Total Network Throughput Research Articles

Contention-Less Multi-Link Synchronous Transmission for Throughput Enhancement and Heterogeneous Fairness in Wi-Fi 7.

Multi-link operation (MLO) is a new and essential mechanism of IEEE 802.11be Extremely High Throughput (Wi-Fi 7) that can increase throughput and decrease latency in Wireless Local Area Networks (WLANs). The MLO enables a Multi-Link Device (MLD) to perform Simultaneous Transmission and Reception (STR) in different frequency bands. However, not all MLDs can support STR due to cross-link or in-device coexistence interference, while an STR-unable MLD (NSTR-MLD) can transmit multiple frames simultaneously in more than two links. This study focuses on the problems when NSTR-MLDs share a link with Single-Link Devices (SLDs). We propose a Contention-Less Synchronous Transmission (CLST) mechanism to improve fairness between NSTR-MLDs and SLDs while increasing the total network throughput. The proposed mechanism classifies links as MLD Dominant Links (MDLs) and Heterogeneous Coexistence Links (HCLs). In the proposed mechanism, an NSTR-MLD obtains a Synchronous Transmission Token (STT) through a virtual channel contention in the HCL but does not actually transmit a frame in the HCL, which is compensated for by a synchronous transmission triggered in the MDL. Moreover, the CLST mechanism allows additional subsequent transmissions up to the accumulated STT without further contention. Extensive simulation results confirm the outstanding performance of the CLST mechanism in terms of total throughput and fairness compared to existing synchronous transmission mechanisms.

Optimal Power Allocation and Delay Minimization Based on Conflict Graph Algorithm for Device-to-Device Communications

Device-to-device (D2D) technology is a promising technique in terms of being capable of providing efficiency, decreased latency, improved data rate, and increased capacity to cellular networks. Allocating power to users in order to reduce energy consumption and maintain quality of service (QoS) remains a major challenge in D2D communications. In this paper, we aim to maximize the throughput of D2D users and cellular users subject to QoS requirements and signal-to-interference-plus-noise ratio (SINR). To this end, we propose a resource and power allocation approach called optimal power allocation and delay minimization based on the conflict graph (OP-DMCG) algorithm that considers optimal power allocation for D2D multi-users in the cellular uplink channels and minimization of the total network delay using conflict graphs. Based on the simulations presented in this paper, we show that the proposed OP-DMCG algorithm outperforms the greedy throughput maximization plus (GTM+), delay minimization conflict graph (DMCG), and power and delay optimization based uplink resource allocation (PDO-URA) algorithms in terms of both total network throughput and total D2D throughput.

ADFPA – A Deep Reinforcement Learning-based Flow Priority Allocation Scheme for Throughput Optimization in FANETs

Flying ad hoc networks (FANETs) are easy to deploy and cost-efficient, however they are limited by the static protocols used in 802.11 and CSMA-based networks to support high bandwidth multi-UAV applications. This work proposes an Anticipatory Dynamic Flow Priority Allocation (ADFPA) scheme to optimize the priority levels of outgoing traffic flows for a transmitting node to maximize the total network throughput. Unlike other deep reinforcement learning (DRL)-based schemes in centralized networks, ADFPA is designed to be distributed, multi-agent, and proactive. It uses current and forecasted multi-context information to optimize the priority levels of traffic flows in a decentralized and dynamic FANET. Furthermore, a traffic flow sampling and padding algorithm is proposed so that a trained agent can be redeployed in different environments without retraining to address the practicality issue. Our evaluations show that ADFPA outperforms other state-of-the-art schemes by a maximum of 37% and 59.4% in terms of the network throughput in the single and multi-transmitting nodes environment, respectively, while achieving the best fairness amongst all schemes. These improvements translate to better data transmission capabilities in a conventional FANET, and the proposed scheme can enable the use of a FANET architecture in more demanding applications without switching to centralized solutions.

Energy-Efficient Hierarchical Resource Allocation in Uplink–Downlink Decoupled NOMA HetNets

The dense deployment of small cell networks is a key feature of next-generation mobile networks aimed at providing the necessary capacity increase. It is noteworthy that small cell networks employ high-capacity backhaul links on millimeter-wave bands to develop multi-hop topologies in order to mitigate data transmission costs. The current static backhaul infrastructures cannot control severe fluctuating network traffic. To resolve this problem, this paper proposed a novel adaptive backhaul topology with the ability to adapt to different traffic patterns. Based on the graph theory, the adaptive system dynamically allows changes to the hybrid millimeter-wave backhaul architecture, and it also provides the possibility of effective channel allocation to each backhaul link to meet capacity and QoS demands. Also, regarding the importance of green networking in integrated-access-and-backhaul networks we proposed a dynamic optimization model which minimizes the overall energy consumption of UL/DL Decoupled NOMA heterogeneous networks in addition to providing the essential coverage and capacity. The proposed model optimizes user association/power utilization and presents an effective modular and scalable framework for analytical technology-oriented modeling of integrated multi-hop backhauls. The numerical results proved that the joint power optimization and hybrid backhaul architecture can increase the total network throughput by 18 percent compared to the current optimized static architectures. It can also reduce the energy consumption level by 30 percent, and enhance users’ quality satisfaction by 24.5 percent with respect to user distribution patterns.

Interference Aware Joint Power Control and Routing Optimization in Multi-UAV FANETs

In this work, we enhance the total network throughput performance of a FANET system consisting of multiple UAVs and a ground control station (GCS) by optimizing the multihop routing structure and total power budget required for communication in the presence of inter-UAV interference. To achieve this, an optimization problem is formulated to maximize the network throughput within a given power budget. However, due to the non-convex and integer nature of the problem, it is NP-hard. Hence, it is dealt with differently by solving two daughter problems iteratively, i.e., (1) power allocation (PA) and (2) optimal link selection (LS) for the given solution of PA. These subproblems are solved iteratively by utilizing alternating optimization to obtain the global optimal solution of the main problem. The study presents various numerical results, which validate the effectiveness of the proposed routing structure and provide insights into the impact of interference on system parameters.

Network Bandwidth Utilization Optimization with Quantum-Inspired Approach

As the popularity of mobile devices and services continues to increase, the demand for more efficient and effective algorithms to manage network utility is increased. Here, we aim to optimize Network Bandwidth Utilization (NBU) by managing user equipments (UEs) and base stations (BSs) association in 5G ORAN architecture. We consider the network total throughput as an objective function and try to find the suitable Cell Individual Offset (CIO) to trigger UE handover with the A3 event such that the network throughput is optimal. We formulate this problem into a Quadratic Unconstrained Binary Optimization (QUBO) problem such that we can explore the power of Quantum Annealing (QA), which is a promising approach to finding the optimal solution for the NP-Hard problem. We also analyze the penalty coefficients setting problem which impacts the outcome of QA significantly. We test our QUBO formulation on a dataset of 7 BSs and 150 UEs with Fujitsu’s third generation Digital Annealer (DA3). Using our QUBO formulation with DA3 solver with fine-tuning, we can improve the throughput with 60% hit rate on this dataset.

Performance analysis for MU-MIMO enabled LTE-U networks coexisting with D2D and WiFi network

LTE-Unlicensed (LTE-U) network is a type of cellular communication network operating the unlicensed spectrum. Offloading cellular traffic to WiFi or Device-to-Device (D2D) network can lead to interference among them. Applying Multiple-Input Multiple-Output (MIMO) technology in Cellular Base Station (CBS) and WiFi Access Point (WAP) can effectively reduce interference among D2D, WiFi and cellular networks. To our best knowledge, there is still no literature to explicitly study the characteristics of the traffic offloading in the Multiple-User MIMO (MU-MIMO) enabled network coexisting with D2D and WiFi networks. In this article, we thoroughly investigate the impact of D2D communication and MU-MIMO enabled WAP and CBS on the performance of the LTE-U network. More specifically, we derive the expressions of the downlink rates for cellular users, D2D users, and WiFi users with incomplete Channel State Information (CSI) feedback, and we validate our analysis through Monte-Carlo simulation. Numerous results illustrate the following conclusions. (i) Increasing the number of WiFi users, the length of CSI feedback, and the quantity of D2D pairs that reuse the channel with a single cellular user can increase the total throughput of the heterogeneous network. (ii) The total throughput decreases when more than two users are offloaded to D2D pairs, and increases as the number of offloaded users increases when less than six users are offloaded to WiFi network. (iii) Simultaneously offloading traffic to D2D pairs and WiFi network can obtain higher total throughput than offloading traffic to only one of them.

Intelligent Access to Unlicensed Spectrum: A Mean Field Based Deep Reinforcement Learning Approach

As the demand for mobile data traffic continues to grow, offloading data traffic to unlicensed spectrum is a promising approach that can relieve the pressure on cellular systems. Therefore, it is an urgent need to propose an unlicensed spectrum access method to guarantee the harmonious and efficient coexistence between cellular network technologies such as LTE and incumbent users such as WiFi in the unlicensed spectrum. However, existing coexistence schemes such as licensed assisted access (LAA) and LTE-unlicensed (LTE-U) still suffer from inefficient spectrum utilization and unsatisfactory fairness. In the paper, we formulate the optimization problem of the unlicensed spectrum access among multiple small bases (SBSs) as a game, and then solve the Nash Equilibrium (NE) with cooperative and distributed multi-agent deep reinforcement learning (MADRL). Specifically, a two level access framework for the coexistence scenario, which consists of feedback cycle and executive cycle, is first proposed, and then the key elements of MADRL including state, action, reward and Q-network are designed in detail based on the proposed access framework. To overcome the problems of learning divergence and prohibitive computation overhead in the coexistence scenario with multiple SBSs due to the non-stability phenomena, we adopt the mean field technology to solve the NE, which can simplify the process of solving NE by converting the interaction of an agent with the remaining multiple agents into an action with the average effect of them. Simulation results show that 1) the proposed algorithm can overcome the learning divergence problem and converge to the NE quickly, and 2) the proposed algorithm can achieve the bi-objective optimization of total throughput and fairness of the coexistence network, and can achieve better performance in terms of throughput and fairness compared with the baseline methods such as Cat-4 LBT, Cooperative LBT and Random schemes.

Deep Reinforcement Learning for Aerial Data Collection in Hybrid-Powered NOMA-IoT Networks

With the help of unmanned aerial vehicle (UAV), remote terminals that out of wireless coverage can be connected to the Internet of Things (IoT) networks. Currently, the IoT relies on a large number of low-cost wireless sensors with limited energy supply to realize ubiquitous monitoring and intelligent control. The hybrid-powered networks composed of wireless-powered communication (WPC) terminal and solar-powered UAV can solve the energy supply problem of the IoT networks, and the nonorthogonal multiple access (NOMA) technique can solve the massive access problem of IoT terminals. Exploiting these benefits, we investigate joint UAV 3-D trajectory design and time allocation for aerial data collection in hybrid-powered NOMA-IoT networks. To maximize the total fair network throughput, we jointly consider energy limitation, Quality of Service (QoS) requirements, and flight conditions. The problem is nonconvex and time-dimension coupled which is intractable to solve by traditional optimization methods. Therefore, we develop a deep reinforcement learning (DRL) algorithm called fair communication is accomplished by trajectory design and time allocation (FC-TDTA), which uses the deep deterministic policy gradient (DDPG) as its basis. Simulation results show that our proposed algorithm performs better than benchmarks in fair throughput maximization. The proposed FC-TDTA algorithm can make the UAV: 1) fly in appropriate direction and speed, so that the UAV can arrive at the charging station before the energy runs out and 2) conduct WPC energy transmission and data collection to achieve fair communication.

Double Deep Q-Network Method for Energy Efficiency and Throughput in a UAV-Assisted Terrestrial Network

Increasing the coverage and capacity of cellular networks by deploying additional base stations is one of the fundamental objectives of fifth-generation (5G) networks. However, it leads to performance degradation and huge spectral consumption due to the massive densification of connected devices and simultaneous access demand. To meet these access conditions and improve Quality of Service, resource allocation (RA) should be carefully optimized. Traditionally, RA problems are nonconvex optimizations, which are performed using heuristic methods, such as genetic algorithm, particle swarm optimization, and simulated annealing. However, the application of these approaches remains computationally expensive and unattractive for dense cellular networks. Therefore, artificial intelligence algorithms are used to improve traditional RA mechanisms. Deep learning is a promising tool for addressing resource management problems in wireless communication. In this study, we investigate a double deep Q-network-based RA framework that maximizes energy efficiency (EE) and total network throughput in unmanned aerial vehicle (UAV)-assisted terrestrial networks. Specifically, the system is studied under the constraints of interference. However, the optimization problem is formulated as a mixed integer nonlinear program. Within this framework, we evaluated the effect of height and the number of UAVs on EE and throughput. Then, in accordance with the experimental results, we compare the proposed algorithm with several artificial intelligence methods. Simulation results indicate that the proposed approach can increase EE with a considerable throughput.

Fairness Oriented H-NOMA Devices Association and Pairing in HCN Slicing Using Cooperation Games

Device association (DA) and pairing (DP) are presented in this letter to evaluate the fairness of different services between enhanced mobile broadband devices (eMBBDs) and ultra-reliability low latency communications devices (uRLLCDs) in RAN-slicing, which has emerged as a viable technique for enhancing total network throughput in heterogeneous cellular networks (HCNs) design. To optimize the eMBBDs' downlink (DL) sum rate while fulfilling the provisions of the uRLLC traffic. A cooperative Nash bargaining solution (NBS) is formulated to depict the association method. First, base stations (BSs) create random pairings between heterogeneous non-orthogonal multi-access (H-NOMA) devices. The BSs are classified into coalitions through the Hungarian approach. So, a two-band partition algorithm is evolved for the two BSs in each coalition to negotiate their allocated devices to improve the NBS utility. A multiplayer bargaining approach is constructed using this algorithm and the Hungarian technique. After finishing the DA process, we study the cooperative matching algorithm to optimize the pairing problem between devices' slices. Simulation results demonstrate that comparing the proposed approach to other schemes can achieve a significant DL rate distribution for eMBBDs, device-side latency for uRLLCDs, and fairness improvements.

UAV-Aided Aerial Reconfigurable Intelligent Surface Communications With Massive MIMO System

To capture the advantages of unmanned aerial vehicle (UAV) and reconfigurable intelligent surface (RIS) technologies, we propose the use of multiple passive aerial RISs in a massive multiple-input multiple-output (MIMO) network. Each aerial RIS is comprised of a RIS panel attached to a UAV, the intention being to support in extending network coverage from the massive MIMO base station. Compared with stationary RISs, our proposed aerial RISs (termed as UAV-RISs) have the ability to reach more users thanks to the line-of-sight links. Our aim is to maximise the total network throughput by finding the optimal power control coefficients at the base station and the phase shifts of the multiple RISs used in the system. This is jointly solved subject to the power consumption constraints, UAV-RIS deployment, and quality-of-service required at the users. We apply zero-forcing precoding for the beamforming design at the base station, and develop an iterative algorithm based on first-order approximation, block coordinate descent, and alternating optimisation technique. Numerical results demonstrate that our proposed method exhibits low computational-complexity and outperforms benchmark schemes in terms of the total network throughput achieved and improvement for the users with worst-case throughput.

When Aloha and CSMA Coexist: Modeling, Fairness, and Throughput Optimization

With the emerging unlicensed spectrum sharing and Machine-to-Machine communications, the coexistence performance of multiple devices with different access schemes operating at the same unlicensed bands has received significant research interests. As the two most representative random-access schemes, Aloha and Carrier Sense Multiple Access (CSMA) both found wide applications in unlicensed bands. Yet most of the studies have focused on the coexistence of CSMA-based networks, leaving the coexistence of Aloha-based and CSMA-based networks largely unexplored. The challenge originates from the lack of a coexistence model of slotted Aloha and CSMA. In this paper, the throughput performance of coexisting Aloha and CSMA networks is characterized and optimized by extending a unified analytical framework proposed for random-access networks. The analysis shows that different from the single-network case where the maximum throughput of CSMA is much higher thanks to carrier sensing, when Aloha and CSMA coexist, Aloha would significantly outperform CSMA if each network optimizes its own throughput performance without cooperation, leading to poor throughput performance for CSMA and severe unfairness. To achieve fair coexistence, inter-network cooperation is crucial. The optimal transmission probabilities of Aloha and CSMA for maximizing the total network throughput under a given throughput ratio are further derived, and applied to coexisting LTE Unlicensed and WiFi networks to optimize their coexistence performance.

“Electromagnetic barrier” assisted dynamic spectrum access in satellite internet communication confrontation

In this paper, the dynamic spectrum access is investigated in satellite internet. Firstly, to describe the confrontation characteristics of the electromagnetic environment, two opposing teams denoted as the blue team (BT) and red team (RT), are designed. In addition, an “electromagnetic barrier” jamming strategy is proposed to achieve the effect of promoting approach defence with the attack. Precisely, the “electromagnetic barrier” can significantly weaken the communication ability of enemies (i.e. RT) while protecting the (i.e. BT) communication quality. Secondly, internal interference, external interference and malicious jamming of BT and RT are considered, and expected weighted aggregate interference and jamming (EWAIJ) are introduced to integrate these interference. Moreover, minimising interference is taken as the optimisation goal. Thirdly, Game theory is introduced to describe the combinatorial optimisation problem. The process of each team finding the optimal spectrum access strategy is proved to be an exact potential energy game. These two sub-games together constitute the Stackelberg game framework. Finally, a distributed hierarchical confrontation channel selection algorithm (DHCCSA) is proposed to find the Stackelberg equilibrium solution. The simulation results show that the proposed algorithm can converge to a better effect compared with other algorithms. In addition, with the assistance of the “electromagnetic barrier,” BT is more robust than RT in terms of convergence rate, total utility, and total network throughput.

3D UAV Deployment in Multi-UAV Networks With Statistical User Position Information

Unmanned aerial vehicle (UAV) carried base stations can provide on-demand deployment for ground users. Given a target area to be serviced, this letter proposes a three-dimensional (3D) deployment method for multiple UAVs based on the knowledge of statistical user position information to maximize the total network throughput. The 3D deployment problem is divided into horizontal deployment problem and vertical deployment problem. Specifically, a virtual force field is built based on the statistical user distributions to help model the communication demand of users. Based on that, the horizontal placement scheme for each UAV is obtained by calculating the force balance points in the virtual field. The classical particle swarm optimization algorithm is adopted to determine the vertical coordinates of UAVs. In order to demonstrate the validity of the proposed algorithm, we also evaluate it through comparison with benchmark algorithms on a real-world telecommunication dataset. Experimental results show that the throughput performance can be improved by the proposed method compared with those benchmark schemes.

Opportunistic non-contiguous OFDMA scheduling framework for future B5G/6G cellular networks

In this paper, we consider the problem of resource allocation within Beyond 5G (B5G) and the envisioned 6G wireless networks with Cognitive Radio (CR) capability. CR technology allows opportunistic and dynamic access to the unused licensed spectrum bands. Unlike the conventional orthogonal frequency division multiple access (OFDMA) schemes that impose spectrum contiguity constraints in assigning the OFDMA resource blocks (RBs), the Dis-contiguous OFDMA (D-OFDMA) approach allows for flexible time–frequency RB scheduling, in which each contending CR user can access adjacent and/or non-adjacent RBs. This can provide a more efficient and flexible spectrum utilization compared to conventional OFDMA schemes. Specifically, we formulate the spectrum assignment and access problem as an optimization problem that attempts to effectively utilize the time–frequency spectrum holes of the D-OFDMA RBs. The main goal is to improve the total network throughput by maximizing the achieved sum-rate of all contending CR users subject to achieving a set of interference and performance requirement constraints. The formulated problem is classified as a binary-linear program (BLP), which is, in general, NP-hard. Thus, we develop a sub-optimal polynomial-time solution based on a relaxing-and-rounding method. Simulation results reveal that the proposed D-OFDMA RB scheduling protocol significantly improves the network throughput while maintaining fairness compared to conventional contiguous-based OFDMA protocols. Specifically, the proposed protocol achieves up to 90% and 150% performance improvements over the contiguous frequency-OFDMA and contiguous time-OFDMA, respectively.

Wireless Lan Performance Enhancement Using Double Deep Q-Networks

Due to the exponential growth in the use of Wi-Fi networks, it is necessary to study its usage pattern in dense environments for which the legacy IEEE 802.11 MAC (Medium Access Control) protocol was not specially designed. Although 802.11ax aims to improve Wi-Fi performance in dense scenarios due to modifications in the physical layer (PHY), however, MAC layer operations remain unchanged, and are not capable enough to provide stable performance in dense scenarios. Potential applications of Deep Learning (DL) to Media Access Control (MAC) layer of WLAN has now been recognized due to their unique features. Deep Reinforcement Learning (DRL) is a technique focused on behavioral sensitivity and control philosophy. In this paper, we have proposed an algorithm for setting optimal contention window (CW) under different network conditions called DRL-based Contention Window Optimization (DCWO). The proposed algorithm operates in three steps. In the initial step, Wi-Fi is being controlled by the 802.11 standards. In the second step, the agent makes the decisions concerning the value of CW after the TRAIN procedure for the proposed algorithm. The final phase begins after the training, defined by a time duration specified by the user. Now, the agent is fully trained, and no updates will be no longer received. Now the CW is updated via the OPTIMIZE process of DCWO. We have selected total network throughput, instantaneous network throughput, fairness index, and cumulative reward, and compared our proposed scheme DCWO with the Centralized Contention window Optimization with DRL (CCOD). Simulation results show that DCWO with Double Deep Q-Networks (DDQN) performs better than CCOD with (i) Deep Deterministic Policy Gradient (DDPG) and (ii) Deep Q-Network (DQN). More specifically, DCWO with DDQN gives on average 28% and 23% higher network throughput than CCOD in static and dynamic scenarios. Whereas in terms of instantaneous network throughput DCWO gives around 10% better results than the CCOD. DCWO achieves almost near to optimal fairness in static scenarios and better than DQN and DDPG with CCOD in dynamic scenarios. Similarly, while the cumulative reward achieved by DCWO is almost the same with CCOD with DDPG, the uptrend of DCWO is still encouraging.

Reliability and robust resource allocation for Cache-enabled HetNets: QoS-aware mobile edge computing

Mobile edge computing is a key feature of next-generation mobile networks aimed at providing a variety of services for different applications by performing related processing tasks closer to the user equipment. Due to the high frame loss rate during multi-hop transmissions in wireless edge computing networks, the problem of reliability and system safety will be of great importance. Data caching is a necessity to provide reliable edge computing and, at the same time a major consumer of energy. So, we propose a reliable mobile edge computing system using active caching and robust resource allocation approaches. This paper presents an energy-efficient stochastic network calculus framework to control edge-computing data flows for active data-caching systems. In accordance with the entrance processes of different QoS-class data streams, closed-form problems were formulated to determine the correlation between resource utilization and the violation probability of each data flow. Also, in the access layer, this paper proposes a reliable user association and resource allocation approach which maximizes the overall energy efficiency of cache-enabled cellular networks in addition to improving system safety. In this energy-cooperative approach, the power can be shared among the cells using a grid network. The simulation results show that the proposed approach can effectively increase the network reliability and power efficiency while guaranteeing the acceptable fairness level for uniform and hotspot user distribution models. The numerical results also exhibit considerable power saving for different traffic models in addition to increasing the total network throughput by up to 25%.

Backscatter-Assisted Wireless Powered Communication Networks Empowered by Intelligent Reflecting Surface

Intelligent reflecting surface (IRS) has recently been emerged as an effective way for improving the performance of wireless networks by reconfiguring the propagation environment through a large number of passive reflecting elements. This game-changing technology is especially important for stepping into the Internet of Everything (IoE) era, where high performance is demanded with very limited available resources. In this paper, we study a backscatter-assisted wireless powered communication network (BS-WPCN), in which a number of energy-constrained users, powered by a power station (PS), transmit information to an access point (AP) via backscatter and active wireless information transfer, with their communication being aided by an IRS. Using a practical energy harvesting (EH) model which is able to capture the characteristics of realistic energy harvesters, we investigate the maximization of total network throughput. Specifically, IRS reflection coefficients, PS transmit and AP receive beamforming vectors, power and time allocation are designed through a two-stage algorithm, assuming minimum mean square error (MMSE) receiver at the AP. The effectiveness of the proposed algorithm is confirmed via extensive numerical simulations. We also show that our proposed scheme is readily applicable to practical IRS-aided networks with discrete phase shift values.

A queueing theory approach to traffic offloading in heterogeneous cellular networks

Future and current cellular networks encounter an unprecedented growth of mobile devices traffic, imposing various critical challenges that should be thoroughly addressed. Catering for such enormous amount of traffic demand via cellular networks significantly increases the network congestion and degrades the achievable quality of service (QoS). Thus, traffic offloading has been suggested to tackle the expected high growth rate in cellular networks and alleviate the foreseen performance degradation. Most of the previous works in the literature aim to increase the number of offloaded users, and, as a consequence, increase the number of admitted users to the network. However, this has been done without considering users’ traffic load. In this paper, we consider the offloading problem in a HetNet with D2D communication taking into account the traffic load in the network. First, we study the maximization of the total network throughput while maintaining the system queues stability, which is found to be a non-convex problem. Thus, we propose a heuristic offloading algorithm that is shown to achieve comparable performance to that obtained through an exhaustive search algorithm. Additionally, we derived the average delay encountered by the network users packets under such setting. Second, we study the maximization of the total network energy efficiency (EE) while maintaining the network queues stability and the maximum transmit power constraint of each user. The optimization problem aims to find the optimal offloading decision besides the transmit powers of each user in the network according to the obtained offloading decision. We propose another heuristic EE-based offloading algorithm due to the non-convexity of the problem. Numerical simulations demonstrate the effectiveness of the proposed algorithms in outperforming similar algorithms in the literature that do not take into consideration the users’ traffic load into the offloading decision.