ABSTRACT Reconfigurable Intelligent Surface (RIS) is emerging as a potential solution for Tera Hertz communication. It supports a re‐configurable and smart wireless environment by enhancing the signal strength and reflecting it to the desired receiver which aids in improving energy efficiency. This article presents a novel mathematical framework of a mixed integer nonlinear programming (MINLP) problem to maximize the energy efficiency (EE) of a RIS‐based multi‐user network having a multi‐antenna base station (BS). The formulated problem is a concave fractional programming problem (CFP) which is transformed into a concave program using the proposed Charnes–Cooper transformation (CCT). MINLP problems are generally NP‐hard in nature and their complexity increases with the number of users, therefore, an outer approximation algorithm (OAA) is applied which gives.

Unmanned aerial vehicle (UAV) networks equipped with mobile edge computing (MEC) servers are increasingly deployed to deliver on-demand computing services in infrastructure-limited areas. However, the dynamic nature of UAV networks and their limited resources present significant challenges for task offloading and resource allocation. To address these challenges, we propose a framework that integrates the digital twin (DT) to optimize task offloading under uncertainty of energy consumption. The DT acts as a virtual replica of the UAV network, offering real-time predictions of local latency and offloading delays, enabling more accurate and adaptive decision making. We formulate a chance-constrained optimization problem to minimize task latency, ensuring that energy consumption exceeds a predefined threshold with only a limited probability. We propose a two-stage approach that combines an intelligent UAV placement algorithm with a modified iterative solver, referred to as the Renaldi algorithm, to solve the optimization problem efficiently and with low computational complexity. The simulation results show that the DT-assisted framework enhances user connectivity, improves resource utilization, and reduces computational complexity.

Trustworthy communication is essential for the success of sixth-generation (6G) networks, enabling seamless and reliable interactions across diverse connected Internet of things (IoT) devices and systems. Uncrewed aerial vehicles (UAVs) are expected to play a pivotal role in this ecosystem by facilitating low-latency communication and promoting efficient resource utilization through flexible deployment strategies. In this context, we propose a trust-driven framework for UAV-assisted mobile edge computing (MEC). Initially, UAVs are deployed using the K-means clustering algorithm to maximize coverage. We then formulate an optimization problem that simultaneously targets several conflicting objectives: (i) maximize the trust level of serving UAVs; (ii) minimize computational latency for IoT devices tasks; (iii) maximize the number of served IoT devices; and (iv) balance the trade-off between maximizing trust and minimizing service provisioning cost. UAV trustworthiness is modeled as a composite metric encompassing success rate, security score, communication stability, computational resource availability, and energy sufficiency. To solve this problem, we develop a penalty-guided optimization (PGO) algorithm that guides relaxed binary decision variables toward integer solutions, followed by refinement using sequential quadratic programming. Simulations demonstrate that the PGO algorithm outperforms baseline relaxation methods and achieves performance close to the optimal branch-and-bound approach, while significantly reducing computational complexity. Additionally, unequally weighted trust metrics yield better UAV trust levels as compared to equal weighting, highlighting the advantage of adaptive trust modeling. The proposed UAV trust evaluation framework and deployment strategy thus collectively lead to enhanced network accessibility, reliability, and utility in aerial IoT networks.

With technological advancements, Unmanned Aerial Vehicles (UAVs) are becoming prominent in next-generation wireless networks because they enable rapid deployment, enhance coverage, and provide advanced services to end users. End users benefit significantly from offloading complex and computationally demanding tasks to flying platforms made possible by UAVs outfitted with edge computing servers. However, attaining optimal and effective network performance depends on proper resource management. The capabilities of next-generation networks are increased through the integration of UAV-assisted Mobile Edge Computing with current wireless infrastructure. Internet of Things (IoT) devices are frequently used for real-time data monitoring, gathering, analysis, and transmission for decision-making. This paper presents an optimization problem to increase the number of IoT devices UAVs can serve while minimizing latency and resource costs related to communication, computing, caching, and energy harvesting. We use Digital Twin technology, allowing thorough network replication and monitoring to analyze latency. A complex mixed-integer nonlinear programming problem has been formulated. We provide a multi-stage offloading mechanism called the Integrality Gap Method with an Interior Point mechanism to tackle this complexity. Simulation findings show that the suggested approach performs better than the straightforward relaxation heuristic technique, confirming its effectiveness.

Unmanned aerial vehicles (UAVs) enable a mobile edge computing (MEC) paradigm with reduced latency by bringing computational resources closer to the network edge. However, UAV-MEC servers have less computation and caching resources than ground base stations (BSs). The management of communication and control resources is crucial to coordinate communication, computing, and caching due to the involvement of aerial networks. Thus, managing joint caching, communication, computing, and control (4C) resources is vital in UAV-assisted MEC networks. To address these challenges, we developed a computational model for efficient resource management to reduce the linear combination of network cost and latency under constrained caching, computing, and offloading. We used binary decision variables for the allocation of computational and offloading resources. The formulated problem is a binary linear programming problem incorporating binary decision variables and linear constraints. We propose an interior point method-based heuristic to obtain a sub-optimal solution with low complexity. Simulation results demonstrate the effectiveness of our proposed approach compared to the branch and bound algorithm.

The sixth-generation (6G) wireless networks are expected to provide ubiquitous connectivity, high data rate, low latency, energy efficiency, and edge intelligence for Internet of Things (IoT) applications. Digital twin technology is a promising solution to enable multi-layer wireless networks that incorporate IoT devices on the ground, unmanned aerial vehicles (UAVs) as mobile edge computing (MEC) servers, and cloud servers. Multi-layer processing can handle time-sensitive and computationally intensive tasks from IoT devices. This paper proposes a digital twin-assisted multi-layer network for low-latency and energy-efficient communication and computation. We mathematically formulate an optimization problem to minimize the latency and energy consumption of IoT devices by optimizing their association with the UAV-MECs, computation resources, communication resources, and offloading portions of tasks. We propose a two-stage scheme based on the K-means method and the deep neural network approach to solve the above optimization problem. We compare the proposed two-stage scheme with existing schemes to highlight the scalability of the proposed solution. Simulation results demonstrate that the proposed multi-layer network achieved optimization results comparable to existing schemes with less computational cost, highlighting its usefulness in achieving low latency and energy-efficient computation and communication.

Effective resource management is critical in the dynamic environment of multiuser, multiservice, and multidevice 6G networks. This necessitates careful consideration of service priorities in the context of conflicting demands and limited resources. To address this challenge, this research introduces intelligent priority-driven resource allocation using the penalty function (IPRAPF) approach, which transforms resource allocation into an integer programming problem, balancing user expectations with available resources. IPRAPF significantly improves service accommodation per priority level over conventional optimization methods, such as simple relax and optimum branch and bound in different 6G networks. Notably, IPRAPF demonstrates robust performance with 20 users, five services, and four computing devices, supporting service allocation improvements ranging from 15% to 18% per priority level. In contrast, the simple relax method yields lower allocations, with improvements ranging from 11% to 13%, highlighting the superior effectiveness of the proposed IPRAPF. Moreover, an analysis of services per priority level highlights the capability of IPRAPF to optimize resource utilization and ensure seamless service delivery, especially with increased service diversity. This emphasizes the adaptability and importance of IPRAPF in navigating the constantly changing environment of 6G wireless networks.

Unmanned-aerial-vehicles (UAVs) have been gaining much attention in the next-generation wireless networks due to their ability to enhance coverage and provide advanced services, particularly for first responders. UAVs equipped with mobile-edge computing (MEC) capabilities can migrate computational resources to airborne platforms. However, it is crucial to manage resources efficiently to optimize overall network performance. Moreover, in public safety scenarios, UAVs can help charge low-power Internet of Things (IoT) devices to sustain system operations. A holistic approach to managing communication, computation, caching, and energy resources is necessary to leverage UAV-assisted MEC networks fully. We formulated an optimization problem to minimize latency and reduce resource costs associated with communication, computation, caching, and energy harvesting while maximizing the number of IoT devices served by UAVs. Therefore, we integrated digital twin technology to analyze the latency. The optimization problem is challenging as it involves a mixed-integer nonlinear programming problem. To address this complexity, we propose a multistage offloading algorithm named the penalty function method heuristic algorithm that combines a learning algorithm with an interior-point method, ultimately delivering a practical solution. Our simulation results validate the performance of the proposed algorithm, which yields superior results compared to the simple relaxation heuristic algorithm.

The sixth-generation (6G) is expected to offer ubiq-uitous connectivity, high data rate, low latency, energy efficiency, and edge intelligence for Internet of things (IoT) applications. To achieve this, the digital twin is considered as a potential technology in multi-layer wireless networks with IoT devices on the ground, unmanned aerial vehicles (UAVs) as mobile edge computing (MEC) servers, and cloud server. Multi-layer processing is used to handle time-sensitive and computationally intensive tasks by IoT devices. This paper proposes a digital twin-assisted multi-layer network for low-latency and energy-efficient communication and computation. We mathematically formulate an optimization problem to minimize IoT devices' latency and energy consumption by optimizing their association with the UAV-MECs, computation resources, communication resources, and offloading portions of tasks. We propose a multi-stage solution based on a learning algorithm and interior point method to solve the problem. The simulation results demonstrate the usefulness of the proposed multi-layer network.

We investigate digital twin-assisted task offloading in unmanned-aerial-vehicle (UAV)–mobile edge computing (UAV-MEC) networks with energy harvesting. Digital twin technology leverages a real-time simulated environment to optimize UAV-MEC networks. Considering unpredictable mobile edge computing (MEC) environments and low-power Internet of Things (IoT) devices, we propose a digital twin-assisted task offloading scheme in UAV-MEC networks with energy harvesting. The goal is to minimize latency and maximize the number of associated IoT devices by optimizing UAV placement and IoT device association. The constraints on computing, caching, energy harvesting, latency, and maximum number of IoT devices an UAV can serve are considered. To solve the formulated problem, we employ a branch-and-bound algorithm to obtain optimal results. We also solve the optimization problem using the relaxed heuristic algorithm. In addition, we propose a difference of convex penalty-based algorithm to solve the problem with reduced computational complexity. This approach provide efficient alternatives to obtain near-optimal solution. Through extensive simulations, we demonstrate the effectiveness of the proposed algorithm and validate the benefits of leveraging digital twin technology in UAV-MEC networks with energy harvesting.

Recently, unmanned aerial vehicle (UAV)-assisted mobile-edge computing (MEC) networks are considered to provide effective and efficient solutions for disaster management. However, the limited size of end-user devices comes with the limitation of battery lives and computational capacities. Therefore, offloading, energy consumption, and computational efficiency are significant challenges for uninterrupted communication in UAV-assisted MEC networks. This article considers a UAV-assisted MEC network with energy harvesting (EH). To achieve this, we mathematically formulate a mixed-integer nonlinear programming problem to maximize the computational efficiency of UAV-assisted MEC networks with EH under disaster situations. A power-splitting architecture splits the source power for communication and EH. We jointly optimize user association, transmission power of user equipment (UE), task offloading time, and UAV’s optimal location. To solve this optimization problem, we divide it into three stages. In the first stage, we adopt$k$-means clustering to determine the optimal locations of the UAVs. In the second stage, we determine user association. In the third stage, we determine the optimal power of UE and offloading time using the optimal UAV location from the first stage and the user association indicator from the second stage, followed by linearization and the use of the interior-point method to solve the resulting linear optimization problem. Simulation results for offloading, no-offloading, offloading-EH, and no-offloading-EH scenarios are presented with a varying number of UAVs and UEs. The results show the proposed EH solution’s effectiveness in offloading scenarios compared to no-offloading scenarios in terms of computational efficiency, bits computed, and energy consumption.

Cooperative communication by employing unmanned aerial vehicle (UAV)-based relays with radio-frequency (RF) energy harvesting (EH) has been emerged as a prominent solution to provide extended coverage, connectivity, capacity, energy efficiency, and reliability in the future Internet-of-Things (IoT) systems. For successful integration of UAV relays in IoT networks, efficient radio resource management (RRM) is critical. We developed a multicriterion framework for energy-efficient RRM in a cooperative IoT network. We considered UAVs as relays, onboard EH facilities and deployed to relay the messages from a satellite terminal to the network IoT devices. We adopted a power splitting (PS)-based EH scheme, i.e., PS relaying protocol, for RF-EH at UAV relays. We formulate a joint optimization problem for IoT device selection, UAV relay assignment, source power allocation, and PS ratio selection. In our multicriterion framework, we consider three conflicting objectives by applying a weighted-sum method: 1) maximizing the network sum rate; 2) maximizing the number of IoT devices to be served; and 3) minimizing the carbon dioxide emissions. We propose an outer approximation algorithm (OAA) to solve the formulated problem, which is a mixed-integer nonlinear programming (MINLP) problem. Simulation results of the proposed algorithm are compared with two existing solutions, namely, the nonlinear optimization by mesh adaptive direct search (NOMAD) algorithm and an evolutionary algorithm (EA). The performance of the NOMAD algorithm is better in terms of computational complexity. However, the simulation results reveal the supremacy of the proposed OAA in terms of network sum rate, the number of selected IoT devices, and network utility.

Coverage, capacity and throughput can be enhanced significantly by offering a hybrid solution consisting of microwave high power base station (HPB) underlaid millimeter wave low power base station (LPB) augmented by the downlink uplink decoupled (DU-De) user association strategy in N-tier heterogeneous networks (HetNets). However, secure user association, power and microwave and millimeter wave sub-carriers allocation employing DU-De strategy has not been investigated in the past. This work formulates mathematical models for DU-De strategy and downlink uplink coupled (DU-Co) strategy to investigate secure user association, power and sub-carrier in microwave and millimeter wave band allocation for secrecy rate maximization in N-tier HetNets. Nature of the formulated problems is complex, challenging and NP-hard. Outer approximation algorithm, with less complexity, is used to solve the formulated problems for optimal solution. Extensive simulation results in terms of secure user association and average secrecy rate show the effectiveness of DU-De strategy over DU-Co strategy in HetNets.

The increasing number of mobile devices and multimedia applications demands enormous capacity that requires innovative network designs. A hybrid non-orthogonal multiple access (NOMA) scheme with user clustering has been considered a promising solution to meet the increasing demand for capacity. Successive interference cancellation (SIC) is applied to minimize interference in NOMA. Also, in addition to this, heterogeneous networks (HetNets) improve network throughput. It is necessary to find optimal resource allocation for throughput maximization. In this paper, a mixed-integer non-linear programming (MINLP) problem has formulated for throughput maximization using a hybrid NOMA scheme with user clustering in a macro base station (MBS) only network and HetNets. MINLP problems are non-deterministic polynomial time-hard (NP-hard) in general. An exhaustive search algorithm with the exponential increase in complexity with the number of users restricts it to use for the problem's solution. The throughput maximization problem is solved using an outer approximation algorithm. The proposed algorithm is evaluated with extensive simulations to show that the proposed solution is effective regarding network throughput and user association. Also, the proposed algorithm compares user admission in clusters, user association with BSs in MBS only network, and HetNets. The results clearly show that HetNet outperforms MBS only network in terms of the network throughput, the number of users admitted in clusters, and the number of users associated.

The rising number of user equipment (UE) and advanced applications in fifth-generation (5G) and beyond fifth-generation (B5G) networks need energy-efficient resource allocation. The researchers have not yet considered hybrid nonorthogonal multiple access (H-NOMA) scheme with UE clustering in heterogeneous networks (HetNets). This paper investigates downlink H-NOMA scheme with UE clustering in HetNets to maximize energy efficiency (EE). A mathematical optimization problem considers UE admission in clusters, UE association with base stations (BSs), power allocation, the minimum rate, and transmit power requirements. We have converted the formulated non-linear concave fractional programming (CFP) problem into a concave optimization problem with Charnes-Cooper transformation (CCT). An ∊-optimal outer approximation algorithm (OAA) solves the formulated problem. The effectiveness of the proposed scheme regarding EE, network throughput, UE admission, and UE association is shown with the simulation results.

On-demand data sensing and wireless power transfer (WPT) can provide sustainability and robust operations in large-scale industrial Internet of Things (IoT) networks. The efficiency of on-demand data collection and WPT can be increased by efficient scheduling of IoT nodes and dedicated energy transmitters respectively. In this article, we propose an energy-aware mode switching strategy to enable IoT nodes to perform either on-demand sensing or dedicated WPT. For on-demand sensing, we propose an IoT node scheduling scheme to maximize the utility of the IoT nodes comprising residual energy and energy required for sensing operation while considering the reliability of sensing tasks. For WPT, we propose an energy transmitter scheduling scheme for IoT nodes to minimize the cost of charging while keeping IoT nodes sufficiently charged. The simulation results for IoT node scheduling demonstrate that less than 50% IoT nodes need to be activated in all scenarios to complete the tasks. The proposed energy transmitter scheduling scheme shows that less than 60% energy transmitters should be scheduled in all the scenarios which results in significant energy reduction in the overall system.

5G is expected to serve diverse applications and users due to the popularity of Internet of Things (IoT), big data and industrial applications. Many of these IoT and industrial applications have inherent loss tolerance that can be used to enable energy efficient uplink communication. The uplink energy efficient system will increase the battery life of devices enabling new use cases in industrial IoT. In this paper, we map the effects of application loss tolerance to the rate requirements of the user. We then mathematically model an energy minimization problem for the uplink user association and resource allocation in heterogeneous networks. We aim to provide acceptable quality of service (QoS) with improved energy efficiency by exploiting the loss tolerance and bandwidth expansion simultaneously. A distributed uplink joint user association and resource allocation strategy for uplink energy per bit minimization is presented. We conduct extensive simulation based study for a heterogeneous network to evaluate the performance of our proposed schemes. Average energy per bit consumption in the proposed scheme is -74 dB compared to -53 dB in state-of-the-art channel individual offset (CIO) scheme.

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.

As the number of cellular and multimedia users are increasing the current mobile networks are being overloaded and need to be upgraded to new architectural featured 5G networks. Heterogeneous Cloud Radio Access Networks (HCRAN) are one of the dominant candidates for future networks with high data rate, minimized interference and high Energy Efficiency (EE). Due to dense users and base stations placement, the power consumption of H-CRAN is much higher than today's cellular networks. Energy harvesting (EH) is the solution to mitigate the grid power consumption problem in which power is harvested from natural resources like wind, solar, etc. EE of the system can be improved using energy harvesting and efficient resource allocation. In the presented article EE of H-CRAN with energy harvesting aided radio remote heads is explored. Formulated system problem is a mixed-integer non-linear programming (MINLP) problem which has the objective to maximize the H-CRAN system's EE. To optimize the proposed optimization problem Mesh Adaptive Direct Search (MADS) algorithm is explored. EE of H-CRAN system is maximized by resource allocation and power allocation which is efficient in terms of energy consumption. Our results show the objective is achieved with the help of low complexity algorithm and lower consumption of grid energy.

An optimal utilization of cellular resources is ambitious target of 5thgeneration (5G) heterogeneous network (HetNet). In 4thgeneration (4G) HetNet, downlink (DL) uplink (UL) coupled access (DUCa) strategy where a mobile station (MS) is associated in DL & UL with a one base station (BS) to ensure energy efficiency (EE) and throughput maximization is a suboptimal solution. An optimal solution in term of EE and throughput is offered by novel DL and UL decoupled access (DUDa) strategy where a MS is associated in DL & UL with one or two BSs in 5G HetNet. This research work conducts performance analysis of DUCa strategy vs DUDa strategy for effective resource allocation to ensure EE in HetNet. First, EE maximization problem in term of resource allocation using DUCa and DUDa strategy in HetNet is formulated. The formulated problem belongs to a class of non-linear fractional programming problem. The formulated fractional programming problem is transformed to a concave optimization problem employing Charnes-Cooper transformation. Then outer approximation algorithm (OAA) is employed to solve the formulated concave optimization problem. Extensive simulation work is done to show the effectiveness of proposed algorithm. OAA approach gives ε-optimal solutions for multiple parameters such as MSs associated, minimum required data rate, EE and throughput in DUCa and DUDa HetNet.

Traditional downlink (DL)-uplink (UL) coupled cell association scheme is suboptimal solution for user association as most of the users are associated to a high powered macro base station (MBS) compared to low powered small base station (SBS) in heterogeneous network. This brings challenges like multiple interference issues, imbalanced user traffic load which leads to a degraded throughput in HetNet. In this paper, we investigate DL-UL decoupled cell association scheme to address these challenges and formulate a sum-rate maximization problem in terms of admission control, cell association and power allocation for MBS only, coupled and decoupled HetNet. The formulated optimization problem falls into a class of mixed integer non linear programming (MINLP) problem which is NP-hard and requires an exhaustive search to find the optimal solution. However, computational complexity of the exhaustive search increases exponentially with the increase in number of users. Therefore, an outer approximation algorithm (OAA), with less complexity, is proposed as a solution to find near optimal solution. Extensive simulations work have been done to evaluate proposed algorithm. Results show effectiveness of proposed novel decoupled cell association scheme over traditional coupled cell association scheme in terms of users associated/attached, mitigating interference, traffic offloading to address traffic imbalances and sum-rate maximization.

In this article, we present a data obfuscation technique for smart meter data based on additive correlated noise. This noise is used to mask the data transmitted by different users to a third party, resulting in protection against eavesdroppers, but at the same time enabling the accurate recovery of statistics of the original data for use by the energy supplier. We analyze the proposed technique by studying its obfuscation performance and accuracy of statistics recovered from the masked data as a function of noise correlation with the users' data. Finally, we identify deep learning techniques such as generative adversarial networks for data obfuscation using correlated noise, and show preliminary results to demonstrate their performance in this scenario.

Green communication with sustainable energy is being considered for 5G cellular network and Internet of Things (IoT) mainly with focus on energy harvesting (EH) to prolong network lifetime. Moreover, device-to-device (D2D) communication on shared channels is also considered as a promising technology to achieve high data rates, ultralow latency communication, and high spectral efficiency. In this paper, we investigated resource allocation in EH-aided D2D communication underlying 5G cellular along with enabling IoT services. The objective is to maximize throughput of the network subject to the joint constraints on user performance, number of admitted users for equity and fair usage, mode assignment (cellular or D2D) as per available energy, and transmit power allocation along with EH techniques, which results in a mixed integer nonlinear programming problem. We have proposed a low complexity and efficient algorithm, adaptive resource allocation and energy sentient network (ARA-ESN) using branch-cut, branch-bound, and mesh adaptive direct search (MADs) solutions, where cellular or D2D communication is based on available energy and user performance criteria along with EH through ambient energy and radio frequency (RF) energy transfer techniques. We have applied the outer approximation based linearization technique that guarantees the convergence to the optimal solution. The results show that ARA-ESN branch-cut outperforms ARA-ESN branch-bound and ARA-ESN MADs. Moreover, we have also observed that ambient harvesting increases performance of network due to better acquisition of energy as compared to RF energy transfer.

Green communication with energy harvesting techniques are being considered for 5thgeneration cellular networks to conserve energy and to prolong network life time. Moreover, Device to Device (D2D) communication on shared channels for 5G cellular networks is also a promising technology to achieve higher data rates, ultra-low latency communication and spectral efficiency. Presently, network optimization with an aim to maximize network throughput by meeting joint constraints of admitted user performance, maximization of admitted users, resource allocation, mode assignment as per available energy, power allocation along with different energy harvesting techniques have not been investigated in the literature due to it's infeasible nature. In this paper, we have formulated NP hard mix integer nonlinear programming (MINLP) optimization problem and then we have proposed a low complexity and efficient algorithm, Adaptive Resource Allocation and Energy Sentient Network (ARA-ESN) using Branch-Bound and Mesh Adaptive Direct Search (MADs) methods with energy harvesting through ambient energy and Radio Frequency (RF) energy transfer techniques. We have applied outer approximation approach (OAA) based linearization technique, which guarantees convergence to the optimal solution. The results show that ARA-ESN MADS outperforms ARA-ESN Branch-Bound method interms of performance and complexity. Moreover, ambient harvesting increases performance of network due to better acquisition of energy as compared to RF energy transfer.

Cloud radio access network (CRAN) is a promising approach to provide ubiquitous and on demand access to future Internet of Things (IoT) networks. The existing CRANs assume a single cloud which suffers from computational complexity and signaling latency to support massive number of IoT devices in large scale network deployments. This paper focuses on the scheduling of IoT devices in a multicloud IoT network scenario. This paper considers the downlink of an IoT network consisting of multiple clouds, each coordinates a cluster of several base stations (BSs) allowing joint signal processing. The transmit frame of each BS is composed of several resource blocks (RBs). The multiple clouds are linked to the central cloud which performs scheduling of IoT devices and synchronization of transmit frames. The work models the IoT devices to RBs assignment problem considering the intercloud and intracloud interference. The optimization problem maximizes the overall network utilization under practical network constraints. Further, this paper also proposes a low complexity heuristic algorithm to solve the constraint resource allocation problem in linear time. Complexity analysis of proposed algorithm is carried out and simulations results for a number of IoT network scenarios demonstrate that proposed solution is numerically accurate and performs close to the optimal solution.

Emerging device centric communication technologies such as device to device (D2D) communication, devices to device (Ds2D) communication and multi- homing (MH) D2D have been considered as essential part of future 5G networks as well as internet of things (IoT). The device centric communication offers enhanced cellular data rates, high spectral efficiency, reduced latency, improved fairness, better energy efficiency and extended coverage; however, the battery life of end devices is crucial to fully reap benefits of this technology. In this article we propose a new method for device centric communication in IoT system, where multiple source IoT devices can send data to multiple destination IoT devices using multiple interfaces. This method is called devices to devices (Ds2Ds) communication. A tree search algorithm is proposed to select the optimal source IoT devices, destination IoT devices and radio interfaces. The results of proposed Ds2Ds communication are benchmarked against Ds2D and MH- D2D. Extensive simulation has been carried out to compare energy efficiency per source device. The simulation results show the superiority of Ds2Ds over Ds2D in terms of energy efficiency, which, in turn implies better throughput. Ds2DS is superior to MH-D2D in terms of energy consumption per source device, a very good and promising requirement for green communication.

Device to device (D2D) communications are emerging as an essential part of technological solutions to boost data rates in the next generation networks. Cognitive radio (CR) opportunistically utilises spectrum to boost spectral efficiency. CR‐assisted D2D networks will bring the benefits of both D2D as well as CR together in futuristic cellular networks. This study proposes to opportunistically use TV spectrum white spaces. A joint user selection, mode assignment, and power allocation in CR‐assisted D2D networks can definitely yield higher data rates. The proposed study maximises data rate together with users' selection fulfilling various users' power, base station's transmit power, quality of service, and interference related thresholds. This problem is mixed integer non‐linear programming and considered non‐deterministic polynomial time (NP)‐complete. Due to the discrete variables in the problem, finding an optimal solution with the help of an exhaustive search algorithm (ESA) becomes very challenging. The problem gets exponentially complex with the increasing number of user pairs. Thus, the need of another method becomes imperative that yields near optimal solution. Mesh adaptive direct search (MADS) algorithm is considered for solution in the CR‐assisted D2D network resource management problem. Simulation results using MADS yield near optimal solution confirming the suitability of MADS for CR‐assisted D2D networks.

Mobile edge or fog computing is a network architecture that brings the functionality of conventional centralized cloud to the edge nodes, which are in close proximity of the end devices in an Internet of Things (IoT) network. Fog networks have many advantages over traditional cloud networks, such as increased bandwidth utilization, enhanced security and privacy, better energy efficiency, improved performance, and support for mobility. The most critical requirement of a fog architecture is to minimize the end to end latency in IoT networks, particularly in scenarios where large number of end devices (IoT nodes) and distributed computing fog nodes (cloudlets) are present. In this paper, we formulated an optimization problem for joint cloudlet selection and latency minimization in a fog network, subjected to maximum work load and latency constraints. The problem can be epitomized as many-to-one matching game in which IoT nodes and cloudlets rank each other in order to minimize the latency. The proposed game belongs to a class of matching games with externalities. We propose an algorithm to solve this game which gives distributed and self-organizing solution. Extensive simulations have been carried out to validate the proposed algorithm.

The foresighted enormous increase in mobile traffic over the coming years will result in severe congestion on available radio spectrum. The use of additional spectrum in future fifth generation (5G) mobile networks will be inevitable. LTE-Unlicensed (LTE-U) is an evolving technology, which effectively utilizes available unlicensed spectrum to increase the capacity of unified 5G network. However LTE-U causes severe interference to existing WiFi networks, which needs to be addressed to take full advantage LTE-U. In this paper we thrive to provide a sub-optimal resource allocation for co-existing LTE-U and WiFi networks to maximize throughput and hence minimize the interference. We formulated an optimization problem for Joint User Association and Power Allocation for Licensed and Unlicensed Spectrum (JUAPALUS) with objective to maximize sum rate of LTE-U/WiFi heterogeneous network (HetNet) in multi-operator scenario, subject to minimum rate guarantee and co-channel interference threshold. We propose mesh adaptive direct search (MADS) algorithm as solution to optimization problem to obtain $\epsilon$-optimal results. The performance of proposed algorithm is shown in terms of network key performance indicators (KPIs) such as throughput, number of users accommodated. We also benchmark the results from MADS against outer approximation algorithm (OAA).

In order to meet requirements of local services in next generation cellular networks, Device to device (D2D) communication is gaining popularity as strategy to maximize overall system throughput. This paper presents a joint resource allocation (JRA) technique with the aim to maximize system throughput while observing the limitations of power and interference. The optimization problem presented is mixed integer, non linear and NP- hard. Increasing discrete variables in the problems, enhances the computational complexity exponentially and exhaustive search becomes formidable task. The problem is solved using mesh adaptive search (MADS) algorithm. It is shown that proposed strategy is suitable for solution of such kind of combinatorial problem. Computational convergence towards solution with adequate iterations shows efficacy of the proposed strategy. System throughput maximization results of simulations show suitability of the suggested scheme in this paper compared to other techniques.

Abstract Device‐to‐device (D2D) communications are being considered a way forward to achieve higher data rate targets for futuristic wireless networks. D2D introduces interference among cellular users and D2D users. A joint resource allocation (JRA) strategy in cellular network with D2D functionality can definitely enhance overall data rate. The strategy under consideration maximizes the overall data rate of cellular network besides meeting threshold of power and interference constraints. The JRA is a class of mixed integer non‐linear constraint optimization problems and is NP hard. Because of discrete nature of variables in the problem, optimal solution performs extensive search of integer variables, and problem becomes exponentially complex with the increasing number of user pairs. In this paper, mesh adaptive direct search algorithm is applied to solve the aforementioned problem. The algorithm is suitable for complex problems of combinatorial nature to solve the JRA strategy in D2D. The proposed algorithm converges to optimal solution within acceptable computational iterations. Simulation results of system capacity and interference also demonstrate the suitability of the proposed approach viz‐a‐viz other algorithms. Copyright © 2016 John Wiley & Sons, Ltd.

In this paper, we propose an optimal power allocation scheme that minimizes the energy per bit of worst user (i.e., the user with highest energy per bit) in a multiuser wireless communication network. The problem of determining energy efficient power allocation to improve the worst user is a constrained non-convex nonlinear fractional programming problem. We propose an iterative energy efficient power allocation algorithm that guarantees optimal solution. We use parametric equivalent formulation to get the optimal solution. Numerical solutions obtained using simulations are presented and compared with equal power allocation scheme.

We study the energy-optimal subcarrier power allocation for OFDM-based cognitive radio (CR) networks. A CR transmitter communicates with CR receivers on a channel borrowed from licensed primary users (PUs) when PUs' transmission are detected absence on those channels. Due to non-orthogonality of the transmitted signals in the adjacent bands, both the PU and the secondary user (SU) cause mutual-interference to each other. We assume that the statistical channel state information between the cognitive transmitter and the primary receiver is known. The secondary transmitter maintains a specified statistical mutual-interference limits for all the PUs communicating in the adjacent channels. We propose iterative method based on Dinkelbach theorem using parametric objective function for the fractional programming problem. We show analytically that under a special case, the optimal power allocation follows waterfilling algorithm. Numerical results are given to show the effect of different parameters on the energy-efficiency.

Low power wireless sensors, personal wireless hub (PWH) and receivers can reduce the workload of the paramedic staff in a hospital. In this paper, we use multiple PWHs to transfer sensor data to the main central controller, which helps the wireless sensor devices. A well designed multiple PWH assignment and power control scheme can reduce the electromagnetic and in-band interference induced to the other medical devices in the hospital. We propose a framework and low complexity algorithm for interference aware joint power control and multiple PWH assignment (IAJPCPA) in a hospital building with cognitive radio capability. The proposed IAJPCPA is a non-convex mixed integer non-linear optimization problem (NC-MINLP) which is generally NP-hard. We present an efficient PWH assignment and power control scheme for IAJPCPA. We also propose an upper bound on the IAJPCPA that converts the non-convex problem into a convex optimization problem. We examine the effect of different system parameters.

In this paper, we apply the cross entropy optimization (CEO) to the problem of joint multiple relay assignment and source/relay power allocation (JMRAPA) in green cooperative cognitive radio (GCCR) networks. We use shared-band amplify and forward relaying for cooperative communication in the JMRAPA problem. The proposed JMRAPA maximizes the total rate and minimizes the greenhouse gas emissions in GCCR networks. It is a non-convex combinatorial optimization problem and is NP-hard. We propose to use concave upper bound that makes it a convex problem. The effectiveness of the proposed CEO-based method is shown through simulation results.

We investigate the optimal allocation of power in the downlink cooperative cognitive radio network using decode and forward (DF) relaying technique. The power allocation in DF relaying for green cooperative cognitive radio with an objective of maximising energy‐efficiency is a constraint non‐linear non‐convex fractional programming problem. The optimisation needs to satisfy the primary users interference constraints and secondary users outage constraints. The authors present the optimal power allocation in DF relaying by transforming the constraint non‐linear non‐convex fractional power allocation problem into a concave fractional programme by using Charnes–Cooper transformation. The authors also present an iterative algorithm that uses parametric transformation and guarantees ε ‐optimal convergence. The convergence of the iterative algorithm is proved and numerical results obtained for cooperative cognitive radio network are presented with different network parameter settings.

In this paper, we investigate the optimal allocation of the power in the downlink cooperative cognitive radio network using decode and forward (DF) relaying techniques. The power allocation in DF relaying for green cognitive radio with objective of maximizing energy efficiency is a constraint nonlinear nonconvex fractional programming (CNNFP) problem. We present the optimal power allocation in DF relaying by transforming the CNNFP power allocation problem into a concave fractional program by using Charnes-Cooper transformation. We also present an iterative ε-optimal solution for the CNNFP problem using Dinkelbach algorithm. The convergence of the iterative algorithm is proved and numerical solutions obtained using simulations for DF cooperative communications are presented.

In this study, the problem of determining the power allocation that maximises the energy efficiency of cognitive radio network is investigated as a constrained fractional programming problem. The energy‐efficient fractional objective is defined in terms of bits per Joule per Hertz. The proposed constrained fractional programming problem is a non‐linear non‐convex optimisation problem. The authors first transform the energy‐efficient maximisation problem into a parametric optimisation problem and then propose an iterative power allocation algorithm that guarantees ε ‐optimal solution. A proof of convergence is also given for the ε ‐optimal algorithm. The proposed ε ‐optimal algorithm provide a practical solution for power allocation in energy‐efficient cognitive radio networks. In simulation results, the effect of different system parameters (interference threshold level, number of primary users and number of secondary users) on the performance of the proposed algorithms are investigated.

This paper combines two emerging research areas: green communications and cognitive radio. A green cognitive radio network must be accountable for its energy expenditure. Energy expenditure of a cognitive base station is reduced by maximizing the bits/Joule energy efficiency (EE) of its transmissions. Any high complexity solution to this optimization problem will spend too much energy in computation. This paper presents a low complexity solution to the problem of finding the power allocation that maximizes the EE, while limiting the interference to the primary users and meeting the users' minimum rate requirements. The objective function of the optimization problem is not concave. Charnes-Cooper Transformation is applied to the problem to convert it into a concave program. KKT conditions were analyzed instead of the Lagrangian dual in lieu of low complexity solutions. A power allocation procedure that branches into two main cases depending on the channel gains is proposed. In the first case, an exact solution is obtained by solving a single non-linear equation that produces a common water level. In the second case, a near optimal solution in closed form is given. Simulation results supporting the analytical green solutions are presented.

In this paper we present a low-complexity Artificial Bee Colony (ABC) based interference aware relay assignment scheme with power control for a cognitive radio network comprises of one source, multiple relays and multiple destination nodes. The Exhaustive Search Algorithm (ESA) returns the optimal solution to the problem; yet it has a high computational complexity that grows exponentially with the number of users and relays. Our contribution includes formulating the jointly relay assignment with source and relays' power allocation as a mixed integer non-linear programming problem. This problem is further reduced to an integer programming problem. In order to demonstrate the performance of the discrete ABC, we compare it with other contemporary Evolutionary Algorithms (EAs) like ACO, EDA and BBO, as well as the optimal ESA. Our Binary ABC relay assignment results outperform other EAs, while its performance is close to the optimal ESA.

The authors presented low-complexity schemes for jointly deciding subcarrier assignment, power allocation and relay assignment (JSPARA) for multiuser non-regenerative relaying in a multigroup, multicast cognitive radio system (MMCRS). The authors considered two different optimisation problems. In one optimisation problem, the authors studied the problem of maximising the sum-rate of an MMCRS. In another optimisation problem, the authors considered the problem of maximising the rate of the worst multicast group of users. And also the authors proposed low-complexity iterative algorithms for JSPARA. The proposed algorithms have low-computational complexity, and their effectiveness is verified through simulation results.

In this paper, we present a binary-based low-complexity interference-aware relay selection scheme for a cognitive radio system with one source and multiple relays, using Biogeography-Based Optimization (BBO) algorithm as one of the novel high-performance Evolutionary Algorithms (EAs). We formulate the joint source and relay power allocation as a mixed integer non-linear programming problem, further reduced to an integer programming problem. Optimally relay assignment using the Exhaustive Search Algorithm (ESA) has a high computational complexity that grows exponentially with the number of relays. We applied the BBO-based relay selection scheme with discrete power control at source and relays for the integer programming problem. We present a low-complex migration model for partial immigration-based BBO, and simulation results show that its performance is close to the optimal ESA.

The authors investigate the interference-aware joint secondary user (SU) selection/scheduling and quantised power control (JSUS-QPC) schemes for the uplink communication in the cognitive multiple-input multiple-output (MIMO) system. The main objective of JSUS-QPC is to maximise the sum-rate capacity of the cognitive MIMO uplink communication system under the constraint that the interference to the primary user (PU) is below a specified level. The computational complexity of finding an optimal JSUS-QPC scheme by exhaustive search grows exponentially with the number of users and power levels. The authors also show that the JSUS-QPC is a non-deterministic polynomial-time hard problem and present two low-complexity algorithms for JSUS-QPC problem. Also, the effect of different system parameters (e.g. interference threshold level, the number of PUs, the number of SUs, the number of quantised power levels, etc.) on the performance of the proposed algorithms is examined. The proposed algorithms have low computational complexity, and their effectiveness is verified through simulation results.

In this paper, we present two low-complexity interference aware multiple relay assignment schemes for cognitive radio systems. The main objective in assigning multiple relays is to maximize the sum capacity of the cognitive radio system under the constraint of acceptable interference to the primary users (PU). The computational complexity of finding an optimal assignment by exhaustive search grows exponentially with the number of relays and users. The proposed schemes have low computational complexity, and their effectiveness is verified through simulation results.

In this paper, we present a low-complexity power allocation scheme for non-regenerative (amplify and forward) relaying in cognitive radio systems. The main objective of the power allocation is to maximize the signal to noise ratio (SNR) at the destination under the constraint of acceptable interference to the primary users (PU). In this paper, we propose an iterative power allocation using SNR upper Bound (IPAUB) for non-regenerative relaying. The proposed algorithm has low computational complexity, and we verify its effectiveness through simulation results.

In this paper, we present a binary particle swarm optimization (BPSO)-based low-complexity interference aware relay assignment scheme for multiple-user cognitive radio networks with discrete power control. We consider a network of cognitive radio nodes comprising single source, multiple relays and multiple destinations. For this system, we formulate an optimization problem to allocate power to source and relays and assign the relays to the destinations. The optimization problem is formulated as a mixed integer nonlinear program. Then, we show that the formulation can be reduced into a simpler integer programming problem. Then, we propose a BPSO-based relay assignment scheme to attain a good suboptimal solution to this integer programming problem. The proposed scheme has low computational complexity, and simulation results show that its performance is close to the optimal exhaustive search algorithm.

We present a spreading gain adaptation protocol between a transmitter and a receiver communicating with each other through a direct-sequence spread spectrum system. This protocol does not require the transfer of an explicit control message indicating the change of spreading gain from the transmitter to the receiver. Also, according to this protocol, the transmitter can change the spreading gain "symbol by symbol" (SBS) as opposed to "frame by frame" (FBF); this enables extremely fast adaptation to the time-varying wireless channel and/or to the rate variation of the traffic. The proposed scheme uses the orthogonal variable spreading factor (OVSF) codes to enable the receiver to detect the spreading gain used by the transmitter. Our analysis indicates that fast adaptation can significantly improve the average throughput over a slow or no adaptation.

This paper explores the application of the particle swarm algorithm for a NP-hard problem in the area of wireless communications. The specific problem is of detecting symbols in a Multi-Input Multi-Output (MIMO) communications system. This approach is particularly attractive as PSO is well suited for physically realizable, real-time applications, where low complexity and fast convergence is of absolute importance. While an optimal Maximum Likelihood (ML) detection using an exhaustive search method is prohibitively complex, we show that the Swarm Intelligence (SI) optimized MIMO detection algorithm gives near-optimal Bit Error Rate (BER) performance in fewer iterations, thereby reducing the ML computational complexity significantly. The simulation results suggest that the proposed detector gives an acceptable performance complexity trade-off in comparison with ML and VBLAST detector.

In Direct Sequence Code Division Multiple Access (DSCDMA) all users transmit at the same time and at the same frequency thus causing mutual interference. In such situation when the powers of the interfering signals are large compared to the desired signal, the performance of the matched filter receiver degrades. This is due to the near-far effect. One way to combat this effect is to use stringent power control, as is done in most commercial systems. Another approach is to use multiuser detection (MUD), which are near-far resistant. MUD has the more fundamental potential of raising capacity by canceling Multiple Access Interference (MAI). MUD has now developed into an important, full-fledged field in multipleaccess communication systems. In this paper, we present advances in algorithms for MUD.