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.

A novel user-clustering (UC) based downlink hybrid-nonorthogonal multiple access (hybrid-NOMA) assisted heterogeneous network (HetNet) design strategy in the presence of an eavesdropper is explored to investigate the secure resource allocation beyond fifth-generation/sixth-generation (B5G/6G) wireless communications. We have formulated a secrecy rate (SR) maximization problem based on the proposed strategy subject to key performance indicators (KPIs) such as the admission of users in clusters, the association of users with the base station (BS), allocation of power to the users, and user's minimum rate requirement. This novel problem is solved to find an ε-optimal solution within ε = 10−3using the outer approximation algorithm (OAA). This paper performs an analysis of the performance of the proposed strategy and it is depicted through simulation results that the proposed strategy maximizes the SR in terms of KPIs. A performance comparison of the proposed strategy with the existing orthogonal multiple access-only (OMA-only) and nonorthogonal multiple access-only (NOMA-only) strategies is conducted, and it is identified through simulation results that the proposed strategy performs best among the existing strategies. Additionally, a comparison of the performance of the proposed strategy for HetNet and macro base station-only (MBS-only) networks is conducted, and the simulation results depicted that the proposed strategy performs better in HetNet compared to the MBS-only network.

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.

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.

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.

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.

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).