D2D Pairs Research Articles

Machine Learning-Based Resource Allocation Algorithm to Mitigate Interference in D2D-Enabled Cellular Networks

Mobile communications have experienced exponential growth both in connectivity and multimedia traffic in recent years. To support this tremendous growth, device-to-device (D2D) communications play a significant role in 5G and beyond 5G networks. However, enabling D2D communications in an underlay, heterogeneous cellular network poses two major challenges. First, interference management between D2D and cellular users directly affects a system’s performance. Second, achieving an acceptable level of link quality for both D2D and cellular networks is necessary. An optimum resource allocation is required to mitigate the interference and improve a system’s performance. In this paper, we provide a solution to interference management with an acceptable quality of services (QoS). To this end, we propose a machine learning-based resource allocation method to maximize throughput and achieve minimum QoS requirements for all active D2D pairs and cellular users. We first solve a resource optimization problem by allocating spectrum resources and controlling power transmission on demand. As resource optimization is an integer nonlinear programming problem, we address this problem by proposing a deep Q-network-based reinforcement learning algorithm (DRL) to optimize the resource allocation issue. The proposed DRL algorithm is trained with a decision-making policy to obtain the best solution in terms of spectrum efficiency, computational time, and throughput. The system performance is validated by simulation. The results show that the proposed method outperforms the existing ones.

Efficient resource allocation using whale optimization for cell-free massive MIMO networks in 6G HetNet

The increase in data rate demands has driven the development of Cell-Free massive Multiple-Input Multiple-Output (CFmMIMO) technology in 6G Heterogeneous Networks (HetNets). This paper proposes an integration of Unmanned Aerial Vehicles (UAVs) and Device-to-Device (D2D) communication in CFmMIMO, aiming to enhance network capacity, coverage, and service delivery in next-generation wireless networks. The paper investigates uplink transmission in a network where Access Points (APs) have imperfect channel state information. It considers Cell-free User Equipment (CUE), UAVs, and D2D pairs, deriving closed-form uplink achievable rates for each user type. Moreover, two optimization problems are formulated to enhance user data rates: one focuses on maximizing the sum data rate through pilot assignment, while the other aims to achieve weighted max–min power control through power allocation. Furthermore, to address these problem, we propose a Modified Whale Optimization Algorithm (MWOA) and a novel Pilot Assignment based on Whale Optimization Algorithm (PAWOA). Performance studies demonstrate significant improvements over state-of-the-art algorithms.

Energy-efficient resource allocation for D2D communication underlaying cellular networks with incomplete CSI

Device-to-device (D2D) communication supports direct communications between nearby devices, which has potential to improve network capacity, spectrum efficiency and energy efficiency. Considering the high overhead to obtain the complete channel state information (CSI), we investigate resource allocation problems of D2D communication underlaying cellular networks with incomplete CSI to minimize the total power consumption. To deal with the challenge brought by incomplete CSI in estimating the instantaneous rates of D2D pairs (DPs), we consider two QoS metrics in terms of the expected rate and outage probability using statistical CSI. Based on that, we first investigate the energy-efficient power control problem for single cellular user (CU) and single DP sharing the same spectrum. With rigorous theoretical analysis of the intrinsic properties of CU rate and two QoS metrics, we design an optimal energy-efficient power control (EPO) algorithm for single CU and single DP. Using EPO as a building block, we then propose an energy-efficient resource allocation algorithm for multiple CUs and multiple DPs with incomplete CSI. Simulation results show that our algorithms consume the lowest powers compared with two baseline algorithms.

Full-Duplex Unmanned Aerial Vehicle Communications for Cellular Spectral Efficiency Enhancement Utilizing Device-to-Device Underlaying Structure

Unmanned aerial vehicle (UAV) communications have gained recognition as a promising technology due to their unique characteristics of rapid deployment and flexible configuration. Meanwhile, device-to-device (D2D) and full-duplex (FD) technologies have emerged as promising methods for enhancing spectral efficiency and offloading traffic. One significant advantage of UAVs is their ability to partition suitable D2D pairs to increase cell capacity. In this paper, we present a novel network model in which UAVs are considered D2D pairs underlaying cellular networks, integrating FD into the communication links between UAVs to improve spectral efficiency. We then investigate a resource allocation problem for the proposed FD-UAV D2D underlaying structure model, with the objective of maximizing the system’s sum rate. Specifically, the UAVs in our model operate in full-duplex mode as D2D users (DUs), allowing the reuse of both the uplink and downlink subcarrier resources of cellular users (CUs). This optimization challenge is formulated as a mixed-integer nonlinear programming problem, known for its NP-hard and intractable nature. To address this issue, we propose a heuristic algorithm (HA) that decomposes the problem into two steps: power allocation and user pairing. The optimal power allocation is solved as a nonlinear programming problem by searching among a finite set, while the user pairing problem is addressed using the Kuhn–Munkres algorithm. The numerical results indicate that our proposed FD-MaxSumCell-HA (full-duplex UAVs maximizing the cell sum rate with a heuristic algorithm) scheme for FD-UAV D2D underlaying models outperforms HD-UAV underlaying cellular networks, with improved access rates for UAVs in FD-MaxSumCell-HA compared to HD-UAV networks.

Study on covert rate in the D2D networks with multiple non-colluding wardens

This paper investigates the covert communication of cellular link in the device-to-device (D2D)-enabled underlaying cellular network, including a base station, a cellular user, a D2D pair, and multiple non-colluding wardens. To conduct covert communication between the base station and the cellular user without being detected by the wardens, the underlaid D2D pair transmits with a random power to add uncertainty to wardens. We first model the detection of multiple non-colluding wardens, and the average minimum detection error probability of the worst case is derived. To measure the covert performance of the cellular link, the average covert rate of the cellular link and the outage probability of the D2D link are explored. Then, we formulate an optimization problem of maximizing the average covert rate under the constraint of the average minimum detection error probability, and an optimization algorithm is designed to solve the optimization problem to identify the optimal transmit power of the D2D pair. Last, the extensive numerical results are presented to show the impacts of network parameters on the covert performance, which indicates that although D2D communication causes interference to the cellular link, it can also help achieve covert communication by carefully designing its maximum transmit power.

Uplink resource shared interference mitigation scheme for in-band D2D underlay 5G networks

The benefits of Device-to-Device (D2D) communication often include increased overall system throughput, reduced latency, and increased spectrum efficiency. These advantages usually come from improved spatial reuse of time and frequency resources and the last but not least, the proximity of users engaged into D2D communication. Radio Resource allocation is a critical issue in D2D model and if not dealt with carefully, it can lead to significant interference to both pure cellular and D2D users, thereby proving detrimental to the desired outcomes. This paper presents novel Uplink Resource Shared Interference Mitigation Scheme called URSIMS. URSIMS adopts guard zones, further it implements radio resource allocation algorithm (RRAA) and distance dependent dynamic power control algorithm (DDDPA) with the goal to mitigate interference and achieve increased overall system throughput and spectral efficiency. URSIMS has been compared with other similar state of the art both for static and user mobility based scenarios; it is found that URSIMS responded adequately well and outperformed Random and Greedy schemes in terms of salient performance indicators including overall system throughput, pure cellular user throughput, spectral efficiency and D2D pair throughout. URSIMS did provide sustained higher system throughput, which significantly improved further in case of increased user mobility while compared to Random and Greedy schemes respectively.

Interference‐aware feedback based iterative Hungarian approach to allocate multiple channels to multiple D2D pairs to maximize underlay D2D network capacity

SummaryThe spectral efficiency of a cellular network can be increased significantly by allowing spatial reuse of its spectrum by an underlay device‐to‐device (D2D) network. In an underlay D2D network, devices in close vicinity are allowed to establish low‐power direct links with little to no involvement of the base station. In order to increase the spectral efficiency and the number of devices with channel access, multiple D2D pairs may transmit in each cellular channel. Additionally, each pair can be allowed to utilize multiple channels to transmit so as to maximize the D2D network capacity. This multiple‐pair multiple‐channel (MPMC) strategy is quite appealing but is limited by the resultant additional aggregate interference and the inherent complexity, hence necessitating the need for a fast and reliable channel allocation scheme. This work proposes a polynomial‐time iterative Hungarian assignment with feedback (IHAF) algorithm for multiple channel allocations amongst multiple D2D pairs that increases the D2D network capacity manifold while maintaining the desired minimum capacity for each cellular user.

Performance evaluation of pair selection algorithms in device-to-device communication using relay-assisted techniques

Communication services that are dependable are crucial, particularly during emergencies when the regular infrastructure for communication may be disrupted or nonexistent. In such situations, device-to-device (D2D) communication can be a helpful choice since it allows user equipment (UE) that is close to one another to connect directly, bypassing the cellular network infrastructure. The primary focus of this thesis is the application of D2D communication in a decentralized emergency scenario with a damaged eNodeB. The main objective is to find an appropriate strategy for finding and selecting D2D couples by simulating several methods in MATLAB. This study compares three D2D pair selection algorithms: distance-based, Signal-to-Interference and Noise Ratio (SINR)-based, and data rate-based distance-based. The simulation results show that the data rate-based strategy is the most effective method for selecting D2D couples in emergency scenarios. In contrast to algorithms that rely on distance and SINR, this one reduces the chance of an outage by 20%. Bit error rate (BER), capacity, spectral efficiency, and energy efficiency are the three types of links that are assessed: direct links, relay links, and UE relay links. The results show that, with the lowest BER and maximum data throughput, the direct link is the most reliable and efficient communication option. However, the relay connection and the UE relay link show better overall spectral efficiency in comparison to the direct link, indicating their ability to transport more data per unit of bandwidth. The option that consumes the least energy among the three is the direct link. The study demonstrates the great potential of D2D communication in emergency scenarios where conventional communication infrastructure may not be available. The direct link is the most dependable and effective alternative for communication, according to the data, although the UE link can still function effectively in the event that the direct link is compromised. The data rate-based method is a useful strategy for finding and choosing D2D partners. The results of this study can direct the development of D2D emergency communication solutions in 5G networks.

An Approach to Maximize the Admitted Device-to-Device Pairs in MU-MIMO Cellular Networks

Due to the shortage of wireless resources and the emergence of a large number of users, determining how to guarantee the quality-of-service (QoS) requirements of users and make more users work in the same spectrum has become an urgent research topic. In this paper, we study a multi-user MIMO (MU-MIMO) cellular network system model in which cellular users (CUs) share the same spectrum resource with multiple device-to-device (D2D) pairs. To maximize the number of admitted D2D pairs sharing the same spectrum with the CUs, a joint power allocation and channel gain (JPACG) algorithm is proposed. The optimization problem is divided into two steps to be solved. First, the power allocation of CUs without D2D pairs admitted is solved. Then, the optimization problem is transformed into minimizing the interference to CUs when CUs are treated as primary users. The admittance order of D2D pairs is determined by the transmission power and channel gain. The proposed algorithm uses a convex optimization algorithm to solve the problem of power allocation joint interference channel gain in order to maximize the number of admitted D2D pairs under the constraints of the signal-to-interference-plus-noise ratio (SINR) threshold and maximum transmission power. In addition, the effect of the number of admitted D2D pairs on the total sum rate of all users is also analyzed. The simulation results show that the proposed JPACG algorithm can achieve better performance in admitting D2D pairs.

Fair resource allocation and SBS selection for MC-NOMA-based HetNets supporting D2D communications

This paper studies the application of downlink multi-carrier non-orthogonal multiple access (MC-NOMA) and device-to-device (D2D) in heterogeneous networks (HetNets). In the considered model, the small base stations (SBSs) can reuse the sub-channels (SCs) of the macro base stations (MBS). The paper aims to maximize the system sum-rate while managing co-tier and co-channel interference by optimizing joint SBS selection, fair SC allocation, and power allocation, under the constraints of guaranteeing a certain amount of resources to all cellular users (CUs)and D2D pairs and satisfying CUs’ minimum signal-to-interference-plus-noise ratio (SINR) requirements. However, obtaining an efficient exact solution is challenging due to the non-convexity of the mixed integer optimization problem. To deal with this issue, the problem is decoupled into two sub-problems: 1- SBS selection and fair SC allocation problem and 2- power allocation problem. Then, a four-sided many-to-one matching method is proposed to solve the SBS selection and fair SC allocation problem jointly. The performance of the SC fair allocation algorithm is investigated in terms of the system quality of services (QoS) using a proposed mathematical expression. Furthermore, a novel dynamic search operator is introduced to enhance the performance of the matching-based algorithm. Next, the non-convex power allocation problem is transformed into a deep reinforcement learning (DRL) based framework, and the deep deterministic policy gradient (DDPG) approach is employed to solve the problem. Moreover, a joint SBS selection, SC allocation, and power allocation iterative algorithm is proposed to improve the system sum-rate. Simulation results validate our analysis and indicate the superiority of the proposed algorithms in terms of the system sum-rate compared to the benchmark algorithms.

Optimal D2D power for secure D2D communication with random eavesdropper in 5G‐IoT networks

SummaryIn the rapidly evolving landscape of fifth generation Internet of Things (5G‐IoT) networks, Device‐to‐Device (D2D) communication has emerged as a promising paradigm to enhance secrecy transmission rate (STR) and connectivity. However, the security of D2D communications in the presence of eavesdroppers remains a critical challenge. This article investigates the problem of optimizing D2D transmit power to achieve secure D2D communication while considering the presence of random eavesdroppers in 5G‐IoT networks. We propose a novel secrecy‐based power control approach (SRMWPCA) approach to model the random distribution of eavesdroppers in the network, taking into account their varying distances from D2D pairs and deliberately increasing interference at the eavesdropper's link. By leveraging tools from stochastic geometry, we derive an analytical expression for the secrecy transmission probability (STP), which quantifies the probability of eavesdroppers successfully decoding the D2D transmission. In this analysis, we have incorporated practical considerations such as channel fading, path loss, and interference from other devices. To enhance the security of D2D communication, we formulate an optimization problem to determine the optimal transmit power levels for D2D pairs, subject to constraints on the secrecy transmission probability and interference to the cellular network. We propose an efficient algorithm to find the power allocation that maximizes the secrecy outage performance while meeting these constraints. Simulation results demonstrate the effectiveness of the proposed approach in achieving secure D2D communication in 5G‐IoT networks with random eavesdroppers. The performance of the proposed SRMWPCA approach improved by 23.25% and 20.9% compared with standard approaches in terms of the secrecy rate and throughput of the users from malicious attacks.

A UAV-Assisted Secure Communication System by Jointly Optimizing Transmit Power and Trajectory in the Internet of Things

Secure communication technology has attracted more and more attention to improve the security of the Internet of Things (IoT). In this article, we discuss the maximum secrecy capacity problem for a mobile unmanned aerial vehicle (UAV) assisted secure communication in IoT, where the UAV relays the covert information from the transmitter (Alice) to the legal receiver (Bob) without detection by an eavesdropper (Eve) though sharing the spectrum with the ground device-to-device (D2D) communications. Our design aims to maximize the sum secrecy capacity Bob received by optimizing the transmit power and the UAV’s trajectory jointly while guaranteeing that the normal communication behavior between ground D2D pairs is not affected. Therefore, we propose a jointly optimizing the transmit power and UAV trajectory algorithm (JOTUTA) based on the block coordinate descent (BCD) method. Specifically, we first transform the non-convex problem of transmit power based on a fixed UAV’s trajectory into a convex problem by D.C. (difference of two convex functions) programming. Secondly, we solve the problem of UAV’s trajectory optimization with given transmit power by the successive convex approximation (SCA) method. Experiments results show the effectiveness of JOTUTA and the advantages of improving the secrecy capacity are verified compared with existing work.

A Deep Reinforcement Learning Scheme for Sum Rate and Fairness Maximization Among D2D Pairs Underlaying Cellular Network With NOMA

Device-to-device (D2D) communication is an emerging technology in 5G and upcoming 6G networks due to its properties to enhanced spectral efficiency (SE), energy-efficiency (EE), and sum rate. Despite these advantages, co-channel and cross-channel interference, and ultra-massive connectivity are major issues which can deteriorate performance of any implemented solution in this environment. To address these issues, in this paper, we integrated the power domain non-orthogonal multiple access techniques (PD-NOMA) on the base station (BS). NOMA serves more than one user using the same resource block (RB) and reduces the effect of interference at CUs due to the presence of successive interference cancellation (SIC). The problem is formulated as a mixed-integer non-linear programming (MINLP) with associated resources and power constraints of the BS and DDPs with an aim to maximize the sum rate and fairness among the NOMA-enabled CUs and D2D pairs (DDPs). We firstly used the centralized deep deterministic policy gradient (DDPG) and arithmetic-geometric mean approximation (AGMA) technique to reduce cross-channel interference (CR-CI) and control the power. Then, to provide fairness to all the users, we transformed the proposed solution into distributed deep deterministic policy gradient (D3PG). Also, the successive convex approximation technique is then integrated into the D3PG to mitigate the effect of co-channel (CO-CI) interference among DDPs. The experimental results show that the proposed scheme has superior performance with respect to sum rate and fairness. Also, the results reveal that the proposed scheme has 21.05%, 34.21%, and 49.8% higher sum rate in comparison to DDPG, Deep dueling, and DQN scheduling.

Sum Rate Maximization and Consistency in D2D Communication Based on ACO and Game Theory

Cellular network is the most popular network setup among today’s wireless communication systems. The primary resource in a cellular system is the spectrum for communication, and owing to the rising number of cellular users, the spectrum that is currently accessible from different service providers is depleting quickly. The resource or channel allocation is the most hindering task in cellular networks. Many efforts have been taken by many researchers to allocate the resources properly in order to increase the channel utilization and it is found that one effective method for reusing the channels inside a cell is device to device (D2D) communication. D2D communication was first developed in order to achieve the fundamental goals of fast data rates, widespread coverage with little latency, energy efficiency, and low per-information transmission costs. The dynamic behaviour of this network set-up again increases the risk of different types of interferences, which is another issue faced by the researchers. In this paper an effort is taken to understand and solve various aspects of channel allocation and Cellular networks have incorporated interference management in D2D communication especially. The two major issues of allocation of resource and management of interference in D2D communication is addressed here. This paper considers the meta heuristic algorithm namely Ant Colony Optimization (ACO) for resource allocation issue and interference management. The sum rate maximization is achieved through Game theory along with the concept of resource exchange in turn to increase the consistency of D2D communication setup. The results demonstrate that our algorithm can significantly increase the sum rate of D2D pairs when compared to other algorithms suggested by related works.

Covert Rate Study for Full-Duplex D2D Communications Underlaid Cellular Networks

Device-to-device (D2D) communications underlaid cellular networks have emerged as a promising network architecture to provide extended coverage and high data rate for various Internet of Things (IoT) applications. However, because of the inherent openness and broadcasting nature of wireless communications, such networks face severe risks of data privacy disclosure. This paper investigates the covert communications in such networks for providing enhanced privacy protection. Specifically, this paper explores the critical covert rate performance in a full-duplex D2D communication underlaid cellular network consisting of a base station, a cellular user, a D2D pair with a transmitter and a full-duplex receiver, and a warden, where the D2D receiver can operate over either the full-duplex (FD) mode or the half-duplex (HD) mode. We first derive transmission outage probabilities of cellular and D2D links under the FD and HD modes, respectively. Based on these probabilities, we further provide theoretical modelling for the covert rate under each mode and explore the corresponding covert rate maximization by jointly optimizing the transmit powers of the D2D pair and the cellular user. To improve the covert rate performance, we propose a general mode in which the D2D receiver can flexibly switch between these two modes. Under the general mode, we also investigate the theoretical modelling and maximization problems of covert rate. Finally, we present extensive numerical results to illustrate the covert rate performances under the FD, HD, and general modes.

Interference Mitigation Strategy for D2D Communication in 5G Networks

Device-to-Device (D2D) communication is one of the most promising developments in 5G networks. D2D communication can reduce communication latency and increase spectrum efficiency and system capacity. However, implementing D2D communication in reuse mode with a traditional cellular network can result in severe interference that can significantly reduce network performance, as both networks share the same resources. It is essential to mitigate interference to improve network capacity when D2D communication coexists with traditional cellular networks. An effective power control strategy can help mitigate the potential detrimental impact of interference and maximize the potential benefits of D2D communication. In this study, a dynamic transmission power control strategy was proposed for D2D transmitters to allow them to dynamically adjust transmission power, aiming to minimize interference and maximize network capacity. The distance between and the number of D2D pairs that experience interference were the primary parameters considered for the proposed strategy, while its complexity was in polynomial time. A simulation was carried out to evaluate the proposed strategy and compare it with fixed and ranged-based approaches, and the results validated its effectiveness.

A bipartite graph based proportional fair scheduling strategy to improve throughput with multiple resource blocks

<p>The fifth-generation wireless communication is expected to provide a huge amount of capacity to cater to the need of an increasing number of mobile consumers, which can be satisfied by device-to-device (D2D) communication. Reusing the cellular user’s resources in an efficient manner helps to increase the spectrum efficiency of the network but it leads to severe interference. The important point in reusing cellular user resources is that D2D communication should not affect the cellular user’s efficiency. After achieving this requirement, the focus is now turned toward the allocation of resources to D2D communication. This resource allocation strategy is to be designed in such a way that it will not affect communication among the cellular user (CU). This scheme improves various performance objectives. This paper aims at designing a proportional fair resource allocation algorithm based on the bipartite graph which maintains the quality of service (QoS) of CUs while providing D2D communication. This algorithm can be merged with any other scheme of resource allocation for improving QoS and adopting changing channels. In this scheme, a D2D pair can be allocated with one or more than one resource blocks. The MATLAB simulations analyze the performance of the proposed scheme.</p>

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.

Autoencoder-based efficient resource allocation in device-to-device communication

Device-to-device (D2D) communication offloads the base station (BS) traffic, enhances spectral efficiency, and provides efficient data transmission with low latency between devices in close proximity. However, there are many issues in D2D communication, such as mode selection, device discovery, security, privacy, resource allocation, and interference management. At the time of the resource allocation, there is an issue of interference and eavesdropping by the malicious user on D2D pairs. Here, an eavesdropper can decrypt the data exchanged between the D2D user devices. This paper focuses on the efficient resource allocation in D2D communication that improves sum rate, computational complexity, and information secrecy capacity by reusing cellular user’s (CU) resources. Here, we propose an efficient resource allocation scheme incorporating the artificial intelligence (AI) algorithm-based autoencoder that overcomes information ambiguity when there is the same weight in the data rate matrix of the Hungarian algorithm. First, we identify cellular and D2D users’ communication data rates and generate a data rate matrix using the channel gain, received signal, and signal-to-interference-plus-noise ratio (SINR) values. The generated data rate matrix is passed as an input into an autoencoder. An autoencoder performs the dimensionality reduction using latent space and reconstructs the optimal cost matrix. The AI model’s result shows that the autoencoder achieves 83% training and 90% validation accuracy using the mean squared error (MSE) loss function. Then, we determine the optimal cost using the Hungarian algorithm. As we aim to maximize the sum rate and reduce the computation cost, our simulation result shows that the resource allocation performs efficiently using the proposed scheme.

Joint Packet Length and Power Optimization for Covert Short-Packet D2D Communications

This paper proposes a joint optimization mechanism for packet length and power in the scenario of covert short packet D2D communication, so as to effectively improve the communication performance between D2D pairs subject to the covert constraint. Specifically, we construct a short-packet D2D communication model assisted by covert communication, and further propose the notion of effective covert throughput (ECT) to quantitatively characterize the trade-off between the covertness and reliability of IoT state monitoring information transmission. Secondly, in the constructed communication scenario, we analyze the detection error probability of the warden and clarify that the existing equal power transmission of pilot and data signals can minimize the detection performance of the warden. However, this strategy is achieved by compromising the transmission performance of the system, which means that the ECT of D2D pair may not be optimal. Thirdly, we aim to maximize the ECT of D2D pair and construct a joint optimization problem for pilot transmission power, data transmission power, and packet length. Furthermore, a joint optimization algorithm based on the 2D search is adopted to obtain the optimal solution of the established optimization problem. Simulation results demonstrated that the transmission performance of the joint optimization algorithm is better than that of the scheme of the equal power scheme on the premise of ensuring the covertness.