyond networks. Dynamic radio and computational resource allocation is needed to handle the fluctuating traffic loads, aiming to reduce power consumption and enhancing user satisfaction. In this paper, we propose a dynamic resource allocation for a SDN-based virtual Fog-RAN. We formulate a mixed-integer non-linear problem to minimize the network power consumption by optimizing the user equipment (UE) - radio units (RUs) association, the physical resource block (PRB) allocation, and the RU power allocation, according to the real-time traffic loads. Then, exploiting the obtained results in the previous problem, we formulate a multiple knapsack problem to optimize the assignment between the active RUs and the virtual baseband units (vBBUs). Finally, we perform a simulation study to analyse the impact of the proposed resource allocation on network power consumption, vBBUs resources savings, and user satisfaction so to validate the performance gains achieved.

Positions of primary and secondary users and the distance between them in a cognitive radio network play an important role in the interference caused by the secondary network to the users in the primary network. In this work, we exploit cooperative localization to estimate the distance between the nodes and then use transmission power control for interference avoidance. The distance between the primary and secondary users is estimated using a cooperative model. Next, the transmission power of the secondary user is adjusted so that it does not cause interference to the primary user. The proposed algorithm is verified by simulations. Results show that the primary user achieves considerable gain in the throughput as a result of increase in the signal to interference and noise ratio due to reduction in the interference caused by the secondary network.

Abstract In vehicular ad hoc networks (VANETs), transmission power is a key factor in several performance measures, such as throughput, delay, and energy efficiency. Vehicle mobility in VANETs creates a highly dynamic topology that leads to a nontrivial task of maintaining connectivity due to rapid topology changes. Therefore, using fixed transmission power adversely affects VANET connectivity and leads to network performance degradation. New cross‐layer power control algorithms called (BL‐TPC 802.11MAC and DTPC 802.11 MAC) are designed, modeled, and evaluated in this paper. The designed algorithms can be deployed in smart cities, highway, and urban city roads. The designed algorithms improve VANET performance by adapting transmission power dynamically to improve network connectivity. The power adaptation is based on inspecting some network parameters, such as node density, network load, and media access control (MAC) queue state, and then deciding on the required power level. Obtained results indicate that the designed power control algorithm outperforms the traditional 802.11p MAC considering the number of received safety messages, network connectivity, network throughput, and the number of dropped safety messages. Consequently, improving network performance means enhancing the safety of vehicle drivers in smart cities, highway, and urban city.

Abstract Fog computing is a promising technique to reduce the latency and power consumption issues of the Internet of Things (IoT) ecosystem by enabling storage and computational resource close to the end‐user devices with additional benefits such as improved execution time and processing. However, with an increase in IoT devices, the resource allocation and job scheduling became a complicated and cumbersome task due to limited and heterogeneous resources along with the locality restriction in such computing environment. Therefore, this paper proposes a cache‐based approach for efficient resource allocation in fog computing environment, while maintaining the quality of service. The proposed algorithm is realized using iFogSim simulator and a comprehensive comparison is presented with the traditional First Come First Served and Shortest Job First policies. The performance evaluation revealed that with the proposed scheme the execution time, latency, processing delays and power consumption decreased by 38%, 11.1%, 6%, and 17.8%, respectively, as compared to those of the traditional schemes.

Summary There is an abundant expansion in the race of technology, specifically in the production of data, because of the smart devices, such as mobile phones, smart cards, sensors, and Internet of Things (IoT). Smart phones and devices have undergone an enormous evolution in a way that they can be used. More and more new applications, such as face recognition, augmented reality, online interactive gaming, and natural language processing are emerging and attracting the users. Such applications are generally data intensive or compute intensive, which demands high resource and energy consumption. Mobile devices are known for the resource scarcity, having limited computational power and battery life. The tension between compute/data intensive application and resource constrained mobile devices hinders the successful adaption of emerging paradigms. In the said perspective, the objective of this article is to study the role of computation offloading in mobile cloud computing to supplement mobile platforms ability in executing complex applications. This article proposes a systematic approach (EFFORT) for offload communication in the cloud. The proposed approach provides a promising solution to partially solve energy consumption issue for communication‐intensive applications in a smartphone. The experimental study shows that our proposed approach outperforms its counterparts in terms of energy consumption and fast processing of smartphone devices. The battery consumption was reduced to 19% and the data usage was reduced to 16%.

Mobile Crowd Sensing is an emerging sensing paradigm that employs massive number of workers' mobile devices to realize data collection. Unlike most task allocation mechanisms that aim at optimizing the global system performance, stable matching considers workers are selfish and rational individuals, which has become a hotspot in MCS. However, existing stable matching mechanisms lack deep consideration regarding the effects of workers' competition phenomena and complex behaviors. To address the above issues, this paper investigates the competition-congestion-aware stable matching problem as a multi-objective optimization task allocation problem considering the competition of workers for tasks. First, a worker decision game based on congestion game theory is designed to assist workers in making decisions, which avoids fierce competition and improves worker satisfaction. On this basis, a stable matching algorithm based on extended deferred acceptance algorithm is designed to make workers and tasks mapping stable, and to construct a shortest task execution route for each worker. Simulation results show that the designed model and algorithm are effective in terms of worker satisfaction and platform benefit.

In an effort towards designing a batteryless Internet of Things (IoT) sensor node that is powered by miniaturized energy-harvesting source(s), we combine the techniques of transient computing, approximate computing, data and energy predictions so as to handle the unpredictable power shortages of the miniaturized energy harvesting sources and reduce the overall power consumption of the IoT node. To evaluate the feasibility of our proposed approach, we build upon and extend an existing platform that consists of a peer-to-peer network (a sender node and a receiver node) where each of these nodes combines a Texas Instruments' FRAM-based micro-controller with a low cost, low power radio module and exchanging its data through SimpliciTI protocol. Our results illustrate that combining transient computing, approximate computing, data and energy predictions adds up their individual benefits to achieve an overall better utilization of the harvested energy of the node. Our results show that out of the total 60 transmissions that were due in an interval of 5 hours, for sending the temperature data from sender node to the receiver node every 5 minutes, a total of 32 transmissions were avoided, leading to a saving of more than 50% of the radio transmissions in the sender node.

To improve data speed and reliability, hybrid wireless networks combine two different Radio Access Technologies (RATs), such as Visible Light Communications (VLC), millimetre wave (mmWave), Wireless Fidelity (WiFi), 4G Long Term Evolution (LTE), etc. The Internet of Radio Light (IoRL) is a cutting-edge system paradigm to combine three RATs for taking advantage the vast VLC and mmWave spectrum with the ubiquitous coverage of WiFi. In this respect, this work introduces a new convex optimisation-based solution method to optimise the three-dimensional (3D) Access Point Assignment (APA) problem of the IoRL system under individual user positioning, priority and minimum Quality-of-Service (QoS) constraints. We use both the IoRL real-world testbed and large-scale Maltab simulations to evaluate that our solution converges in linear time, and attains higher throughput-vs-fairness trade-off than existing efforts.

The foreseen enormous generation of mobile data would result in congestion of the spectrum available. To efficiently use the available spectrum new paradigm named fog computing is a promising solution. In this paper, we developed a fog-IoT network to provide an ε-optimal resource allocation to maximize the overall network throughput. A joint cloudlet selection and power allocation problem is formulated under association and Quality-of-Service (QoS) constraints. The formulated problem falls in class of mixed-integer nonlinear programming (MINLP) problem which is NP-hard generally. We solved our problem by applying a less complex linearization technique that uses the outer approximation algorithm (OAA). Resource allocation and power allocation are efficiently conducted as a result of this optimization, which is less complicated compared to exhaustive search.

Low latency is a critical requirement of beyond 5G services. Previously, the aspect of latency has been extensively analyzed in conventional and modern wireless networks. With the rapidly growing research interest in wireless-powered ambient backscatter communications, it has become ever more important to meet the delay constraints, while maximizing the achievable data rate. Therefore, to address the issue of latency in backscatter networks, this paper provides a deep Q-learning based framework for delay constrained ambient backscatter networks. To do so, a Q-learning model for ambient backscatter scenario has been developed. In addition, an algorithm has been proposed that employ deep neural networks to solve the complex Q-network. The simulation results show that the proposed approach not only improves the network performance but also meets the delay constraints for a dense backscatter network.

With advances in fog and edge computing, various problems such as data processing for large Internet of Things (IoT) systems can be solved in an efficient manner. One such problem for the next generation smart grid (SG) IoT system comprising of millions of smart devices is the data aggregation problem. Traditional data aggregation schemes for SGs incur high computation and communication costs, and in recent years, there have been efforts to leverage fog computing with SGs to overcome these limitations. In this article, a new fog-enabled privacy-preserving data aggregation scheme (FESDA) is proposed. Unlike existing schemes, the proposed scheme is resilient to false data injection attacks by filtering out the inserted values from external attackers. To achieve privacy, a modified version of the Paillier cryptosystem is used to encrypt the consumption data of the smart meter (SM) users. In addition, FESDA is fault-tolerant, which means, the collection of data from other devices will not be affected even if some of the SMs malfunction. We evaluate its performance along with three other competing schemes in terms of aggregation, decryption, and communication costs. The findings demonstrate that FESDA reduces the communication cost by 50%, when compared with the privacy-preserving fog-enabled data aggregation scheme.

This paper investigates a secure wireless-powered sensor network (WPSN) with the aid of a cooperative jammer (CJ). A power station (PS) wirelessly charges for a user equipment (UE) and the CJ to securely transmit information to an access point (AP) in the presence of multiple eavesdroppers. Also, the CJ are deployed, which can introduce more interference to degrade the performance of the malicious eavesdroppers. In order to improve the secure performance, we formulate an optimization problem for maximizing the secrecy rate at the AP to jointly design the secure beamformer and the energy time allocation. Since the formulated problem is not convex, we first propose a global optimal solution which employs the semidefinite programming (SDP) relaxation. Also, the tightness of the SDP relaxed solution is evaluated. In addition, we investigate a worst-case scenario, where the energy time allocation is achieved in a closed form. Finally, numerical results are presented to confirm effectiveness of the proposed scheme in comparison to the benchmark scheme.

We investigate the robust beamforming design for a simultaneous wireless information and power transfer (SWIPT) enabled system, with the cooperative non-orthogonal multiple access (NOMA) protocol applied. A novel cooperative NOMA scheme is proposed, where a strong user with better channel conditions adopts power splitting (PS) scheme and acts as an energy-harvesting relay to forward the decoded signal to the weak user. The presence of channel uncertainties is considered by introducing the outage-based constraints of signal to interference plus noise ratio (SINR). Specifically, it is assumed that only imperfect channel state information (CSI) is known at the base station (BS), due to the reason that the BS is far away from both users and suffers serious feedback delay. Our aim is to maximize the strong user's data rate, by optimally designing the robust transmit beamforming and PS ratio, while guaranteeing the correct decoding of the weak user. The proposed formulation yields to a challenging nonconvex optimization problem. To solve it, we first approximate the probabilistic constraints with the Bernstein-type inequalities, which can then be globally solved by two-dimensional exhaustive search. To further reduce the complexity, an efficient low-complexity algorithm is proposed with the aid of successive convex approximation (SCA). Numerical results show that the proposed algorithm converges quickly, and the proposed SWIPT-enabled robust cooperative NOMA system achieves better performance than existing protocols.

This work presents a novel method to generate secret keys shared between a legitimate node pair (Alice and Bob) to safeguard the communication between them from an unauthorized node (Eve). To this end, we exploit the reciprocal carrier frequency offset (CFO) between the legitimate node pair to extract common randomness out of it to generate shared secret keys. The proposed key generation algorithm involves standard steps: the legitimate nodes exchange binary phase-shift keying (BPSK) signals to perform blind CFO estimation on the received signals, and do equi-probable quantization of the noisy CFO estimates followed by information reconciliation-to distil a shared secret key. Furthermore, guided by the Allan deviation curve, we distinguish between the two frequency-stability regimes-when the randomly time-varying CFO process i) has memory, ii) is memoryless; thereafter, we compute the key generation rate for both regimes. Simulation results show that the key disagreement rate decreases exponentially with increase in the signal to noise ratio of the link between Alice and Bob. Additionally, the decipher probability of Eve decreases as soon as either of the two links observed by the Eve becomes more degraded compared to the link between Alice and Bob.

A tractable stochastic geometry model is proposed to characterize the performance of novel point-to-multipoint (P2MP) assisted backhaul networks with millimeter-wave (mm-wave) capability. The novel performance analysis is studied based on the general backhaul network (GBN) and the simplified backhaul network (SBN) models. To analyze the signal-to-interference-plus-noise ratio (SINR) coverage probability of the backhaul networks, a range of the exact- and closed-form expressions are derived for both the GBN and SBN models. With the aid of the tractable model, the optimal power control algorithm is proposed for maximizing the trade-off between energy-efficiency (EE) and area spectral-efficiency (ASE) for the mm-wave backhaul networks. The analytical results of the SINR coverage probability are validated, and they match those obtained from Monte-Carlo experiments. The numerical results of the ASE performance demonstrate the significant effectiveness of our P2MP architecture over the traditional point-to-point setup. Moreover, our P2MP mm-wave backhaul networks are able to achieve dramatically higher rate performance than that obtained by the ultra-high-frequency networks. Furthermore, to achieve optimal EE and ASE tradeoff, the mm-wave backhaul networks should be designed to limit the link distances and line-of-sight interferences while optimizing the transmission power.

We consider an idealistic scenario where the vacation (no-load) period of a typical base station (BS) is known in advance such that its vacation time can be matched with a sleep depth. The latter is the sum of the deactivation latency, actual sleep period and reactivation latency. Noting that the power consumed during the actual sleep period is a function of the deactivation latency, we derive an accurate closed-form expression for the optimal deactivation latency for deterministic BS vacation time. Further, using this expression, we derive the optimal average power consumption for the case where the vacation time follows a known distribution. Numerical results show that significant power consumption savings can be achieved in the sleep mode by selecting the optimal deactivation latency for each vacation period. Furthermore, our results also show that deactivating the BS hardware is sub-optimal for BS vacation less than a particular threshold value.

Low-Iatency communication is crucial to satisfy the strict requirements on latency and reliability in 5G communications. In this paper, we firstly consider a contract-based vehicular fog computing resource allocation framework to minimize the intolerable delay caused by the numerous tasks on the base station during peak time. In the vehicular fog computing framework, the users tend to select nearby vehicles to process their heavy tasks to minimize delay, which relies on the participation of vehicles. Thus, it is critical to design an effective incentive mechanism to encourage vehicles to participate in resource allocation. Next, the simulation results demonstrate that the contract-based resource allocation can achieve better performance.

The increasing adoption of the Internet of Things has led to the need for systems with higher spectral and energy efficiency (EE) in order to enable communication. Larger data rate demands had led researchers to look at millimeter wave (mmWave) bands to boost network rates. This paper investigates the downlink performance of a three-tier heterogeneous network that consists of sub-6 GHz macrocells overlaid with small cells operating on both the mmWave and sub-6 GHz bands. A model is developed using tools from stochastic geometry to analyze the coverage, rate, area spectral efficiency, and EE of such a network. Various deployment strategies and their impacts on the considered metrics are studied. Simulation results are used to verify the validity of the proposed model.

Millimeter wave (mmWave) links have the potential to offer high data rates and capacity needed in fifth generation (5G) networks, however they have very high penetration and path loss. A solution to this problem is to bring the base station closer to the end-user through heterogeneous networks (HetNets). HetNets could be designed to allow users to connect to different base stations (BSs) in the uplink and downlink. This phenomenon is known as downlink-uplink decoupling (DUDe). This paper explores the effect of DUDe in a three tier HetNet deployed in two different real-world environments. Our simulation results show that DUDe can provide improvements with regard to increasing the system coverage and data rates while the extent of improvement depends on the different environments that the system is deployed in.

In this paper, we present an analytical framework to model the sleep mode power consumption of a base station (BS) as a function of its sleep depth. The sleep depth is made up of the BS deactivation latency, actual sleep period and activation latency. Numerical results demonstrate a close match between our proposed approach and the actual sleep mode power consumption for selected BS types. As an application of our proposed approach, we analyze the optimal sleep depth of a BS, taking into consideration the increased power consumption during BS activation, which exceeds its no-load power consumption. We also consider the power consumed during BS deactivation, which also exceeds the power consumed when the actual sleep level is attained. From the results, we can observe that the average total power consumption of a BS monotonically decreases with the sleep depth as long as the ratio between the actual sleep period and the transition latency (deactivation plus reactivation latency) exceeds a certain threshold.

The existing cellular networks are being modified under the umbrella of fifth generation (5G) networks to provide high data rates with optimum coverage. Current cellular systems operating in ultra high frequency (UHF) bands suffer from severe bandwidth congestion hence 5G enabling technologies such as millimeter wave (mmWave) networks focus on significantly higher data rates. In this paper, we explore the impact of co-existance of massive Multiple-Input Multiple-Output (MIMO) that provides large array gains and mmWave small cells on coverage. We investigate the downlink performance in terms of coverage and rate of a three tier network where a massive MIMO macro base stations (MBSs) are overlaid with small cells operating at sub-6GHz and mmWave frequency bands. Based on the existing stochastic models, we investigate user association, coverage probability and data rate of the network. Numerical results clearly show that massive MIMO enabled MBSs alongside mmWave small cells enhance the performance of heterogeneous networks (HetNets) significantly.

Millimeter wave (mmWave) technology is considered as a key enabler for fifth generation (5G) networks to achieve higher data rates with low transmission power by offloading the users with low signal-to-noise-ratios. Millimeter wave networks operating at E and W frequency bands have available bandwidth of 1 GHz or more to provide higher data rates whereas their propagation characteristics differ greatly from the conventional Ultra High Frequency (UHF) networks operating at sub 6 GHz frequency band. The purpose of this paper is to investigate the performance in terms of coverage and rate, of hybrid cellular networks where base stations (BSs) operating at mmWave and sub 6 GHz bands coexist in suburban environment such as a university campus. The actual building locations within a suburban university campus are modeled as blockages and the analysis is carried out for different densities of UHF and mmWave BSs for different densities of outdoor users. Our analysis also highlight the fact that mmWave cellular networks are predominantly noise-limited due to larger available bandwidth in comparison to the interference limited conventional UHF networks. Extensive simulation results demonstrate the effectiveness of dense deployment of mmWave BSs to achieve better coverage and rate probabilities in comparison to the stand alone UHF network.

For the past few years, 5G heterogeneous networks (HetNets) have gain phenomenal attention in the wireless industry. In this paper, we propose a hierarchical game theoretical framework for the optimal resource allocation on the uplink of a heterogeneous network with femtocells overlaid on the edge of a macrocell. In the first game, the femtocell access points (FAPs) play a non- cooperative game to choose their access policy between open and closed in order to maximize the rate of their home subscribers. The second game of the algorithm allows macrocell user equipments (MUEs) to decide their connectivity between the FAPs and the macrocell base station (MBS) with the goal of maximizing their rates and the overall network performance; thereby, distributing intelligence and control to the users. The FAPs and the MUEs are the players of two different games that strategically decide their policies in an ordered fashion. Simulation results show that this hierarchical game approach with network- assisted user-centric design offers a significant improvement in terms of the performance of HetNets relative to an closed and only network-centric access policy schemes.

Millimeter wave (mmWave) technology integrated with heterogeneous networks (HetNets) has emerged as a new wave to overcome the thirst for higher data rates and severe shortage of spectrum. In this paper, we consider the uplink of a hybrid HetNet with femtocells overlaid on a macrocell, and formulate a two layer game theoretic framework to maximise the energy efficiency (EE) while optimising the network resources. The outer layer allows each femtocell access point (FAP) to maximise the data rate of its users by selecting the frequency band either from the sub-6 GHz and the mmWave. The solution to this non-cooperative game can be obtained by using pure strategy Nash equilibrium. The inner layer ensures the energy efficient user association method subject to the minimum rate and maximum transmission power constraints by using dual de-composition approach. Simulation results show that the proposed hybrid HetNet scheme exploiting the mmWave frequency band improves the sum-rate and EE in comparison to the scenario where all the networks operate at sub-6 GHz frequency band. The performance can further be enhanced by incorporating the power control mechanism.

Green Small Cell Networks aim at achieving high rates and low powers by offloading users with low signal-to-noise-ratios from macrocell to the pico base station. In this work, we propose to jointly optimise energy efficiency (EE) and spectrum efficiency (SE) such that the network providers can dynamically tune the trade-off parameter for different design requirements. This paper formulates the EE-SE trade-off as a multi-objective optimisation problem (MOP) in the uplink of multi-user two-tier Orthogonal Frequency Division Multiplexing Heterogeneous Networks. Using the weighted sum method, the MOP can be transformed into a single-objective optimisation problem (SOP). The proposed EE and SE trade-off optimisation problem is strictly quasi-concave. Hence, using Dual Decomposition approach, we derive the unique optimal solution. Numerical results demonstrate the effectiveness of the proposed approach and illustrate the fundamental tradeoff between EE and SE for different tradeoff parameters such as maximum transmission power and circuit power.

This paper proposes joint user association and energy-efficient resource allocation in the uplink of multi-user two-tier Orthogonal Frequency Division Multiplexing (OFDM) Heterogeneous Networks (HetNets) subject to user's maximum transmission power and minimum-rate constraints. The proposed scheme aims at achieving high rates at low powers satisfying the user's quality-of-service (QoS) constraints (in terms of minimumrate requirements) by offloading the users with low signal to noise ratio (SNR) from macrocell to the pico base station (BS). A channel-to-noise-ratio (CNR)-based rate proportional resource allocation approach is proposed to transform the minimumrate constraint into a minimum required transmission power constraint on each subcarrier. The single-user single-carrier and multi-user multi-carrier energy efficiency (EE) maximization problems are then solved under maximum and minimum power constraints using Karush-Kuhn-Tucker (KKT) conditions. The impact of users' maximum transmission power and minimumrate requirements on EE and throughput are investigated through illustrative results. The rate-proportional approach is evaluated against the equal rate allocation approach for different user associations and various numbers of users, maximum transmission power, and circuit powers. Significant gains in EE can be achieved for the HetNets if the path loss based user association is combined with the proposed CNR rate proportional mechanism.

This paper proposes a novel dynamic network selection mechanism in cooperative heterogeneous wireless networks considering both network and user perspective. This approach adopts a suitably defined utility function, which at the same time takes into account the users' importance for the considered attributes (i.e. offered bit rate, coverage prediction, preferred interface and price being charged) and the quality offered for these attributes by the available networks. The strength of this approach is its ability to allow users to dynamically change their preferences achieving better Quality of Service (QoS) whereas the network operator can also vary their controlling parameters (e.g. network re-configuration and network adjustment) dynamically to improve their benefits. The dynamics of network selection in cooperative wireless networks is modeled using an evolutionary game theory where an evolutionary equilibrium is sought as a solution to this game. The performance of the proposed dynamic network-selection algorithm is investigated and evaluated by using simulations.

This paper illustrates an approach to improving the user perceived QoS in heterogeneous networks through the use of a two layered optimization. The top layer views the problem of prediction of the network that would be chosen by a user where the criteria are offered bit rate, price, mobility support and reputation. At the second level, conditional on the strategies chosen by the users, the network operator hypothetically, reconfigures the network, subject to the network constraints of bandwidth and acceptable SNR and optimizes the network coverage to support users who would otherwise not be serviced adequately. This forms an iterative cycle until a solution that optimizes the user satisfaction subject to the adjustments that the network operator can make to mitigate the binding constraints, is found. The process is illustrated through a simple example using a Wi-Fi and a WiMAX network.