Satellite communications (SatCom) are envisioned as a critical enabler of 6G networks, enabling seamless global coverage by integrating terrestrial infrastructures with multi-layered satellite constellations. Among these, the integration between geostationary (GEO) and low Earth orbit (LEO) satellite networks is particularly attractive, as they combine the broad coverage of GEO satellites with the low latency and high capacity of LEO systems. Within this context, we address the resource allocation problem for LEO satellite through a joint design of beam size and transmit power, while accounting for GEO interference constraints, residual Doppler frequency offsets, and frequency reuse strategies. The objective is to maximize the spectral efficiency of LEO system operating in multi-beam GEO-LEO networks. Motivated by the limitations of classical deep reinforcement learning (RL) in such dynamic orbital settings and the potential of quantum RL for accelerated convergence, we propose a hybrid solution that exploits quantum acceleration during offline training and subsequently exports the learned policy into a classical representational format for onboard LEO satellite deployment. A fully quantum deep deterministic policy gradient framework with variational quantum circuit-based actor and critic is developed, along with a neural network-based policy translator for classical inference. To the best of our knowledge, this is the first deployment-ready quantum RL framework in SatCom, offering efficient offline training, reduced retraining latency, and practical deployment compatibility with existing LEO satellite hardware.

The Mean Opinion Score (MOS) is a standard metric for assessing the Quality of Experience (QoE) in Voice over IP (VoIP) applications. Accurate prediction of how network conditions influence MOS is critical for network planning, operation, and optimization. This requires modeling traffic flows with application-level granularity, which significantly increases both the dimensionality and structural complexity of the learning task. The ability to achieve efficient, robust, and generalizable data-driven learning in the presence of such complexity depends critically on the careful design of model architectures. This paper presents NetMOS, a Graph Neural Network (GNN) architecture specifically crafted to model IP networks and predict VoIP MOS scores. NetMOS models IP networks as heterogeneous graphs and designs a two-stage Message Passing Neural Network (MPNN) to capture both permutation invariant and sequential dependencies in traffic flow and network interactions. It uses a Gated Recurrent Unit (GRU) layer to model the ordered influence of links along a traffic path and introduces a customized attention layer with Sigmoid activations to model the cumulative effects of multiple flows on the links. Simulations demonstrate that NetMOS consistently outperforms conventional GNN-based baselines across diverse network topologies in Mean Absolute Error (MAE), R² score, Pearson correlation, and Spearman correlation. NetMOS generalizes effectively beyond the training topology, maintaining high prediction accuracy on unseen network topologies and varying network activity durations without retraining. NetMOS also provides MOS predictions 44×–170× faster than packet-level simulations.

As deep learning–based AI proliferates, model theft and plagiarism pose increasing Intellectual Property (IP) risks. However, watermarking alters model weights and can degrade performance, while fingerprinting often merely verifies uniqueness or requires heavy computation. In this article, we propose a Neural Network Fingerprinting-Based Model Authentication Code (NNFMAC) scheme that verifies both model uniqueness and ownership without affecting performance. NNFMAC extracts key weights from a trained model, applies a median-based method to generate a unique binary fingerprint, and uses this fingerprint as a codebook to encode ownership information via a newly designed index-based function with expansion, producing reliable authentication codes. This non-intrusive approach integrates fingerprinting for uniqueness verification and authentication coding for ownership verification, delivering comprehensive model IP protection while preserving the model’s original performance. Extensive experiments demonstrate that NNFMAC preserves model accuracy without additional training overhead, unlike other watermarking schemes that degrade accuracy by 0.36–1.53%. It achieves bit error rates of 0.12 under weight perturbation, 0.03 under fine-tuning, 0.08 under pruning, and 0.09 under weight shifting attacks, which are substantially lower than the 0.51, 0.49, 0.46, and 0.22 reported in prior work, while consistently outperforming state-of-the-art schemes in effectiveness, efficiency, and robustness.

Data collection and distributed task execution in Internet of Things (IoT) networks require efficient coordination among autonomous agents to handle the growing volume of sensing and computational demands. Unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) present promising candidates for these operations due to their complementary capabilities and mobility advantages. However, effective cooperation between these heterogeneous agents faces significant challenges including communication limitations, energy constraints, and suboptimal task allocation efficiency. In this paper, we aim to maximize data collection capacity, task completion rates, while minimizing energy consumption across all UAVs. We propose U2GNet, a novel UGV-assisted framework for UAV that enables efficient task offloading and resource allocation in dynamic environments by leveraging Deep Reinforcement Learning (DRL) enhanced with Heterogeneous Graph Attention Networks (HGAT). The framework employs HGAT to process local observations and information shared by neighboring agents, while Gated Recurrent Units (GRU) address partial observability by integrating historical information, and DRL optimizes the decision-making process. Simulation results demonstrate that U2GNet improves the average data collection rate and task completion rate by 16.90% and 10.81% respectively compared to the baseline HGN approach.

The advancement of sixth-generation (6G) technology significantly enhances the Internet of Things (IoT) applications, especially in remote areas where traditional cellular infrastructure is not feasible. Satellite communication, a crucial component of 6G, extends IoT connectivity to these underserved regions. In this context, the growing interest in low Earth orbit (LEO) satellite communication stems from its recent advancements in offering high data rate services and minimizing service latency. Next-generation LEO satellite systems, with regenerative capabilities, allow for adaptability in bandwidth management and on-board data processing. However, the scarcity of satellite spectrum presents a barrier to the expansion of LEO satellite networks and the development of integrated terrestrial-space infrastructures. To address this challenge, we propose constructing a radio environment map (REM) aboard LEO satellites to opportunistically tap into the unused spectrum of geostationary (GEO) satellites within a cognitive GEO-LEO satellite IoT network. This solution facilitates REM construction through collaboration among neighboring LEO satellites while also considering the frequency reuse scheme of GEO satellites. Our REM construction approach leverages cyclostationary-based sensing at LEO satellites, serving the dual purpose of REM construction and Doppler shift estimation to track multiple GEO frequency signals. Following REM construction, LEO satellites utilize deep learning techniques to predict GEO spectrum occupancy without further sensing, thereby optimizing secondary spectrum utilization of the IoT network. We propose a deep learning neural network architecture based on a sequence-to-sequence model tailored for spectrum prediction at LEO satellites. Simulations demonstrate superior performance in detection probability of the proposed deep learning network compared to convolutional long short-term memory networks, achieving this with lower computational complexity.

Mobile crowd sensing (MCS) is an emerging paradigm that enables participants to collaborate on various sensing tasks. UAVs are increasingly integrated into MCS systems to provide more reliable, accurate and cost-effective sensing services. However, optimizing UAV trajectories and communication efficiency, especially under non-line-of-sight (NLoS) channel conditions, remains a significant challenge. This paper proposes TRAIL, a Transformer-enhanced deep reinforcement Learning (DRL) algorithm. TRAIL aims to jointly optimize UAV trajectories and Reconfigurable Intelligent Surface (RIS) phase shifts to maximize data throughput while minimizing UAV energy consumption. The optimization problem is modeled as a Markov Decision Process (MDP), where the Transformer architecture captures long-term dependencies in UAV trajectories, and these features are input into a Double Deep Q-Network with Prioritized Experience Replay (PER-DDQN) to guide the agent in learning the optimal strategy. Simulation results demonstrate that TRAIL significantly outperforms state-of-the-art methods in both data throughput and energy efficiency.

Driving trajectory data remains vulnerable to privacy breaches despite existing mitigation measures. Traditional methods for detecting driving trajectories typically rely on map-matching the path using Global Positioning System (GPS) data, which is susceptible to GPS data outage. This paper introduces CAN-Trace, a novel privacy attack mechanism that leverages Controller Area Network (CAN) messages to uncover driving trajectories, posing a significant risk to drivers’ long-term privacy. A new trajectory reconstruction algorithm is proposed to transform the CAN messages, specifically vehicle speed and accelerator pedal position, into weighted graphs accommodating various driving statuses. CAN-Trace identifies driving trajectories using graph-matching algorithms applied to the created graphs in comparison to road networks. We also design a new metric to evaluate matched candidates, which allows for potential data gaps and matching inaccuracies. Empirical validation under various real-world conditions, encompassing different vehicles and driving regions, demonstrates the efficacy of CAN-Trace: it achieves an attack success rate of up to 90.59% in the urban region, and 99.41% in the suburban region.

Aerial computing paradigms, particularly those involving UAVs as access points and radio towers, show significant promise for local data analysis and real-time service provision in aerial access networks. However, the limited battery life of UAVs, compounded by the high-energy demands of communication tasks and prolonged computation delays, presents a significant challenge. Deep reinforcement learning (DRL) enables UAVs to autonomously optimize their operations, reducing both energy consumption and latency. Nevertheless, the prolonged learning process of DRL can lead to inefficiencies, especially in dynamic environments where the equitable participation of UAVs is crucial. To address these issues, we introduce a fairness-oriented federated learning (FL) scheme that employs importance sampling to select UAVs for training, ensuring equitable utilization of each UAV's data. Furthermore, we integrate this FL fairness scheme into the design of DRL algorithms, termed FedDRL. This algorithm jointly optimizes the computation capabilities and bandwidth allocation of UAVs to minimize system costs. Numerical results demonstrate the fairness of FedDRL in fFL networks. Specifically, compared to other state-of-the-art DRL algorithms (e.g., TD3 and DDPG), FedDRL reduces system costs by 43.82% and 49.45%, respectively.

As demand for ubiquitous connectivity grows, integrating satellite communications into sixth-generation (6G) networks has emerged as a crucial strategy to enhance global coverage, especially in remote and underserved regions. However, achieving the stringent performance, reliability, and spectral efficiency required for 6G presents significant challenges. Coexisting geostationary (GEO) and low Earth orbit (LEO) satellite networks offer a promising solution by enabling complementary coverage and enhanced service capabilities. Nonetheless, a critical challenge is managing intersystem interference from the LEO satellite system on the GEO system when sharing spectral resources, all while maintaining the performance of both systems. To address this, this paper introduces a fast fuzzy deep reinforcement learning (DRL)-based approach for joint beam design and power allocation in multi-beam GEO-LEO coexisting satellite networks. A robust design problem of LEO beam size and power allocation is formulated to maximize the spectral efficiency of the LEO system, considering tolerable interference on the GEO system, frequency reuse schemes employed by both GEO and LEO systems, and Doppler frequency offset induced by LEO satellite movement. A fast DRL algorithm, integrating fuzzy logic, post-decision state, and deep deterministic policy gradient, is proposed to solve this problem. Numerical results demonstrate a faster learning convergence rate for the proposed DRL algorithm compared to benchmark algorithms and confirm that the proposed method enhances LEO spectral efficiency while maintaining tolerable intersystem interference on the GEO system.

As the primary standard protocol for modern cars, the controller area network (CAN) is a critical research target for automotive cybersecurity threats and autonomous applications. As the decoding specification of CAN is a proprietary black-box maintained by original equipment manufacturers (OEMs), conducting related research and industry developments can be challenging without a comprehensive understanding of the meaning of CAN messages. In this article, we propose a fully automated reverse-engineering system, named ByCAN, to reverse engineer CAN messages. ByCAN outperforms the existing research by introducing byte-level clusters and integrating multiple features at both the byte and bit levels. ByCAN employs the clustering and template matching algorithms to automatically decode the specifications of CAN frames without the need for prior knowledge. Experimental results demonstrate that ByCAN achieves high accuracy in slicing and labeling performance, i.e., the identification of CAN signal boundaries and labels. In the experiments, ByCAN achieves slicing accuracy of 80.21%, slicing coverage of 95.21%, and labeling accuracy of 68.72% for the general labels when analysing the real-world CAN frames.

This paper explores the significance of the timeliness of information in the context of fifth generation (5G) non-terrestrial networks (NTN). As 5G technology continues to evolve, its integration with non-terrestrial components such as satellites, high-altitude platforms, and unmanned aerial vehicles brings about new possibilities and challenges for ensuring the timely delivery of information. In this paper, we delve into the network structure of NTNs and emphasize the significance of timeliness in various applications, including 5G massive Internet of Things and enhanced Mobile Broadband. We conduct an in-depth review of the design technologies and methodologies that enhance the timeliness of information in these applications. These include network architecture design, resource allocation, protocol design, modulation design, trajectory planning, reconfigurable intelligent surfaces design, energy harvesting scheduling design, offloading strategy design, and caching strategy design. By exploring these technical aspects and solutions, we aim to provide valuable insights into ensuring timely information delivery in 5G NTN. Furthermore, we propose potential future research directions to further improve the timeliness of information in NTNs. Recognizing the importance of timeliness and addressing the related challenges will unlock the full potential of 5G NTN, enabling the successful deployment and operation of a wide range of applications and services that depend on real-time data exchange.

The undisclosed Controller Area Network (CAN) decoding specification is important to the in-vehicle network (IVN) research for both industry and academia. Researchers have developed several CAN reverse engineering systems to predict signal boundaries and labels in order to map out CAN signal decoding specifications. Existing works mainly use one parameter (i.e., bit flip rate) to determine CAN signals boundary, which results in biased slicing and labelling of CAN signals. In this paper, we propose a multi-layer CAN reverse engineering system to cluster signal boundary at byte-level and label sliced CAN signal blocks at bit-level. The proposed system avoids biased signal slicing and labelling by introducing multiple parameters in signal classification, while existing works only use the bit flip rate and the number of unique value. The feasibility and adaptability of the proposed system is assessed by deploying it into a web application as a functionality module. We evaluate the proposed system with CAN messages from real cars. Compared with existing reverse engineering models, the proposed system introduces multi-layer signal processing to avoid over-slicing and over-labelling problem.

The legacy design of the Controller Area Network (CAN) weakens the encryption and authentication of the In-Vehicle Networks (IVN). Anomaly detection systems, e.g. the Long-Short Term Memory (LSTM) based Intrusion Detection System (IDS), are employed to remedy the defection of CAN. Existing works feed the LSTM-based IDS with the byte values of the data payload of CAN to train and test the LSTM model. In this paper, we propose an LSTM-based IDS leveraging byte-level features, i.e., byte flip rate, byte-level change rage, and byte-level distinct value rate, to augment the sensitivity of proposed LSTM-based IDS when distinguishing malicious CAN messages. By using the byte-level signal features, the proposed system achieves high accuracy with a small size of the training dataset. The experiment results show that the model with the byte-level features can achieve a performance gain of the$F$1Score up to 20% over the model without the byte-level features.

The development of location-based services (LBS) leads to the rapid growth of location data, potentially increasing the threat to location privacy. Existing location obfuscation techniques focus on two-dimensional (2D) planar areas and overlook the features of road networks. In this paper, we leverage differential privacy and propose a new notion of Road Network-Indistinguishability (RN-Indistinguishability) to measure the indistinguishability of locations in road networks. With the RN-Indistinguishability, we design a Cloaking Region Obfuscation (CRO) mechanism to protect the location privacy of vehicles on roads. With the CRO mechanism, vehicle locations in a cloaking region are obfuscated following the same obfuscation distribution. The proposed CRO mechanism is proved to achieve RN-Indistinguishability and can be generalized with road network features holding the triangle inequality. Comprehensive experiments show that the CRO mechanism outperforms existing 2D obfuscation mechanisms in real-world road networks.

Accurate assessment of fish quality is difficult in practice due to the lack of trusted fish provenance and quality tracking information. Working with Sydney Fish Market (SFM), we develop a Blockchain-enabled fish provenance and quality tracking (BeFAQT) system. A multilayer Blockchain architecture based on attribute-based encryption (ABE) is proposed to tackle the privacy issue caused by applying Blockchain to secure supply chain data and achieve trusted and confidential data sharing among parties in fish supply chains. An Internet-of-Things (IoT) chain saves encrypted fish provenance and quality tracking data, and an ABE chain is specifically designed for the access control to the data in the IoT chain. Latest IoT and artificial intelligence (AI) technologies, including NarrowBand-IoT, image processing, and biosensing, are developed for fish origin proof, supply chain tracking, and objective fish quality assessment. As proven by field trials with SFM and a local fish supply chain, the BeFAQT is able to provide trusted and comprehensive fish provenance and quality tracking information in real time.

To meet the capacity needs of the next generation wireless communications, U.S. Federal Communications Commission has recently introduced Spectrum Access System. Spectrum is shared between three tiers - Incumbents, Priority Access Licensees (PAL) and General Authorised Access (GAA) Licensees. When the incumbents are absent, PAL and GAA share the spectrum under the constraint that GAA ensure the aggregate interference to PAL is no more than -80 dBm within the PAL protection area. Currently GAA users are required to report their geolocations. However, geolocation is private information that GAA may not be willing to share. We propose a distributed GAA power allocation algorithm that does not require centralised coordination on sharing locations with other GAA users via SAS. We analytically proved the critical point of the interference along the PAL protection area to avoid calculating the interference on every points of the area. We proposed exclusion zone, transitional zone and open zone for GAA users to calculate the self-determined transmit power. Simulation results show that our method meets the interference requirement and achieve more than 90% of capacity approximation to the optimal centralised method, while completely masking the GAA locations.

Unmanned Aerial Vehicles (UAVs) are fast becoming a popular choice in a variety of applications in wireless communication systems. UAV-mounted base stations (UAV-BSs) are an effective and cost-efficient solution for providing wireless connectivity where fixed infrastructure is not available or destroyed. We present a method of using UAV-BSs to provide coverage to mobile users in a fixed area. We propose an algorithm for predicting the user locations based on their mobility data and clustering the predicted locations, so that one UAV-BS would provide coverage to one user cluster. The proposed method, hence is similar to the UAV-BSs following the users to keep them under the coverage region. Simulation results show that the proposed method increases the user coverage by 47%-72% and increases the spectral efficiency by 43%-55% depending on the scenario and in addition, reduces the number of UAV-BSs required to provide coverage.

Network densification has been one of the principal causes of performance gain in cellular networks, and 5G networks will not be any different. As cell sizes shrink, handovers become more frequent incurring extra delays that bury all the prospective gains. Mobility in multi-tier dense cellular networks calls for a change in the way it has been traditionally handled in an always-on world, where users take universal data access for granted. Invisible to them, mobile network operators need to provision backhauling to include advanced interference mitigation techniques. In this paper, we propose a spectrum database-aided handover management technique that aims to mitigate the number of disconnections without overloading the backhaul unnecessarily. The proposed technique exploits a spectrum database that stores reception information along with geolocation data, commercially available on any handheld device. Moreover, we have benchmarked several state-of-the-art handover schemes for 5G networks against ours in a realistic urban environment with user mobility trace data. The results highlight that our method can deliver the same downstream traffic with 33% decrease in disconnections when compared to the conventional approach. At the same time, backhaul traffic is reduced up to 68% against our counterparts.

Unmanned Aerial Vehicles (UAV) are gaining popularity in a range of areas and are already being used for a wide variety of purposes. While UAVs have many desirable features, limited battery lifetime is identified as a key restriction in UAV applications. Typical UAVs being electric devices, powered by on-board batteries, this constrain has limited their capabilities to a considerable extent. Thus planning UAV missions in an energy efficient manner is of utmost importance. To achieve this, for prediction of power consumption, it is necessary to have a reliable power consumption model. In this paper, we present a consistent and complete power consumption model for UAVs based on empirical studies of battery usage for various UAV activities. The power consumption model presented in this paper can be readily used for energy efficient UAV mission planning.

Unmanned Aerial Vehicles (UAV) are becoming popular in a range of areas. This has given rise to the concept of UAV swarms, where multiple UAVs act together to achieve a common task. With multiple UAVs flying in close proximity to each other, sharing the same airspace, the risk of inter-UAV collisions increases. It's important to avoid these collisions while having minimal impact on the UAV system. We propose a novel Potential Field Method (PFM) based algorithm for inter-UAV collision avoidance which considerably reduces the total time taken by the UAV system to achieve its goal. We control the collision avoidance actions of the UAVs by virtually relocating their targets. The positions of the virtual targets are calculated to minimize the collision probability, based on a probability function we introduced. The proposed algorithm reduces the total system time approximately by 20\% as opposed to the traditional PFM.

Spectrum Access System (SAS) is a three-tier spectrum sharing framework proposed for 3.5 GHz by Federal Communication Commission (FCC) in the United States. General Authorized Access (GAA) users in SAS do not have an assigned channel and can opportunistically access the Priority Access Licensee (PAL) channel satisfying the interference constraint proposed by FCC. Coexistence among GAA users in SAS is a key problem to be solved to enhance the system capacity to meet the increasing traffic demand. In this work, we propose a method for fair and efficient spectrum utilisation for GAA users. To achieve the fairness among GAA users equal interference budget allocation scheme is proposed for each set of GAA users that can hear each other. Our proposed method decide the optimal channel switching schedule that maximises the average capacity of GAA users while satisfying the interference constraint at PAL protection area. This work jointly considers the fairness between GAA users and the average capacity maximisation of GAA network. Simulation result justifies the performance of our proposed method for average capacity maximisation of GAA users and fairness between GAA users by comparing with existing works.

The optimal use of spectrum is a key focus for all regulatory bodies. Federal Communications Commission has introduced Spectrum Access System (SAS) to maximise the spectrum utilisation in the US 3.5 GHz band. SAS is a three-tier spectrum sharing framework where Citizen Broadband Radio Service (CBRS) devices can access the channel when it is not used by Incumbent Access users. CBRS consists of Priority Access Licensee (PAL) and General Authorized Access (GAA). In this paper, we consider the problem of optimum transmit power allocation for GAA users using a carrier sensing range i.e. maximum distance a user can be sensed while guaranteeing the interference to PAL from GAA users is below the threshold. We use carrier sensing range to find the sets of GAA users that cannot transmit at the same time and adjust the interference budget of transmitting GAA users. We present an algorithm for transmit power allocation for GAA users in the SAS. The proposed algorithm uses the transmission characteristics and location information provided by Citizen Broadband Radio Service Devices to SAS to maximise the peak capacity of GAA users ensuring the interference constraint to PAL. Simulation results show that the proposed algorithm significantly increases the peak capacity of GAA users by considering the carrier sensing range and adjusted interference budget.

The advent of 5G networks, where a plethora of spectrum-sharing schemes are expected to be adopted as an answer to the ever-growing users' need for data traffic, will require addressing mobility ubiquitously. The trend initiated with the deployment of heterogeneous networks and past standards will give way to a multitiered network where different services will coexist, such as device-to-device, vehicle-to-vehicle or massive-machine communications. Because of the high variability in the cell sizes given the different transmit powers, the classical handover process, which relies solely on measurements, will lead to an unbearable network overhead as a consequence of the high number of handovers. The use of spatial databases, also known as radio environment maps (REM), was first introduced as a tool to detect opportunistic spectrum access opportunities in cognitive radio applications. Since then, REM usage has been widely expanded to cover deployment optimization, interference management or resource allocation to name a few. In this paper, we introduce a handover algorithm that can predict the best network connection for the current user's trajectory from a radio environment map. We consider a geometric approach to derive the handover and handover-failure regions and compare the current handover algorithm used in Long-Term Evolution with our proposed one. Results show a drastic reduction in the number of handovers while maintaining a trade-off between the ping-pong shandover and the handover-failure probabilities.

Spectrum Access System (SAS) is a three tier spectrum sharing framework proposed by the FCC. In this framework the aggregate interference of tier-3 General Authorised Access (GAA) users should be below a predetermined threshold anywhere within the tier-2 Priority Access Licensee (PAL) exclusion zone. GAA are expected to use a diverse range of Radio Access Technologies (RATs) with different levels of loading. We propose an optimal transmit power and probability of spectrum utilisation allocation scheme for GAA users that meets the average aggregate interference constraint within the GAA network. Most of the capacity maximisation studies consider the instantaneous aggregated interference from secondary users. In this paper we present an average aggregated interference method to optimise the capacity of GAA users in a single channel. Simulation results suggest that we can significantly increase the capacity of the channel by considering the probability spectrum utilisation of GAA users.

A distributed antenna system (DAS) architecture is a key enabler for Cloud Radio Access Networks (CRAN) where geographically separated base stations are connected to a centralized processing and decision making unit. Many schemes have been proposed to leverage Fractional Frequency Reuse (FFR) and co- ordinated joint transmission between base stations to improve cell-edge performance for static network deployments. In this paper, we investigate dynamic decision making that whether co-ordinated joint transmission should be selected in the downlink of a FFR-aided DAS. We derive the transmitting Signal-to- Noise-Ratio (SNR) threshold for making these decisions and we show that this SNR plays a key role in the FFR- aided DAS analysis and can be used as a guide in the evaluation of DAS performance.

A distributed antenna system (DAS) architecture is believed to be able to enhance capacity performance of Cloud Radio Access Networks (C-RAN), especially for users near the cell boundary who experience low Signal-Noise-Ratio (SNR). However, the problem of finding the analytical bounds on the capacity of DAS with the rising number of antennas in low SNR rigime has not been fully studied. In this paper, we investigate a case in C-RAN of multiple transmitting base stations and a single receiving user under low SNR conditions. We derive closed-form upper and lower bounds in efficiently computable expressions for differential capacity (DCAP) using the moment generating function (MGF) of SNR. Bounds accuracy is evaluated and compared to results in current literature. Numerical results corroborate our analysis and the analytic bounds on DCAP is tight in the low SNR regime. Furthermore, The upper bound approximates better compared with the one obtained in [1] under two different channel models. These lower and upper bounds provide more accurate capacity measures which can be used in the evaluation of DAS performance and C-RAN design.

An efficient hardware-optimized Physical Random Access Channel (PRACH) baseband signal generation algorithm and its ASIC implementation in the LTE user equipment (UE) transmitter are presented in this paper. A simplified DFT of the Zadoff-Chu (ZC) sequence as well as a phase computation are applied to the prime size DFT of the PRACH preamble and the large size IDFT is accomplished by groups of smaller size IFFTs. The optimized algorithm achieves significantly lower computational complexity compared with the original algorithm in the LTE specification and better performance compared to another publication. The ASIC architecture is also designed to reduce the memory size and logic complexity, which achieves a low hardware cost in terms of the cell area. The proposed design was implemented in 65nm CMOS and it was demonstrated that this design can satisfy the timing requirements of the LTE specification.