ABSTRACT This paper studies the energy efficiency (EE) maximisation problem in an intelligent reflecting surface (IRS)-assisted rate-splitting multiple access (RSMA)-aided non-orthogonal multiple access (NOMA) downlink MIMO system. A multi-antenna base station (BS) serves multiple single-antenna users with the assistance of an IRS composed of passive reflecting elements. The resulting EE maximisation problem is highly non-convex due to its fractional objective function, coupled transmit precoding and IRS phase-shift design, and the unit-modulus constraints of the IRS elements. To tackle this challenge, an alternating optimisation (AO) framework is developed, where the BS precoders and IRS phase shifts are iteratively optimised. Specifically, the IRS phase optimisation subproblem is reformulated using semidefinite programming (SDP), which enables an efficient and tractable solution. Numerical results demonstrate that the proposed IRS-assisted RSMA-aided NOMA scheme significantly outperforms conventional OMA and NOMA schemes in terms of EE. Moreover, the proposed design achieves consistent EE gains under various system settings, including different transmit SNRs, numbers of IRS elements, circuit power consumption levels, and user densities. These results highlight the effectiveness of combining RSMA with IRS-assisted passive beamforming for energy-efficient future wireless networks.

Reconfigurable intelligent surfaces (RISs) are regarded as a transformative technique for future wireless networks. Currently, the majority of research efforts have focused on channel estimation scenarios in communication systems assisted by a single passive RIS. However, single-RIS-assisted systems suffer from limited coverage performance, with significant performance degradation observed in dense obstacle environments. To mitigate the adverse impacts imposed by environmental factors, a dual-RIS-assisted communication system exhibits superior adaptability to practical scenarios. This work focuses on investigating such a system. It is worth noting that fully passive RISs lack the capability to process signals independently. Furthermore, when employing pilot-aided algorithms to acquire channel state information (CSI), wireless systems often encounter challenges arising from large channel matrix dimensions, thereby leading to substantial pilot overhead. To address the aforementioned issues, this paper proposes a novel semi-blind channel estimation method for multiple-input multiple-output (MIMO) systems aided by double reconfigurable intelligent surfaces (D-RISs). Specifically, we construct two tensor models, namely the Parallel Factor (PARAFAC) model and the Parallel Tucker2 model, for the received signal in two separate stages. By means of tensor decomposition, the joint channel estimation and symbol detection problem is reformulated as a least squares problem and solved using a two-stage algorithm. In the first stage, the ALS algorithm is adopted to estimate the transmitted symbols and provide initialization for the second stage. Then, in the second stage, the TALS algorithm is employed to obtain the final estimation results of the three sub-channels. Simulation results verify the effectiveness of the proposed receiver.

Due to its ability of significantly improving data rate, intelligent reflecting surface (IRS) will be a potential crucial technique for the future generation wireless networks like 6G. In this paper, we will focus on the analysis of degree of freedom (DoF) in IRS-aided multi-user MIMO network. Firstly, the DoF upper bound of IRS-aided single-user MIMO network, i.e., the achievable maximum DoF of such a system, is derived, and the corresponding results are extended to the case of IRS-aided multiuser MIMO by using the matrix rank inequalities. In particular, in serious rank-deficient, also called low-rank, like line-of-sight (LoS) channel, the network DoF may doubles over no-IRS with the help of IRS. To verify the rate performance gain from augmented DoF, three closed-form beamforming methods, null-space projection plus maximize transmit power and maximize receive power (NSPMTP- MRP), Schmidt orthogonalization plus minimum mean square error (SO-MMSE) and two-layer leakage plus MMSE (TLL-MMSE) are proposed to achieve the maximum DoF. Simulation results shows that IRS does make a dramatic rate enhancement. For example, in a serious rank-deficient channel, also called low-rank, the sum-rate of the proposed TLL-MMSE aided by IRS is up to 2.54 times that of no IRS. This means that IRS may make a significant DoF improvement in such a channel.

The evolution of Multiple Input Multiple Output (MIMO) technology has significantly strengthened wireless communication performance; however, persistent challenges remain in interference mitigation, computational complexity, and spectrum efficiency. Filter Bank Multicarrier with Offset Quadrature Amplitude Modulation (FBMC-OQAM) has emerged as a viable alternative to Orthogonal Frequency Division Multiplexing (OFDM). This non-orthogonal waveform is attractive because of its low out-of-band radiation and high spectral efficiency. Nevertheless, inter-symbol and inter-carrier interference arising from the non-orthogonal nature of FBMC-OQAM signals and computational complexity degrade system performance. To address these challenges, this paper will present a new framework which is a synergistic integration of Code Division Multiple Access (CDMA), FBMC-OQAM, and space-time coding in MIMO. There are three major goals of the suggested strategy. To reduce mitigation of interference, the incorporation of CDMA spreading sequences is done to reduce inter-symbol and inter-carrier interference. Second, the space-time coding is designed with the aim of minimizing the number of computations made without affecting the performance. Third, the framework's objective is to improve bit error rate and other performance indicators, spectral efficiency, and robustness against multipath fading, ensuring reliable communication in MIMO environments. Performance of the proposed method is assessed and contrasted with additional benchmark models. Experimental results show that integrating CDMA with FBMA-OQAM and space-time coding significantly reduces interference, minimizes complexity, and enhances spectral utilization. The developments will ensure that future wireless networks can enjoy quality and dependable communication procedures. The proposed work establishes a solid foundation for the practical implementation of FBMC-OQAM in MIMO systems.

Terahertz (THz) communication is a promising enabler for next-generation wireless networks because it can support ultra-high data rates. However, severe path loss, molecular absorption, and high sensitivity to blockage significantly limit coverage and reliability. To address these challenges, this work proposes a RIS-assisted UAV positioning (RAVP) framework that integrates reconfigurable intelligent surfaces (RIS) with unmanned aerial vehicles (UAVs) and jointly optimizes RIS configuration and UAV deployment to enhance THz communications. RISs provide controllable reflections to improve propagation conditions, while UAVs enable flexible placement of RISs at advantageous locations. A reinforcement learning (RL)-based strategy that combines modified K-means clustering with gradient-based optimization coordinates user grouping, RIS phase-shift adaptation, and UAV positioning within a unified framework. Simulation results show consistent gains in link robustness, achievable data rate, and user connectivity across different network configurations compared with conventional THz systems without RISs or UAV-assisted optimization. These findings highlight the potential of coordinated RIS-UAV optimization for future 6G-enabled wireless networks, including smart-city and Internet of Things (IoT) applications.

Abstract This article presents an experimentation environment, empirical study and results of runtime energy consumption in 5th generation (5G) networks and discusses the potential solutions to enhance energy efficiency with radio access network (RAN) and application control. Further, the paper evaluates the actual power consumption of selected end user devices and applications in 5G RAN covering indoor small-cell installations and 5G outdoor macro sites. The evaluation setup utilized a state-of-the-art 5G radio access network (RAN) alongside efficient, accurate energy measurement equipment, measuring power consumption in live scenarios with selected video streams at varying bit rates to reflect advanced video streaming configurations. The outcome of the research shows that several enhancements, such as lower total energy consumption and longer end device battery life, are possible when advanced control is applied to the existing and future wireless mobile networks. The experimental results provide solid power consumption figures and lead to detailed reasons for the holistic view of energy enhancements covering the overall end-to-end data path including the end user devices, RAN as well as edge and cloud infrastructure and applications—addressing these aspects at a scale not covered in earlier studies. The increasing demand for high-quality digital services, particularly video streaming, presents significant challenges for reducing energy consumption in mobile networks; as these networks and services continue to grow, this study contributes to ongoing efforts to minimize their runtime energy consumption.

Optimizing IRS placement and element configuration in B5G: A novel cooperative hybrid communication system

This research introduces a graphene-based 2 × 2 MIMO microstrip antenna designed for 6G terahertz (THz) wireless communication. The antenna features a hybrid slot-elliptical patch configuration on a polyimide substrate, operating within the 2.4673–6.185 THz frequency spectrum. It showcases excellent electromagnetic performance, with three notable resonances at 3.0162, 4.942, and 5.8301 THz. The antenna boasts a bandwidth of 3.7177 THz, a peak gain of 11.19 dB, and a radiation efficiency of 88.06%. Furthermore, it achieves a minimal envelope correlation coefficient (ECC = 0.0002577) and an almost perfect diversity gain (DG = 9.9987 dB), making it well-suited for MIMO applications in high-capacity 6G systems. A key contribution of this study is the integration of a circuit-level RLC model that aligns closely with the results of full-wave simulations. This RLC model simplifies the design process by providing quick insights into the antenna’s performance while ensuring accuracy. It employs resistive (R), inductive (L), and capacitive (C) components to enhance understanding of the antenna’s resonant characteristics and optimization. Additionally, machine learning optimization techniques are used to refine the antenna design. Various machine learning algorithms, including Decision Tree, Random Forest, and Extra Trees Regressor, were evaluated for their effectiveness in predicting performance metrics and optimizing design parameters. These machine learning models significantly reduce computational load, improve predictive accuracy, and surpass traditional optimization methods. A regression-based machine learning approach accelerates the antenna design process, ensuring an ideal balance of bandwidth, gain, and isolation, which is crucial for the emerging 6G THz communication systems. Overall, this study combines RLC circuit modeling with machine learning optimization to create a stable and efficient method for developing high-performance THz MIMO antennas. This approach facilitates the advancement of sophisticated, low-interference, high-capacity wireless networks for 6G.

The growing demand for high-capacity next-generation networks drives the need for efficient and scalable multiple access schemes. This study proposes a unified integrated analytical framework for intelligent omni-surface (IOS)-enhanced multi-user MIMO cooperative hybrid NOMA (MU-MIMO-C-HNOMA) system employing maximal ratio transmission (MRT) at the gNodeB (gNB) to enhance reliability, throughput, and sum spectral efficiency (SSE). Closed-form mathematical models for outage probability and throughput expressions are derived, and an optimal power allocation strategy is formulated to maximize SSE under the dual impairments due to imperfect successive interference cancellation (iSIC) and hardware distortions (HWD). Crucially, the analysis establishes a compelling energy-efficient scaling paradigm, revealing that leveraging passive IOS elements is fundamentally superior to scaling active transmit antennas. Furthermore, the study validates real-world feasibility by demonstrating that practical discrete-phase and blind-IOS configurations offer robust performance that asymptotically approaches the ideal benchmarks. The proposed system with strong-weak strong-weak (SWSW) user pairing achieves SNR gains of approximately 0.36–3.14 dB for weaker user equipment (WUE) and 0.13–2.5 dB for stronger UE (SUE) at a target outage of $$10^{-3}$$ resulting in throughput gains of about 0.03 - 0.33 bps/Hz and SSE gains of 0.02 - 0.14 bps/Hz over other pairing schemes. Furthermore, optimal power allocation achieves 0.48 - 1.02 bps/Hz higher SSE than sub-optimal and minimum power allocation schemes, confirming robustness against dual-impairments due to iSIC and HWD effects. Compared to the conventional IOS-aided HNOMA system, the proposed framework demonstrates superior scalability, reliability, and performance, achieving up to 0.31 bps/Hz SSE improvement for two antennas, confirming its potential as a highly promising solution for next-generation wireless networks.

For future wireless networks beyond 5G (B5G), integrating and dynamically reconfiguring advanced technologies is crucial for achieving high spectral efficiency and ensuring massive user connectivity. This work proposes a practical and improved millimeter-wave non-orthogonal multiple access (NOMA) framework that synergistically integrates a reconfigurable intelligent surface (RIS) with fluid antenna system (FAS) receivers. The port selection diversity of FAS is utilized to enhance signal reception and aid interference suppression during successive interference cancellation (SIC). A central contribution is the development of a max–min fairness-based power allocation (PA) algorithm designed to equalize the ergodic capacities of NOMA users by maximizing the minimum achievable signal-to-interference-plus-noise ratio (SINR) under imperfect SIC conditions, ensuring a fair and balanced rate distribution. Crucially, three major practical impairment sources, such as the combined impact of channel state information (CSI) with bounded estimation error, finite-resolution RIS phase-shift quantization, and residual interference due to imperfect SIC with configurable error levels are explicitly modeled and analyzed. Simulation results evaluate the system performance across various transmit power and FAS port numbers, conclusively demonstrating that the RIS-FAS integration yields substantial gains in ergodic capacity, successfully balances spectral efficiency with user fairness, and highlights the critical trade-offs necessary for realistic networks.

Cognitive Radio Networks (CRNs) have to be efficient in spectrum allocation to meet the Quality of Service (QoS) demands in emergent wireless contexts. This work develops a spectrum allocation framework that incorporates spectrum sensing, adaptive channel-quality scoring, and resource-aware decision-making oriented to QoS. The model evaluated in the ns-3.39 platform with a wireless network of 20 secondary users (SUs) and five primary users (PUs) operating across the 2.4/5 GHz bands with realistic Nakagami-m fading and random waypoint mobility. The simulations demonstrated exceptional sensing performance, with detection probability (Pd) increasing from 78% at -5 dB to 98% at 10 dB and false alarm rates below 5%. Packet-level examination demonstrated that for the smaller packet sizes (512 bytes), there was significantly greater performance (~0.037 Mbps) than larger packet sizes; however, throughput appeared to be the same under varying load conditions. Scalability experiments with respect to the PDR and delay the QoS showed that as SUs increased from 2-20, PDR rose from 13.96% to 28.54%, average latency decreased from 0.0072 s to 0.0036 s and jitter decreased from 0.0027 to 0.0016 s. Given that the proposed framework produced 81% higher throughput, 71% less jitter and 100% PDR under best-case conditions compared to evaluated baseline allocation scheme, these results demonstrate the potential of resource-aware allocation that is QoS-specialised on spectrum utilization, fairness, and user experience in CRNs, as a scalable and energy-efficient means of supporting future cognitive wireless networks.

Integrated sensing and communication (ISAC) is expected to be a key enabler for future wireless networks, improving spectral and hardware efficiency by jointly performing radar sensing and wireless communication within a unified framework. This paper proposes BeamNet, an unsupervised deep learning framework for transmit beamforming in dual-function radar-communication systems operating over general fading with imperfect channel state information (CSI). BeamNet maps noisy estimates of the communication and sensing channels to a transmit beamforming vector and is trained end-to-end by maximizing a weighted sum of the communication rate (CR) and sensing rate (SR), thereby learning the CR-SR Pareto frontier without beamforming labels or embedded optimization solvers. Using Rayleigh fading with perfect CSI, we first show that BeamNet reproduces the analytical Pareto-optimal beamforming solutions. We then use BeamNet to characterize, for Nakagami-m and Rician fading, the CR-SR trade-off across a range of fading parameters, and to assess robustness under distribution mismatch between training and test channels. Finally, under imperfect CSI, we demonstrate that BeamNet yields CR-SR trade-offs that are consistently sandwiched between the perfect-CSI and mismatched analytical baselines, outperforming the closed-form beamformer applied to imperfect CSI and recovering part of the performance loss caused by channel estimation errors. These results indicate that unsupervised learning offers a flexible and robust approach to ISAC beamforming in fading environments with imperfect channel knowledge.

Artificial General Intelligence (AGI) and Large Language Models (LLMs) are gaining attention for their transformative potential across various fields. While LLMs have significantly advanced Natural Language Processing (NLP), they face challenges in reasoning, adaptability, and bias. AGI, with its human-like cognitive functions, offers a promising solution by enhancing the flexibility and context-awareness of LLMs. This paper explores the integration of AGI with LLMs to address complex, dynamic problems, focusing on advancements in Cognitive Radio (CR) and Spectrum Intelligence (SI) technologies. Spectrum sensing, a cornerstone of CR and SI, is critical for identifying underutilized frequency bands and mitigating interference. Traditional methods often struggle in dynamic environments due to their reliance on static models. By combining AGI’s adaptive decision-making with LLMs’ context-aware understanding, the integrated system can enhance the accuracy and efficiency of spectrum sensing. This integration enables better processing of diverse data, prediction of spectrum usage, and dynamic adaptation to changing conditions, paving the way for intelligent spectrum management. As the demand for efficient communication grows with the proliferation of connected devices, AGI-augmented LLMs offer scalable, context-aware solutions to modern communication challenges. AGI with LLMs has the potential to transform spectrum sensing and management into a more adaptive, efficient paradigm, ensuring the performance of next-generation wireless networks.

As one of consumer unmanned electronic systems, unmanned aerial vehicles (UAVs) have become ubiquitous in daily life, essential for numerous tasks, and will play a pivotal role in future wireless networks and Internet-of-Things. However, their widespread adoption and connectivity make them vulnerable to various cyber threats. Although deep learning-based attack detection models offer promising solutions for enhancing UAV network security, these models typically rely on manual trial-and-error approaches for determining hyper-parameters and neural architectures, resulting in limited generalization capability and often overlooking model lightweightness. To address these limitations, this paper proposes an innovative automated multi-objective recurrent neural network (RNN) with attention mechanism, called MoARNN-AM, to effectively solve attack detection problems in UAV systems. In MoARNN-AM, we consider six typical RNN variants and three widely-used attention mechanisms as core classification models for feature extraction and data learning of UAV systems. First, an effective encoding mechanism is developed to represent different combinations along with their corresponding hyper-parameters and neural architectures. Subsequently, considering both attack detection performance and model lightweightness as two objectives, we elaborately design an efficient non-dominated sorting genetic algorithm II (NSGA-II)-based evolutionary operation to evolve various combinations with their associated hyper-parameters and neural architectures for discovering optimized RNN with attention mechanism model. The performance of the proposed MoARNN-AM method is validated using two datasets, i.e., UAV-INDD dataset and WSN-DS dataset collected from different UAV systems. Experimental results demonstrate that MoARNN-AM outperforms five state-of-the-art manually designed attack detection models in terms of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">accuracy, precision, recall</i>, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</i><sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub>-<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">score</i> metrics while maintaining superior model lightweightness.

This paper proposes a quality-of-service (QoS)-aware multi-user communication framework facilitated by multiple simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs). The user groups are established based on their QoS requirements specified by the minimum data rate, which is provisioned by the optimized transmission and reflection configurations of the STAR-RISs. Particularly, we formulate an optimization problem to maximize the aggregate link rate across all users, under group-specified rate requirements by jointly considering the transmit beamforming and STAR-RIS configurations. Then, we employ the Lagrangian duality with quadratic transformation to tackle the non-convexity of the objective. We decompose the problem within a block coordinate descent framework, and the subproblems are solved through convex approximation and iterated to approach the optimal solution. Simulation results demonstrate the effectiveness of the proposed method in enhancing the system sum rate with guaranteed QoS performance for heterogeneous users, offering valuable insights for the deployment of STAR-RISs in future QoS-aware wireless networks.

Semantic communication (SemCom) has been regarded as a promising candidate technology for future intelligent wireless networks by transmitting semantic features. Meanwhile, the rapidly increasing real-time applications bring high demands for audio streaming data processing and exchange. Inspired by this, we propose a SemCom system for audio streaming transmission, named SemAudio. To effectively extract the semantic features of streaming audio, we develop a streaming enhanced Transformer (SET) network to serve as the streaming semantic codec. Specifically, the flexible mask attention network and enhanced memory slot are incorporated in SET to improve the streaming audio reconstruction quality while mitigating the computational complexity. Then, to alleviate the channel noise, we jointly design the semantic and channel coding by integrating the knowledge in both the time and frequency domains. Extensive experimental results demonstrate that the proposed SemAudio outperforms conventional methods in different audio types. Our scheme achieves a better quality-latency tradeoff than traditional communications. Moreover, due to the proposed mask attention strategy, SemAudio can flexibility adjust the latency and adapt to the non-streaming mode.

Integrated sensing and communication (ISAC) is a key pillar for future wireless networks, demanding solutions that simultaneously deliver high-capacity communication and highaccuracy sensing. The terahertz (THz) band, particularly when integrated with fiber-optic networks, emerges as a highly promising candidate, offering the potential for terabit-per-second communication data rates and millimeter-level sensing resolution. Crucially, the integrated waveform is paramount to efficiently and seamlessly combine these dual functionalities, enabling compact frontends design and streamlined baseband processing for photonic THz-ISAC systems. This article presents our recent system-level investigations into diverse integrated ISAC waveform designs. We further synthesize and discuss the latest global research and development progress in advancing this rapidly evolving field.

Integrated sensing, communication, and computation (ISCC) has emerged as a promising paradigm for enabling intelligent services in future sixth-generation (6G) networks. However, existing ISCC systems based on fixed-antenna architectures inherently lack spatial adaptability to cope with the signal degradation and dynamic environmental conditions. Recently, (non-fixed) flexible antenna architectures, such as fluid antenna system (FAS) and movable antenna (MA) have gained significant interest. Among them, intelligent rotatable antenna (IRA) is an emerging technology that offers significant potential to better support the comprehensive services of target sensing, data transmission, and edge computing. This article investigates a novel IRA-enabled ISCC framework to enhance received signal strength, wireless coverage, and spatial adaptability to dynamic wireless environments by flexibly adjusting the boresight of directional antennas. Building upon this, we introduce the fundamentals of IRA technology and explore IRA’s benefits for improving system performance while providing potential task-oriented applications. Then, we discuss the main design issues and provide solutions for implementing IRA-based ISCC systems. Finally, experimental results are provided to demonstrate the great potential of IRA-enabled ISCC system, thus paving the way for more robust and efficient future wireless networks.

Cellular-connected unmanned aerial vehicles (C-UAVs) will be an integral component of future wireless networks. Thanks to the mobility and maneuverability of UAVs, we can transform the interference management and route scheduling problems of C-UAVs into an anti-interference trajectory planning problem, aiming to jointly minimize the UAV mission time and transmission outage time. However, none of the existing methods have taken both the spatio-temporal uncertainty of interference sources and multi-UAV trajectory planning into consideration. To address this issue, we propose a novel method, referred to as uncertainty-aware multi-agent reinforcement learning (UA-MARL), for anti-interference trajectory planning of C-UAVs. In UA-MARL, a transmission outage probability (TOP) has been introduced to improve the robustness of the model. A transmission outage probability experience memory (TOPEM) has been designed to increase sample efficiency and reduce inference time. MARL algorithms integrated with an adaptive post-decision state (PDS) have been introduced to accelerate the convergence and stabilize the training. Experimental results show that UA-MARL outperforms baselines in average reward, convergence efficiency, and convergence stability. Furthermore, we find that higher residential density and wider considered area will lead to a decrease in training efficiency and stability.

This paper explores the efficiency of peak-to-average power ratio (PAPR) reduction methods in non-orthogonal multiple access (NOMA) systems under Rayleigh and Rician fading channels for various sub-carrier configurations. The μGA-PTS method improves PAPR performance with maintaining the bit error rate (BER) and power spectral density (PSD) performance in NOMA systems. The research explores complementary cumulative distribution function (CCDF) of PAPR under sub-carrier sizes from 256 to 4096. Traditional techniques including Clipping and Filtering (C&F), Selective Mapping (SLM), and Partial Transmit Sequence (PTS) offer fair performance against PAPR reduction. With an increase in the number of sub-carriers, their efficiency reduces because the dimensional complexity grows and phase diversity is limited. Compared to conventional methods, the newly proposed micro genetic algorithm-based PTS (μGA-PTS) method considerably outperforms others in all settings. For example, at a CCDF of 10−3, μGA-PTS attains maximum PAPR reductions of 16.4 dB and 7.7 dB for 4096 and 256 sub-carriers, respectively, in Rayleigh channels. Additionally, the μGA-PTS scheme shows high flexibility to Rician fading environments, attaining PAPR gains of up to 5.8 dB. Bit error rate (BER) analysis indicates that μGA-PTS also improves system performance with lower required SNR values (e.g., 5.9 dB and 8.0 dB for μGA-PTS V = 8 and V = 4, respectively, for BER = 10−3) than conventional approaches. The results validate the superior performance of μGA-PTS in efficiently exploring larger solution spaces and selecting optimal phase sequences, making it a promising candidate for high-capacity, power-efficient NOMA systems in 5G and future wireless networks. The technique notably reduces nonlinear distortion, improves signal integrity, and enhances overall transmission reliability under Rayleigh and Rician channels. By minimizing out-of-band radiation through optimized phase selection, it outperforms C&F, SLM, and PTS. It also provides up to 9.7 dB SNR gain at BER = 10−3, providing better signal quality.