User Equipment Research Articles

Message Passing Detectors for UAV-Based Uplink Grant-Free NOMA Systems

Utilizing unmanned aerial vehicles (UAVs) as mobile access points or base stations has emerged as a promising solution to address the excessive traffic demands in wireless networks. This paper investigates improving the detector performance at the unmanned aerial vehicle base stations (UAV-BSs) in an uplink grant-free non-orthogonal multiple access (GF-NOMA) system by considering the activity state (AS) temporal correlation of the different user equipments (UEs) in the time domain. The Bernoulli Gaussian-Markov chain (BG-MC) probability model is used for exploiting both the sparsity and slow change characteristic of the AS of the UE. The GAMP Bernoulli Gaussian-Markov chain (GAMP-BG-MC) algorithm is proposed to improve the detector performance, which can utilize the bidirectional message passing between the neighboring time slots to fully exploit the temporally correlated AS of the UE. Furthermore, the parameters of the BG-MC model can be updated adaptively during the estimation procedure with unknown system statistics. Simulation results show that the proposed algorithm can improve the detection accuracy compared to existing methods while keeping the same order complexity.

Dynamic SNR, Spectral Efficiency, and Rate Characterization in 5G/6G mmWave/sub-THz Systems with Macro- and Micro-Mobilities

The performance of 5G/6G cellular systems operating in millimeter wave (mmWave, 30–100 GHz) and sub-terahertz (sub-THz, 100–300 GHz) bands is conventionally assessed by utilizing the static distributions of user locations. The rationale is that the use of the beam tracking procedure allows for keeping the beams of a base station (BS) and user equipment (UE) aligned at all times. However, by introducing 3GPP Reduced Capability (RedCap) UEs utilizing the Radio Resource Management (RRM) Relaxation procedure, this may no longer be the case, as UEs are allowed to skip synchronization signal blocks (SSB) to improve energy efficiency. Thus, to characterize the performance of such UEs, methods explicitly accounting for UE mobility are needed. In this paper, we will utilize the tools of the stochastic geometry and random walk theory to derive signal-to-noise ratio (SNR), spectral efficiency, and rate as an explicit function of time by accounting for mmWave/sub-THZ specifics, including realistic directional antenna radiation patterns and micro- and macro-mobilities causing dynamic antenna misalignment. Different from other studies in the field that consider time-averaged performance measures, these metrics are obtained as an explicit function of time. Our numerical results illustrate that the macro-mobility specifies the overall trend of the time-dependent spectral efficiency, while local dynamics at 1–3 s scales are mainly governed by micro-mobility. The difference between spectral efficiency corresponding to perfectly synchronized UE and BS antennas and time-dependent spectral efficiency in a completely desynchronized system is rather negligible for realistic cell coverages and stays within approximately 5–10% for a wide range of system parameters. These conclusions are not affected by the utilized antenna array at the BS side. However, accounting for realistic radiation patterns is critical for a time-dependent performance analysis of 5G/6G mmWave/sub-THz systems.

Quantum-compliant users scheduling optimization in joint transmission mobile access networks

Joint Transmission (JT) is the dynamic coordination of transmission and/or reception at multiple geographically separated sites to improve end-user service quality. When user equipment receives signals from multiple sites, downstream performance improves. An optimization problem arises in selecting the best user subset for JT within a multiple-input–multiple-output (MIMO) system. Unfortunately, a pure brute-force approach is not feasible due to exponential time growth with user combinations, unsuitable for real-time selection in mobile networks with users continuously changing in time. This article proposes quantum-compliant heuristics using quadratic unconstrained binary optimization (QUBO) for JT user scheduling. QUBO handles initial user selection, followed by brute-force exploration for the solution. Numerical results indicate that quantum-compliant methods decrease solution time without substantial accuracy loss compared to brute-force methods.

Performance Impact of Nested Congestion Control on Transport-Layer Multipath Tunneling

Multipath wireless access aims to seamlessly aggregate multiple access networks to increase data rates and decrease latency. It is currently being standardized through the ATSSS architectural framework as part of the fifth-generation (5G) cellular networks. However, facilitating efficient multi-access communication in next-generation wireless networks poses several challenges due to the complex interplay between congestion control (CC) and packet scheduling. Given that enhanced ATSSS steering functions for traffic splitting advocate the utilization of multi-access tunnels using congestion-controlled multipath network protocols between user equipment and a proxy, addressing the issue of nested CC becomes imperative. In this paper, we evaluate the impact of such nested congestion control loops on throughput over multi-access tunnels using the recently introduced Multipath DCCP (MP-DCCP) tunneling framework. We evaluate different combinations of endpoint and tunnel CC algorithms, including BBR, BBRv2, CUBIC, and NewReno. Using the Cheapest Path First scheduler, we quantify and analyze the impact of the following on the performance of tunnel-based multipath: (1) the location of the multi-access proxy relative to the user; (2) the bottleneck buffer size, and (3) the choice of the congestion control algorithms. Furthermore, our findings demonstrate the superior performance of BBRv2 as a tunnel CC algorithm.

Edge computing collaborative offloading strategy for space‐air‐ground integrated networks

SummaryDue to geographical factors, it is impossible to build large‐scale communication network infrastructure in remote areas, which leads to poor network communication quality in these areas and a series of delay‐sensitive tasks cannot be timely processed and responded. Aiming at the problem of limited coverage in remote areas, the space‐air‐ground integrated networks (SAGIN) combined with mobile edge computing (MEC) can provide low latency and high reliability transmission for offloading delay‐sensitive tasks for users in remote areas. Considering the strong limitation of satellite resources in the space‐ground integrated network and insufficient energy of local user equipment, firstly, a satellite‐UAV cluster‐ground three‐layer edge computing network architecture is proposed in this paper. Under the condition that the delay requirements of various ground tasks are met, the task offloading problem is transformed into a Stackelberg game between ground user equipment and edge servers. In addition, it is proved that the existence of Nash equilibrium in non‐cooperative game between ground user equipment by using potential game. Finally, a Nash equilibrium iterative offloading algorithm based on Stackelberg game (NEIO‐SG) is proposed to find the optimal offloading strategy for tasks to minimize the system offloading cost and the optimal forwarding percentage strategy for offloading tasks to maximize the utility function of the edge server. Simulation results show that compared to other baseline algorithms, NEIO‐SG reduces the total system latency during task offloading by about 13 and the energy consumption of the edge server by about 35.

A Reinforcement Learning-Based Reverse Auction Enforcing Smart Pricing Policies towards B5G Offloading Strategies

In this paper, we present our work on developing a Smart Pricing Policies module specifically designed for individual users and Mobile Network Operators (MNOs). Our framework will operate in a multi-MNO blockchain radio access network (B-RAN) and is tasked with determining prices for resource sharing among users and MNOs. Our sophisticated adaptive pricing system can adjust to situations where User Equipment (UE) shifts out of the coverage area of their MNO by immediately sealing a contract with a different MNO to cover the users’ needs. This way, we aim to provide financial incentives to MNOs while ensuring continuous network optimization for all parties involved. Our system accomplishes that by utilizing deep reinforcement learning (DLR) to implement a reverse auction model. In our reinforcement learning scenario, the MNOs, acting as agents, enter a competition and try to bid the most appealing price based on the user’s request, and based on the reward system, agents that do not win in the current round will adjust their strategies in an attempt to secure a win in subsequent rounds. The findings indicated that combining DRL with reverse auction theory offers a more appropriate method for addressing the pricing and bid challenges, and additionally, administrators can utilize this strategy to gain a notable edge by dynamically selecting and adjusting their methods according to the individual network conditions and requirements.

Downlink Transmissions of UAV-RIS-Assisted Cell-Free Massive MIMO Systems: Location and Trajectory Optimization.

In this paper, we investigate a cell-free massive multiple-input multiple-output (CF-mMIMO) system with a reconfigurable intelligent surface (RIS) carried by an unmanned aerial vehicle (UAV), called the UAV-RIS. Compared with the RIS located on the ground, the UAV-RIS has a wider coverage that can reflect all signals from access points (APs) and user equipment (UE). By correlating the UAV location with the Rician K-factor, we derive a closed-form approximation of the UE achievable downlink rate. Based on this, we obtain the optimal UAV location and RIS phase shift that can maximize the UE sum rate through an alternating optimization method. Simulation results have verified the accuracy of the derived approximation and shown that the UE sum rate can be significantly improved with the obtained optimal UAV location and RIS phase shift. Moreover, we find that with a uniform UE distribution, the UAV-RIS should fly to the center of the system, while with an uneven UE distribution, the UAV-RIS should fly above the area where UEs are gathered. In addition, we also design the best trajectory for the UAV-RIS to fly from its initial location to the optimal destination while maintaining the maximum UE sum rate per time slot during the flight.

Optimizing Disaster Response through Efficient Path Planning of Mobile Aerial Base Station with Genetic Algorithm

The use of unmanned aerial vehicles (UAVs), or drones, as mobile aerial base stations (MABSs) in Disaster Response Networks (DRNs) has gained significant interest in addressing coverage gaps of user equipment (UE) and establishing ubiquitous connectivity. In the event of natural disasters, the traditional base station is often destroyed, leading to significant challenges for UEs in establishing communication with emergency services. This study explores the deployment of MABS to provide network service to terrestrial users in a geographical area after a disaster. The UEs are organized into clusters at safe locations or evacuation shelters as part of the communication infrastructure. The main goal is to provide regular wireless communication for geographically dispersed users using Long-Term Evolution (LTE) technology. The MABS traveling at an average speed of 50 km/h visits different cluster centroids determined by the Affinity Propagation Clustering (APC) algorithm. A combination of graph theory and a Genetic Algorithm (GA) was used through mutators with a fitness function to obtain the most efficient flyable paths through an evolution pool of 100 generations. The efficiency of the proposed algorithm was compared with the benchmark fitness function and analyzed using the number of serviced UE performance indicators. System-level simulations were used to evaluate the performance of the proposed new fitness function in terms of the UEs served by the MABS after the MABS deployment, fitness score, service ratio, and path smoothness ratio. The results show that the proposed fitness function improved the overall service of UEs after MABS deployment and the fitness score, service ratio, and path smoothness ratio under a given number of MABS.

Enhancing UAV‐HetNet security through functional encryption framework

SummaryIn the current landscape, the rapid expansion of the internet has brought about a corresponding surge in the number of data consumers. As user volume and diversity have escalated, the shift from conventional, uniform networks to Heterogeneous Networks (HetNets) has emerged. HetNets are designed with a primary objective: enhancing Quality of Service (QoS) standards for users. In the context of HetNets facilitated by Unmanned Aerial Vehicles (UAVs), a substantial influx of users and devices is observed. Within this multifaceted environment, the potential for malicious intruder nodes to efficiently execute and propagate harmful actions across the network is a distinct concern. Consequently, the entirety of network communication becomes susceptible to a multitude of security threats. To address these vulnerabilities and safeguard communication, the Functional Encryption (FE) technique is employed. FE empowers the protection of data against intrusion attacks. This paper presents a comprehensive methodology for implementing FE within UAV‐integrated HetNets, executed in two sequential phases. The initial phase secures communication between User Equipment (UE) and Micro Base Station (MBS), followed by the second phase, which focuses on securing communication among MBS and UAV. The viability of the proposed approach is substantiated through validation using the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool. The validation process involves the development of High‐Level Protocol Specification Language (HLPSL) codes. The successful security validation outcome underscores the capacity of the proposed methodology to provide the intended security measures and robustness to the network environment.

Energy efficiency optimization of NB-IoT using integrated Proxy & ERAI technique

The 3rd Generation Partnership Project (3GPP) standardized the NarrowBand Internet of Things (NB-IoT) technology in Release-13, aiming to support applications requiring minimal device power consumption, efficient small data transfer, and tolerance for delays. This paper provides two major contributions: first, it characterizes the NB-IoT architecture and its enhancements in subsequent 3GPP releases; second, it introduces an energy-saving technique for NB-IoT user equipment based on semi-Markov processes. Integrating a Proxy state and an enhanced release assistant indication (ERAI), the proposed technique improves upon the traditional Discontinuous Reception (DRX) method in the NB-IoT standard. This novel approach reduces energy consumption during low data communication, distinguishing it from other power-saving methodologies. The effectiveness of this technique is assessed through the power-saving factor (PSF) under various conditions, such as different eDRX durations (T_eDRX), PSM durations (T_PSM), DRX cycle periods (T_ACTIVE+T_SLEEP), and DRX active periods (T_ACTIVE). Our findings confirm that the proposed Proxy & ERAI technique significantly boosts the power-saving capabilities of NB-IoT devices, especially for small data packet transmissions. Theoretical and simulation results indicate that the PSF can be enhanced by up to 99.4% with optimized eDRX durations (T_eDRX) and up to 99.9% with optimized PSM (T_PSM) durations. These results underscore the significant energy-saving potential of our proposed technique, making it highly effective for NB-IoT applications with low data transmission needs.

A Novel Radio Network Information Service (RNIS) to MEC Framework in B5G Networks

Multi-Access Edge Computing (MEC) reduces latency, provides high-bandwidth applications with real-time performance and reliability, supporting new applications and services for the present and future Beyond the Fifth Generation (B5G). Radio Network Information Service (RNIS) plays a crucial role in obtaining information from the Radio Access Network (RAN). With the advent of 5G, RNIS requires improvements to handle information from the new generations of RAN. In this scenario, improving the RNIS is essential to boost new applications according to the strict requirements imposed. Hence, this work proposes a new RNIS as a service to the MEC framework in B5G networks to improve MEC applications. The service is validated and evaluated, and demonstrates the ability to adequately serve a large number of MEC apps (two, four, six and eight) and from 100 to 2000 types of User Equipment (UE).

Distributed neighbor discovery with mobility adaptive probing for beyond 5G D2D communications

Representing a paradigm shift in cellular communication, Device-to-Device (D2D) Communication is an emerging technology that enables direct communication between two User Equipments(UEs) without going through the Base Station. It enables decentralization of cellular networks, enhances network capacity, increases spectral efficiency, and offloads network traffic. In order to proceed with direct D2D communication, the devices must have knowledge of the proximal devices and should work in all coverage scenarios such as in-coverage, partial-coverage and out-of-coverage. Most of the existing works on Device Discovery are based on 3GPP Model A and Model B. However, these schemes suffer from overhead of discovery message exchanges with the increase in the number of devices in a mobile environment. To address these issues, we propose a distributed and adaptive device discovery mechanism that can discover the proximal UEs in all possible scenarios. To achieve this, we propose a Hybrid Discovery Model ‘H’, which uses the concept of ‘Who are there?’. In Model ‘H’, the discoverer sends a single request message to discover all proximal devices. The responses of the proximal UEs are staggered based on the estimated number of neighbors to avoid collisions. In addition to that, we adapt the discovery mechanism to the relative mobility of the neighborhood using our proposed Adaptive Probing Algorithm named ‘ReMAP’. The relative mobility is determined based on the current and previous neighbor lists of each UE. The key advantage of our proposed mechanism is that we do not require the knowledge of position and mobility pattern of the UEs. We implement and evaluate our proposed work in D2D Module of the NS3 simulator. From the simulation results, we observe that our proposed model shows better performance in terms of the number of UEs discovered and reduced number of discovery message exchanges compared to the state-of-the-art. We also compare the performance of ReMAP with a constant probing mechanism and show the improvement in terms of adaptability to the mobility of the neighborhood.

Localization Performance Analysis and Algorithm Design of Reconfigurable Intelligent Surface-Assisted D2D Systems.

The research on high-precision and all-scenario localization using the millimeter-wave (mmWave) band is of great urgency. Due to the characteristics of mmWave, blockages make the localization task more complex. This paper proposes a cooperative localization system among user equipment (UEs) assisted by reconfigurable intelligent surfaces (RISs), which considers device-to-device (D2D) communication. RISs are used as anchor points, and position estimation is achieved through signal exchanges between UEs. Firstly, we establish a localization model based on this system and derive the UEs' positioning error bound (PEB) as a performance metric. Then, a UE-RIS joint beamforming design is proposed to optimize channel state information (CSI) with the objective of achieving the minimum PEB. Finally, simulation analysis demonstrates the advantages of the proposed scheme over RIS-assisted base station positioning, achieving centimeter-level accuracy with a 10 dBm lower transmission power.

Joint optimization of deployment, user association, channel, and resource allocation for fairness‐aware multi‐UAV network

AbstractThis paper studies the problem of joint deployment, user association, channel, and resource allocation in unmanned aerial vehicle‐enabled access network. Since different user equipments performing different tasks and have different data rate requirements, the priority‐based traffic fairness problem is investigated. This problem, however, is a mixed integer nonlinear programming problem with NP‐hard complexity, making it challenging to be solved. To address this issue, a self‐organized and distributed framework “sense‐as‐you‐fly” based on the decomposition process, which divides the original problem into several subproblems, is proposed. Assuming without central controller, we derive the closed‐form resource allocation scheme and propose distributed many‐to‐one matching to optimize user association subproblem. Considering the coupled characteristics, the multi‐unmanned aerial vehicle deployment and channel allocation subproblems are modelled as a local altruistic game. The existence of Nash equilibrium is proved with the aid of exact potential game and efficient best response learning‐based algorithm is proposed. The original problem is finally addressed by solving the sub‐problems alternately and iteratively. Simulation results verify its effectiveness. By jointly optimizing multidimensional variables, the proposed algorithm unlocks network performance gains, especially in resource‐limited regimes.

Implementation of 5G New Radio Secondary Synchronization on FPGA

: 5th Generation New Radio (NR) cellular technology operates at low latency, and higher bandwidths, and makes use of a large number of antennas. NR communication is compatible with a wide range of machine- and human-centered applications. The cell search procedure plays a crucial role in establishing a synchronized connection with the base station (gNodeB) when the user equipment (UE) is enabled, or in the on state. The procedure for cell search finds the physical cell identity of the base station with primary synchronization and secondary synchronization for 3rd Generation Partnership Project (3GPP) 5G NR standards. This paper focuses on identifying the secondary synchronization of Physical Cell Identity (PCI) in the 3rd Generation Partnership Project (3GPP) 5G NR system. Field Programmable Gate Arrays are reprogrammable, flexible, reconfigurable computing circuits and simple to design at very peak frequencies. This suggested architecture employs Secondary Synchronization Signal detection and aims for low power consumption and high speed. The secondary synchronization block has been designed and executed on the Xilinx Zynq FPGA board. Index Terms: 5G New Radio, FPGA, Cell search, Physical Cell Identity, Primary and Secondary synchronization (SS).

Cross-link interference in the 5G cell search procedure: Observation and field experiments for mitigation

This paper presents field experiment results that illustrate the impact of cross-link interference on the cell search procedure in a 5G time division duplex system. We initially show how cross-link interference affects to the cell search procedure from an analytic aspect. An example of an experimental result provided in this paper shows how the user equipment wrongly estimates the symbol timing offset due to cross-link interference. Furthermore, we propose a modified cell search algorithm which is robust against the strong influence of cross-link interference. The proposed modification efficiently suppresses the effects of cross-link interference by applying selective energy normalization to the correlation process. The experimental results show that the modified algorithm can accurately estimate the symbol timing offset with a negligible increment in computation time.

Extremely Large-Scale MIMO: Fundamentals, Challenges, Solutions, and Future Directions

Extremely large-scale multiple-input-multipleoutput (XL-MIMO) is a promising technology to empower the next-generation communications. However, XL-MIMO, which is still in its early stage of research, has been designed with a variety of hardware and performance analysis schemes. To illustrate the differences and similarities among these schemes, we comprehensively review existing XL-MIMO hardware designs and characteristics in this article. Then, we thoroughly discuss the research status of XL-MIMO from "channel modeling", "performance analysis", and "signal processing". Several existing challenges are introduced and respective solutions are provided. We then propose two case studies for the hybrid propagation channel modeling and the effective degrees of freedom (EDoF) computations for practical scenarios. Using our proposed solutions, we perform numerical results to investigate the EDoF performance for the scenarios with unparallel XL-MIMO surfaces and multiple user equipment, respectively. Finally, we discuss several future research directions.

Enhancing privacy and security in 5G networks with an anonymous handover protocol based on Blockchain and Zero Knowledge Proof

The latest advancement in cellular technology, 5G, has the ability to facilitate various applications such as Device-to-Device (D2D) communication, vehicular communications, and the Internet of Everything (IoE). However, the technology is faced with challenges related to user privacy, resource management, handover authentication, and security of the air interface and internet channels. Moreover, the use of specialized base stations such as wireless Access Points (AP) and Road-Side Units (RSU) in vehicular communications may be owned by subscribers outside formal network operators. The conventional handover authentication schemes have a lengthy latency period that conflicts with the 5G quality of service. To tackle these challenges, this paper presents an anonymous scheme that utilizes Blockchain and Zero Knowledge Proof (ZKP) to ensure efficient privacy in the handover protocol of 5G Networks. The proposed scheme has been thoroughly evaluated for security and privacy and has shown to be resistant to identity catching, location area tracing, and replay attacks while achieving Forward/Backward secrecy. Furthermore, the performance evaluation of the proposed scheme indicates that it is efficient and speedy as it involves a limited number of messages, low computation, and does not necessitate the involvement of the Home Network (HN) in the authentication or handover protocols. A diligently crafted BAHO authentication protocol, following cryptographic principles, guarantees robust security against the Real-or-Random model. This results in a seamless and secure transition for User Equipment, upholding confidentiality, integrity, and authenticity during handovers between Access Points.

Optimized DM-RS Configuration for Improved 5G New Radio Network Capacity and Performance

Network load in mobile networks is continuously growing, putting pressure on mobile operators to deliver target network performance and user experience. This paper focuses on network capacity improvement through increased spectral efficiency, which can be achieved with overhead reduction by optimizing the demodulation reference signal (DM-RS) configuration, which is one of the channels/reference signals with the largest contribution to overhead. In a high-speed scenario, the Doppler effect noticeably influences the temporal nature of the channel such that a channel interpolation process is required within a slot. However, increasing the number of symbols used for DM-RS has a negative impact on capacity. The Doppler effect was analyzed for various 5G NR configurations of operating frequency and subcarrier spacing (SCS), and various use cases were considered using user equipment (UE) speed as the main parameter. For suitable use cases, the DM-RS configuration was optimized in networks with live traffic. The impact of the configuration change on 5G/NR spectral efficiency, user experience and link adaptation performance was assessed through a deep-dive analysis of active measurements, available performance Management (PM) counters and key performance indicators. An optimized DM-RS configuration is proposed, and it is demonstrated to achieve gains of 5–15%, depending on the metric used, use case analyzed, network load, traffic mix and other relevant network characteristics such as topology and clutter type.

Resource allocation-aware efficient interference management technique for ultra-dense Femto environment

Addressing the challenge of severe inter-layer and intra-layer interference in densely deployed Femtocells, we introduce an efficient resource allocation strategy centered on clustering. Initially, we employ the Fractional Frequency Reuse (FFR) technique to mitigate interference by segmenting cells into distinct zones. Subsequently, leveraging graph theory and convex optimization, we cluster the Femto Base Stations (FBS) to enhance efficiency. An algorithm prioritizing user fairness governs sub-channel allocation among FBSs. Additionally, a distributed power control algorithm dynamically adjusts FBS power levels, further enhancing system throughput. Comparative analysis reveals that our approach surpasses traditional non-clustered methods and three other comparison methods, exhibiting superior system throughput and Signal-to-Interference Ratio (SINR). Notably, our method significantly enhances user fairness, promoting greater satisfaction among Femto User Equipment (FUE) users.