Uplink Data Transmission Research Articles

Energy-efficient resource allocation for UAV-aided full-duplex OFDMA wireless powered IoT communication networks

The rapid development of wireless-powered Internet of Things (IoT) networks, supported by multiple unmanned aerial vehicles (UAVs) and full-duplex technologies, has opened new avenues for simultaneous data transmission and energy harvesting. In this context, optimizing energy efficiency (EE) is crucial for ensuring sustainable and efficient network operation. This paper proposes a novel approach to EE optimization in multi-UAV-aided wireless-powered IoT networks, focusing on balancing the uplink data transmission rates and total system energy consumption within an orthogonal frequency-division multiple access (OFDMA) framework. This involves formulating the EE optimization problem as a Multi-Objective Optimization Problem (MOOP), consisting of the maximization of the uplink total rate and the minimization of the total system energy consumption, which is then transformed into a Single-Objective Optimization Problem (SOOP) using the Tchebycheff method. To address the non-convex nature of the resulting SOOP, characterized by combinatorial variables and coupled constraints, we developed an iterative algorithm that combines Block Coordinate Descent (BCD) with Successive Convex Approximation (SCA). This algorithm decouples the subcarrier assignment and power control subproblems, incorporates a penalty term to relax integer constraints, and alternates between solving each subproblem until convergence is reached. Simulation results demonstrate that our proposed method outperforms baseline approaches in key performance metrics, highlighting the practical applicability and robustness of our framework for enhancing the efficiency and sustainability of real-world UAV-assisted wireless networks. Our findings provide insights for future research on extending the proposed framework to scenarios involving dynamic UAV mobility, multi-hop communication, and enhanced energy management, thereby supporting the development of next-generation sustainable communication systems.

Joint passive beamforming and deployment design for active intelligent reflecting surface-assisted energy harvesting-cognitive radio sensor networks.

To prolong the network lifetime of energy harvesting-cognitive radio sensor networks (EH-CRSNs), this paper integrates active intelligent reflecting surface (IRS) to support both downlink EH and uplink data transmissions and formulates a constrained non-convex optimization problem that maximizes the net energy gain. To solve this problem, a joint passive beamforming and IRS deployment mechanism is proposed to determine the optimal deployment location of the active IRS and optimally configure the IRS reflection coefficient matrices. Specifically, the net energy gain maximization problem at a specified location is divided into two sub-problems according to its characteristics: maximizing the cumulative energy harvested by all CRSNs nodes in the downlink and minimizing the energy consumption of cluster heads in uplink data transmissions, with each sub-problem being solved independently. Furthermore, this paper explores how factors such as the number of active reflecting elements and the amplification power budget influence the optimal deployment location of the active IRS. The findings from this investigation can guide the design of active IRS-assisted EH-CRSNs under specified parameters. Simulation results confirm the aforementioned conclusions and highlight the benefits of the proposed joint mechanism in enhancing net energy gain over various benchmark mechanisms.

Maximizing Uplink Data Transmission of LEO-Satellite-Based Wireless-Powered IoT

Maximizing Uplink Data Transmission of LEO-Satellite-Based Wireless-Powered IoT

Support of resourses redistribution in NB-IоT LTE networks

The NB-IoT is expected to be used with the deployed LTE network as well as future 5G networks. The rapid development of the IoT concept has entailed the need to provide wireless communication to a huge number of devices included in the infrastructure. As part of the 5G NR standard for such devices, Massive Machine Communications (m MTC) technology is focused on optimizing the use of network resources to support a large number of stable connections per unit area. The Narrowband Internet of Things (NB-IoT) has been analyzed to enable the connectivity of a wide range of new IoT devices and services to the mobile network. The NB-IoT is also shown to be designed for fixed devices with low data transmission and low consumption, leading to an increase in the number of interconnected devices. In turn, standard NB-IoT modules attempting to simultaneously request radio channel resources for uplink data transmission may suffer from random access preamble collision. The proposed model describes the macro- and microcells of an NB-IoT LTE cluster. An increase in the efficiency of using the bandwidth of networks based on macro- and microcells with a high concentration of users of the NB-IoT LTE networks is shown. Numerical results of an analysis of identifying factors affecting system performance are presented. A linear increase in throughput has been revealed depending on the throughput of the macrocell when using the shared resource of the macrocell for a microcell of equal size without prior repacking of channels when servicing moving subscribers. In the absence of moving subscribers, the additional load is serviced, and the efficiency of using a macro cell increases almost 3 times. In addition, repacking channels significantly increases system throughput.

Deep Learning-Driven Interference Perceptual Multi-Modulation for Full-Duplex Systems

In this paper, a novel data transmission scheme, interference perceptual multi-modulation (IP-MM), is proposed for full-duplex (FD) systems. In order to unlink the conventional uplink (UL) data transmission using a single modulation and coding scheme (MCS) over the entire assigned UL bandwidth, IP-MM enables the transmission of UL data channels based on multiple MCS levels, where a different MCS level is applied to each subband of UL transmission. In IP-MM, a deep convolutional neural network is used for MCS-level prediction for each UL subband by estimating the potential residual self-interference (SI) according to the downlink (DL) resource allocation pattern. In addition, a subband-based UL transmission procedure is introduced from a specification point of view to enable IP-MM-based UL transmission. The benefits of IP-MM are verified using simulations, and it is observed that IP-MM achieves approximately 20% throughput gain compared to the conventional UL transmission scheme.

MagSonic: Hybrid Magnetic-Ultrasonic Wireless Interrogation of Millimeter-Scale Biomedical Implants With Magnetoelectric Transducer.

Wireless interrogation (power and data transfer) of biomedical implants, miniaturized to millimeter (mm) dimensions, is critical for their chronic operation. Achieving simultaneous wireless power and data transfer at deep sites reliably within safety limits for closed-loop sensing/actuation functions of mm-sized implants is challenging. To enable this operation, a hybrid magnetic-ultrasonic interrogation approach (called MagSonic) is realized through a single magnetoelectric (ME) transducer at the implant that can generate and receive both magnetic field and ultrasound. The fabricated mm-sized bar-shaped ME transducer (5.2×2×1.6 mm3) operates at acoustic wave resonance, functioning at sub-MHz frequencies. For the first time, we demonstrate wireless power reception through one modality (magnetic field or ultrasound) and simultaneous uplink data transmission using the other. At 40 mm depth, the MagSonic link could achieve 100 kbps uplink data rate (bit error rate ≤ 10-5) using 190 pJ/bit transmitted energy and 8 mW delivered power in tissue. The robustness of the MagSonic interrogation link against power carrier interference and misalignments is also demonstrated.

Clustering Approach for Reliable Wireless Communication

Multiple access techniques for 5G systems are based on principles related to security, efficiency, and performance increase. Multi-user shared access (MUSA) is included in code-domain non-orthogonal multiple access (CD-NOMA) techniques with multiple benefits including multiple-access interference minimization and system capacity increase. The additional benefits of MUSA include its grant-free nature, which makes it applicable to scenarios involving random access channels, minimal latency communications, etc. Depending on the high-frequency diversity of the fading-affected channels, this technique is also used to lessen their effects. This paper illustrates the concept of code correlation used in MUSA by proposing a method to allocate MUSA codes to active users in an uplink data transmission system based on clusters. This clustering approach (grouping of 3GPP TR 38.812 spreading codewords) enables performance improvement in different system configurations by implementing various scenarios and providing reliable results. It is demonstrated that four-length spreading code selection from 3GPP family codes is a satisfactory solution, and this cluster grouping can be designed in a less overloaded multi-user system. The challenges, advantages, and limitations of the proposed method are outlined throughout the study.

Assessment of the Contribution of Radiations of User Equipment to the Anthropogenic Electromagnetic Background Created by Mobile (Cellular) Communications

The declared increase in spatial density of user (terminal, peripheral, etc.) radiating equipment (UE) of mobile communications up to 0.1 UE/m2 in 4G (LTE) networks, up to 1.0 UE/m2 in 5G (NR) networks and up to 10 UE/m2 in promising 6G networks may cause an unacceptable increase in electromagnetic background and in corresponding forced risks to public health. The paper proposes a method for assessing the contribution of UE radiations to the level of anthropogenic electromagnetic background created by mobile communications. This method is based on the analysis of the electromagnetic loading on the area created by stationary and mobile radiation sources of mobile communications and determined by the area density of mobile traffic, its asymmetry in downlink and uplink data transmission, the degree of UE concentration in the observation point vicinity, the radio channels spectral efficiency, the size of base stations service areas and other characteristics. The calculated data are given, indicating that in places of UE concentration, the component of electromagnetic background formed by UE radiations may be predominant, many times exceeding the contribution of base station radiations, and determining the actual level of forced risks to public health, which requires consideration in the system of their hygienic rationing.

Data Collection for Target Localization in Ocean Monitoring Radar-Communication Networks

With the ongoing changes in global climate, ocean data play a crucial role in understanding the complex variations in the earth system. These variations pose significant challenges to human efforts in addressing the changes. As a data hub for the satellite geodetic technique, unmanned aerial vehicles (UAVs) instill new vitality into ocean data collection due to their flexibility and mobility. At the same time, the dual-functional radar-communication (DFRC) system is considered a promising technology to empower ubiquitous communication and high-accuracy localization. In this paper, we explore a new fusion of UAV and DFRC to assist data acquisition in the ocean surveillance scenario. The floating buoys transmit uplink data transmission to the UAV with non-orthogonal multiple access (NOMA) and attempt to localize the target cooperatively. With the mobility of the UAV and power control at the buoys, the system throughput and the target localization performance can be improved simultaneously. To balance the communication and sensing performance, a two-objective optimization problem is formulated by jointly optimizing the UAV’s location and buoy’s transmit power to maximize the system throughput and minimize the attainable localization mean-square error. We propose a joint communication and radar-sensing many-objective optimization (CRMOP) algorithm to meliorate the communication and radar-sensing performance simultaneously. Simulation results demonstrate that compared with the baseline, the proposed algorithm achieves superior performance in balancing the system throughput and target localization.

Optimization of Delay Using Killer Whale Algorithm (KWA) on NB-IoT

Abstract: NB-IoT is designed to connect IoT devices with low-power, wide-area coverage and efficient costs. Ensuring optimal data transmission delay is a challenge in NB-IoT implementation. Inadequate coverage can hinder IoT adoption. Optimization balances energy saving and delay trade-off. The Killer Whale Algorithm (KWA) optimizes delay by adjusting repetition variables. KWA addresses dimensions, variable limits. Applying KWA in NB-IoT optimizes transmission, enhancing QoS. Optimizing delay involves reducing latency in uplink data transmission using repetition variables. This study applies KWA to optimize NB-IoT delay. Analysis in Table 4 shows non-linear repetition-distance correlation. Interestingly, delay outcomes exhibit a contrasting relationship. Still, delay remains advantageous, remaining under 1 second even at 10 km, specifically 9.2674 ms (0.0092674 seconds). This thesis aims to optimize delay in NB-IoT network transmission using the Killer Whale Algorithm (KWA), crucial for modern communication networks and IoT applications. Leveraging KWA, the research identifies solutions to reduce transmission delay, enhancing efficiency and meeting IoT communication demands for speed and timeliness

A Resource Optimization for Two-Step Random Access in 5G New Radio

This paper presents a resource optimization method for the 2-step random access (RA) procedure in the 5th generation new radio (5G NR). In 2-step RA, the MsgA includes a sequence as a preamble on the physical random access channel (PRACH) and uplink payload on the physical uplink shared channel (PUSCH). The mapping between PRACH slots and PUSCH slots is specified by system configuration parameters. The network reserves the corresponding PUSCH resource fixedly, regardless of the random access occasion (RO) occupancy rate and the uplink data traffic request. It deprives the uplink scheduling resource efficiency in cases of some RO unoccupied. To achieve higher utilization efficiency, this paper studies the reusing of the PUSCH slots and occasions fixed to RACH in MsgA. By detecting the preambles in RO before the corresponding PUSCH slots, the network acquires the occupancy of the RO. Then the PUSCH occasion (PO) in reserved PUSCH can be released, which conforms to the RO detection result. The release of the previously reserved PUSCH occasions can be rescheduled and reused by the user equipment (UE) for uplink data transmission. Hence more wireless resources can be scheduled in contrast to the existing allocation of uplink share channel in 2-step type random access. It suggests that using the method improves the efficiency of resource utilization.

User group-based pilot allocation and data power optimization in cell-free massive MIMO for coexistence of UAVs and ground users

To address the pilot contamination issue in cell-free massive multi-input multi-output (MIMO) systems supporting simultaneous communication between unmanned aerial vehicles (UAVs) and ground users (GUEs), a user group-based pilot allocation algorithm is proposed. Furthermore, the system performance is enhanced by optimizing the data power. Initially, the distances between users and pilots are calculated based on the principles of maximizing spectral efficiency and utilizing the large-scale fading matrix. Ground users and UAVs are randomly divided into two groups: primary users and shared users. The contamination intensity between users is measured based on the distance between users and pilots, and shared user pairs that are allocated the same orthogonal pilots are generated. Next, a user-centric approach is employed to select a subset of access points (APs) to serve the users. The scalable fractional power optimization strategy is combined as a power optimization scheme for uplink data transmission, providing a controllable trade-off between user fairness and average spectral efficiency. Simulation results demonstrate that the user group-based pilot allocation algorithm significantly improves the system’s overall spectral efficiency, and the fractional power optimization algorithm enhances the average spectral efficiency of the system.

Novel Access Methods in Cellular Networks Based on the Analog Bloom Filter

In cellular networks, the User Equipment (UE) often needs to send control information to the base station. For example, to initiate a connection, a UE transmits a Zadoff-Chu (ZC) sequence on a channel called PRACH, where the sequence is randomly selected from up to 64 sequences. Also, a connected UE may send the Scheduling Request (SR) bit on a channel called PUCCH to start an uplink data transmission. In this paper, novel access methods, namely, ABF PRACH and ABF SR, are proposed based on the Analog Bloom Filter (ABF). With ABF PRACH, a UE transmits a pattern, which consists of multiple ZC sequences. The performance is improved, because the number of patterns, currently 2304, is much larger than the number of sequences, resulting in a much lower collision probability. Similarly, with ABF SR, each connected UE is assigned a unique pattern. The system capacity is improved, because the resource is shared by many UEs, instead of dedicated to individual UEs. Both ABF PRACH and ABF SR have been demonstrated in real-world experiments. Simulations show that ABF PRACH significantly outperforms the existing PRACH. Also, ABR SR achieves a 2.52-fold increase of capacity, while maintaining very low error ratios.

SNR Gain Evaluation in Narrowband IoT Uplink Data Transmission with Repetition Increment: A Simulation Approach

Deploying Internet of Things (IoT) on a large scale necessitates widespread network infrastructures supporting Machine Type Communication. Integrating IoT into cellular networks like LTE, known as Narrowband-IoT (NB-IoT), can fulfill this infrastructure need. Standard 3GPP Release 13 introduces NB-IoT's Repetition features, expanding radio transmission coverage while maintaining LTE performance. Focusing on uplink data traffic, this study examines NB-IoT's repetition mechanism, grid resource distribution, and NPUSCH performance through simulations. Results show that at SNR greater than -5 dB, maximum repetitions of 128 yield the highest BLER, while minimum repetitions of 2 result in the lowest. Quadrupling repetitions increases SNR by 5 dB, emphasizing repetition's role in error mitigation and uplink reliability, especially in challenging SNR conditions. For optimal throughput in SNR above -5 dB, maximum repetitions of 128 for NPUSCH format 1 are recommended. These findings underscore the importance of repetition in enhancing Narrowband IoT performance, offering insights for system optimization, where increasing the number of repetitions generally leads to higher SNR gain. The attained BLER and throughput values from Narrowband IoT simulations highlight the robustness of data transmission across varying channel conditions, affirming NB-IoT applicability to a wide range of IoT applications.

Space-Terrestrial Cooperation Over Spatially Correlated Channels Relying on Imperfect Channel Estimates: Uplink Performance Analysis and Optimization

A whole suite of innovative technologies and architectures have emerged in response to the rapid growth of wireless traffic. This paper studies an integrated network design that boosts system capacity through cooperation between wireless access points (APs) and a satellite for enhancing the network’s spectral efficiency. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">As for our analytical contributions</i> , upon coherently combing the signals received by the central processing unit (CPU) from the users through the space and terrestrial links, we first mathematically derive an achievable throughput expression for the uplink (UL) data transmission over spatially correlated Rician channels. Our generic achievable throughput expression is applicable for arbitrary received signal detection techniques employed at the APs and the satellite under realistic imperfect channel estimates. A closed-form expression is then obtained for the ergodic UL data throughput, when maximum ratio combining is utilized for detecting the desired signals. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">As for our resource allocation contributions</i> , we formulate the max-min fairness and total transmit power optimization problems relying on the channel statistics for performing power allocation. The solution of each optimization problem is derived in form of a low-complexity iterative design, in which each data power variable is updated relying on a closed-form expression. Our integrated hybrid network concept allows users to be served that may not otherwise be accommodated due to the excessive data demands. The algorithms proposed allow us to address the congestion issues appearing when at least one user is served at a rate below his/her target. The mathematical analysis is also illustrated with the aid of our numerical results that show the added benefits of considering the space links in terms of improving the ergodic data throughput. Furthermore, the proposed algorithms smoothly circumvent any potential congestion, especially in face of high rate requirements and weak channel conditions.

Optimized Resource Allocation for IoT-D2D Communication Using WSN

The need for a strong system to access radio resources demands a change in operating frequency in wireless networks as a part of Radio Resource Management (RRM). In the fifth-generation (5G) wireless networks, the capacity of the system is expected to be enhanced by Device-to-Device (D2D) communication. The cooperation and Resources Allocation (RA) in the development of Internet of Things (IoT) enabled 5G wireless networks are investigated in this paper. Developing radio RA methods for D2D communication while not affecting any Mobile Users’ (MU) communication is the main challenge of this research. Distinct performance goals such as practising equality in the rates of user data, increasing Network Throughput (NT), and reducing End-to-End Delay (EED) are achieved by RA. The study undertaken on optimising performance for various wireless networks is focused on in this research work. Proposing a polynomial-time Proportional Fair Resource Allocation Method (PFRAM), which considers the MU’s rate requirements, is the prime objective of this paper. Any Resource Allocation Method (RAM) can be used by the proposed method for MU, and the time and differing location channel conditions are the factors to be adapted with. Allotting more than one resource block is allowed by our PFRAM to a D2D pair. The automatic maintenance of battery-less IoT wireless devices’ energy level is done potentially using an Extensible Energy Management System (EEMS). Finally, the device’s Node Transmission Power (NTP) can be managed using an Energy-Saving Algorithm (ESA) designed in this work for Node Uplink Data Transmission (NUDT). The trade-off between the Packet Loss Rate (PLR) and NTP is balanced by the algorithm. The cost of NUDT’s average Energy Consumption (EC) is reduced by locating the optical NTP. In order to free much bandwidth for wireless information, NUDT conserves the harvested energy for minimising Radio Frequency (RF) Energy Transmission (ET). MATLAB simulations are used to assess the proposed EEMS. The IoT device’s NTP is managed using ESA designed for NUDT. The significant minimisation of channel hopping EED and the selection of the premium quality communication channel by the proposed framework are indications of the simulation results. 67.19% is the bandwidth to transmit DPs with the Bandwidth Allocation Algorithm (BAA), which is greater than the cases in its absence.

Energy-Efficient Pairing and Power Allocation for NOMA UAV Network Under QoS Constraints

Due to the increasing number of unmanned aerial vehicles (UAVs) with generally limited battery power, energy-efficient data transmission schemes with massive connectivity capabilities are required for future wireless networks. Nonorthogonal multiple access (NOMA) is a promising technique that provides such massive connectivity by allowing superposed data transmission of multiple users over the same resource block. This article presents a user pairing and power allocation technique for energy efficiency and Quality-of-Service (QoS)-based NOMA transmission for cellular-connected mobile UAVs and ground users (GUs) in a NOMA UAV network. The aim is to minimize the energy consumption of both aerial users (AUs) and GUs during uplink data transmission and guarantee their required transmission rates through efficient pairing and power optimization. Therefore, a joint pairing and power optimization problem is formulated as a nonconvex optimization problem under QoS constraints. Furthermore, a suboptimal solution is proposed to show that the proposed energy-efficient pairing technique effectively fulfills the QoS requirements of both GUs and AUs in a network with low complexity. The simulation results of the proposed technique show the performance gain in terms of energy efficiency, outage probability, and pairing complexity compared to the conventional channel condition and power-consumption-based NOMA pairing and power allocation techniques.

Multi-objective uplink data transmission optimization for edge computing in UAV-assistant mobile wireless sensor networks

Unmanned aerial vehicles (UAVs) have attracted growing attention in enhancing the performance of mobile wireless sensor networks (MWSNs) since they can act as aerial servers (ASs) and have the autonomous nature to collect data for edge computing. In this paper, we consider to construct a virtual antenna array (VAA) consists of mobile sensor nodes (MSNs) and adopt collaborative beamforming (CB) to achieve long-distance and efficient uplink data transmissions with the ASs. First, we formulate a low interference and high-performance uplink transmission multi-objective optimization problem (UTMOP) of the CB-based UAV-assisted MWSN to simultaneously improve the total transmission rates, suppress the total maximum sidelobe levels (SLLs) and reduce the total propulsion energy consumptions of MSNs by jointly optimizing the positions and excitation current weights of MSN-enabled VAA and the order of communicating with different ASs. Then, we propose an improved non-dominated sorting genetic algorithm-III (INSGA-III) with chaos initialization, average grade mechanism and hybrid-solution generate strategy to solve the problem. Simulation results verify that the proposed algorithm can effectively solve the formulated UTMOP, and it has better performance than some other benchmark methods and peer algorithms.

Coverage Analysis for Cell-Free Massive MIMO High-Speed Railway Communication Systems

In traditional cellular framework, the frequent handoff is one of the main factors that affect the performance of high-speed railway (HSR) communication systems. The cell-free massive multiple input multiple output (MIMO) is proposed as a promising technique for the antenna point (AP) deployment of HSR communication systems. This paper investigates the coverage performance of the cell-free massive MIMO HSR communication systems with multiple APs and multiple users. Initially, the system and channel models are proposed. Based on the established models, the uplink training, uplink data transmission and downlink data transmission are analyzed, respectively. By using the deterministic equivalent analysis, we derive a deterministic signal-to-interference-plus-noise ratio expression for downlink transmission. Then, a tight upper bound on the coverage probability is derived. In the high power regime, the asymptotic coverage performance is also analyzed. Numerical results verify the correctness of the derived theoretical expressions. Moreover, the effects of the downlink transmit power, AP’s antenna number, AP’s density, user number, and channel fading parameter on coverage performance are also discussed.

Joint Antenna and Device Scheduling in Full-Duplex MIMO Wireless-Powered Communication Networks

In this article, we study a joint antenna and Internet of Things device (ID) scheduling problem in the full-duplex (FD) multiple-input–multiple-output (MIMO) wireless-powered communication network (WPCN) over time-varying fading channels. We first formulate an optimization problem to maximize the average sum rate of IDs while satisfying their minimum average data rate requirements by jointly scheduling ID selection for uplink data transmission, antenna switching, and beamforming. To deal with the problem, we propose a scheduling algorithm based on Lagrangian duality and the stochastic optimization theory. The proposed scheduling algorithm necessitates solving per-time-slot problems, each of which aims at maximizing the weighted sum of the selected ID’s uplink data rate and the nonselected IDs’ harvested power from the downlink by jointly optimizing ID selection, antenna switching between uplink and downlink, and beamforming at that time slot. To solve the per-time-slot problem, we develop a joint ID selection, antenna switching, and beamforming (Joint-IAB) algorithm based on the block coordinate descent (BCD) and successive convex approximation (SCA) methods. Through simulation, we demonstrate that our scheduling algorithm with the proposed Joint-IAB algorithm provides better performance than the other scheduling algorithms while well satisfying the given minimum average data rate requirements of IDs.