Open Radio Access Network (O-RAN) architectures are driving a shift towards modular, software-defined RANs, where virtualization is key for flexible component development. OpenAirInterface (OAI) provide software-based RAN functionalities, complemented by Near-Real-Time RAN Intelligent Controllers (Near-RT RICs) like FlexRIC. However, the performance of these virtualized environments is fundamentally limited by the underlying compute infrastructure. This study establishes a crucial performance baseline by isolating the impact of raw computational constraints within a virtualized O-RAN testbed composed of OAI simulators and the FlexRIC controller. To achieve this, our setup deliberately excludes radio virtualization middleware, ensuring all signal processing and channel emulation to be offloaded to the CPU. We assess the impact of these constraints on the end-user’s Quality of Experience (QoE) by streaming video and audio files to a client within the simulated UE’s network namespace. Our findings reveal that video quality degrades significantly, as reflected in low VMAF, while audio quality holds steady under the same constraints.

In this paper, we investigate the uplink (UL) radio resource management for 5G aerial corridors with an open-radio access network (O-RAN)-enabled cell-free (CF) massive multiple-input multiple-output (mMIMO) system. Our objective is to maximize the minimum spectral efficiency (SE) by jointly optimizing unmanned aerial vehicle (UAV)-open radio unit (O-RU) association and UL transmit power under quality-of-service (QoS) constraints. Owing to its NP-hard nature, the formulated problem is decomposed into two tractable sub-problems solved via alternating optimization (AO) using two computationally efficient algorithms. We then propose (i) a QoS-driven and multi-connectivity-enabled association algorithm incorporating UAV-centric and O-RU-centric criteria with targeted refinement for weak UAVs, and (ii) a bisection-guided fixed-point power control algorithm achieving global optimality with significantly reduced complexity, hosted as xApp at the near-real-time (near-RT) RAN intelligent controller (RIC) of O-RAN. Solving the resource-allocation problem requires global channel state information (CSI), which incurs substantial measurement and signaling overhead. To mitigate this, we leverage a channel knowledge map (CKM) within the O-RAN non-RT RIC to enable efficient environment-aware CSI inference. Simulation results show that the proposed framework achieves up to 440% improvement in minimum SE, 100% QoS satisfaction and fairness, while reducing runtime by up to 99.7% compared to an interior point solver-based power allocation solution, thereby enabling O-RAN compliant real-time deployment.

This paper investigates uplink radio resource optimization of a user-centric (UC) cell-free (CF) massive multiple-input multiple-output (mMIMO) system aided by the rate splitting multiple access (RSMA) technique subject to pilot contamination. We formulate problem to maximize the minimum spectral efficiency (SE) problem by jointly addressing decoding order selection, power allocation, and access point (AP) - user equipment (UE) association assignment. The envisioned optimization exhibits two challenges. First, it requires global channel state information (CSI) for near-optimal performance, which incurs substantial overhead and data collection costs in large-scale CF networks. Second, the optimization is intractable due to its NP-hard and discrete non-linear programming nature. To address the CSI acquisition issue, we utilize a digital twin (DT) of the CF mMIMO system, leveraging its context-awareness to acquire global CSI with reduced overhead. To address computational intractiablity of the optimization problem, we decompose it into three sub-problems. The power allocation sub-problem is transformed into a second-order cone programming problem and solved by the bisection method. Additionally, we propose a computationally efficient heuristic approach for power allocation. Next, we propose an analytical method for the decoding order selection by ranking the channels in descending order of strength. Simulation results validate the ability of the proposed approach to attain the near-optimal performance. Subsequently, the AP-UE association assignment problem is solved by a heuristic approach to further improve the SE performance. Finally, we solve the original NP-hard problem in a unified manner via the block-coordinate descent algorithm. Simulation results underscore a substantial 61% improvement in the SE performance when integrating the RSMA technique into a UC CF mMIMO system.

This paper addresses channel estimation error (CEE) mitigation in user-centric cell-free (CF) massive multiple-input multiple-output (mMIMO) systems under pilot contamination (PC). To suppress the PC, we formulate an optimization problem to minimize the normalized CEE through the joint treatment of the pilot power allocation and the pilot assignment. However, the NP-hard nature of the problem makes finding a global optimal solution computationally infeasible. To tackle this challenge, we decompose the problem into two sub-problems: the fractional programming (FP) technique is employed for pilot power allocation, and a multi-agent reinforcement learning (RL) approach is applied for pilot assignment. The RL-based scheme, however, involves iterative action generation and reward computation, leading to significant overhead from repeated information exchanges. To mitigate this issue, we leverage a digital twin (DT) of the CF mMIMO system, utilizing its virtual environment and contextual awareness to optimize CEE reduction with minimal overhead. By employing closed-form expressions, the proposed schemes achieve computational efficiency without reliance on complex numerical solvers. Finally, we solve the original NP-hard problem via the block-coordinate descent and sequential optimization methods. Numerical evaluations demonstrate that the proposed FP-based pilot power allocation scheme improves the 95%-likely spectral efficiency (SE) by up to 15% and reduces the average pilot transmission power by up to 83% compared to existing methods. Furthermore, the pilot assignment scheme enhances the average SE performance by up to 7.4% over state-of-the-art pilot assignment approaches. These results validate the effectiveness of our proposed framework in reducing CEE, leading to enhanced improvement in system performance in the presence of PC.

In this paper, we investigate user equipment (UE)-radio access technology (RAT) association and bandwidth assignment to maximize sum-rates in a multi-RAT network. To this end, we formulate an optimization problem that jointly addresses UE association and bandwidth allocation, adhering to practical constraints. Because of the NP-hard nature of this problem, finding a globally optimal solution is computationally infeasible. To address this challenge, we propose a centralized and computationally efficient heuristic algorithm that aims to maximize sumrates while enhancing quality of service (QoS). Yet, the proposed approach requires global channel state information (CSI) for near-optimal performance, which incurs substantial overhead and data collection costs in large-scale multi-RAT networks. To alleviate this burden, we use a digital twin (DT) of the multi-RAT network, leveraging its context-awareness to acquire global CSI with reduced overhead. Our numerical results reveal that our approach improves sum-rates by up to 43 % over baseline method, with less than a 5 % deviation from the theoretical optimal solution, while achieving up to a 43 % improvement in QoS. Further analysis reveals that our method not only surpasses the optimal solution in terms of QoS enhancement, but also ensures significant computational efficiency.

The Internet of Vehicles (IoV) generates massive data traffic and demands reliable end-to-end connectivity to achieve multi-Gbps throughput between vehicles and roadside units. Millimeter-wave (mmWave) bands, with their abundant bandwidth, are promising for high-throughput IoV networks. However, in this context, significant propagation losses, intermittent line-of-sight availability, and dynamic topology changes due to vehicle mobility present critical challenges. This article introduces resource allocation for vehicular networks ($\textsf {RAVEN}$), a centralized resource management framework designed to address these challenges effectively.$\textsf {RAVEN}$leverages a digital twin network (DTN) to optimize the end-to-end system capacity of multihop mmWave IoV networks by effectively managing co-channel interference among vehicles.$\textsf {RAVEN}$comprises the following three steps: 1) a channel prediction step that utilizes DTN’s awareness of vehicular mobility and environmental contexts to predict site-specific channel gains for vehicular communication links; 2) a clustering step that partitions vehicles into nonoverlapping clusters, allowing vehicles within each cluster to share the same mmWave channel for data transmission, while simultaneously reducing co-channel interference; and 3) a multihop connectivity optimization step that provides a connected vehicular networking topology by jointly optimizing vehicle-to-vehicle and vehicle-to-infrastructure connectivity using a graph theory approach. A proof-of-concept of$\textsf {RAVEN}$is developed by implementing a DTN on the Microsoft Azure Digital Twins platform while integrating real-world vehicular mobility traces, edge-cloud collaboration, and parallel computing. Extensive simulations demonstrate that$\textsf {RAVEN}$outperforms several benchmark schemes, and offers scalability and near real-time decision-making capabilities for managing interference in large-scale IoV networks.

The exponential growth of data traffic beyond the 5G era necessitates improved resource utilization for the integrated terrestrial and non-terrestrial networks (ITNTN). In this work, we consider a multi-user multiple input multiple output (MU-MIMO)-empowered 5G ITNTN network consisting of terrestrial 5G and multi-beam geostationary earth orbit (GEO) satellite-based gNBs and develop an interference management framework that allows multiple users to receive downlink data over the same resource blocks (RB) simultaneously. Our developed framework first employs a traffic offloading algorithm by leveraging the reference signal received power (RSRP) and celledge width criteria to offload traffic from terrestrial to NTN networks. Subsequently, we formulate the resultant interference management as a joint power allocation and user-RB scheduling optimization problem to maximize the network’s spectral efficiency. Since the joint optimization problem is NP-hard and computationally intractable, a fractional programming-based solution is developed to obtain sub-optimal yet efficient transmit power allocation and user scheduling at terrestrial and satellite gNBs. A realistic ITNTN simulator is developed for performance evaluation by considering 3GPP channel models, antenna gains, and 5G RB numerology in rural terrestrial-GEO coexistence scenarios. Extensive simulation results confirm the efficacy of the proposed framework in managing interference and improving resource utilization at 5G ITNTN networks.

Internet-of-Vehicles (IoV) is envisioned to connect vehicles with each other, the surrounding environment, and central control centers. Spectrum sharing among active vehicular links is imperative to enhance the utilization of the spectrum licensed to IoV networks. However, co-channel interference among neighboring vehicular communication links poses a fundamental challenge when enabling spectrum sharing in IoV networks. This paper introduces a resource optimization framework, entitled PRISM (ProactiveResource optimization forInterference andSpectrumManagement), to mitigate co-channel interference in IoV networks. PRISM proactively allocates resources among a set of Vehicle-to-Infrastructure (V2I) communication links by accurately predicting the links’ positions and multi-path channel gains, thereby preventing outdated resource scheduling in dynamic IoV networks. PRISM is a three-step approach. In the first step, a multi-layer long short-term memory neural network and transfer learning are employed to predict the vehicles’ positions. In the second step, a digital twin network incorporating high-fidelity 3D maps and a ray tracing tool entitled$\mathrm {Sionna}^{\textrm {TM}}$is used to predict the V2I links’ multi-path channel gains. In the third step, a resource allocation algorithm is executed to efficiently determine V2I clusters and their transmit power allocations to maximize the overall system capacity. Simulation results show that PRISM enhances IoV network’s capacity up to 33% compared to non-proactive schemes, as validated through a simulation framework using real-world vehicular mobility traces.

Automated spectrum monitoring necessitates the accurate detection of low probability of intercept (LPI) radio frequency (RF) anomaly signals to identify unwanted interference in wireless networks. However, detecting these unforeseen low-power RF signals is fundamentally challenging due to the scarcity of labeled RF anomaly data. In this paper, we introduce WANDA (Wireless ANomaly Detection Algorithm), an automated framework designed to detect LPI RF anomaly signals in low signal-to-interference ratio (SIR) environments without relying on labeled data. WANDA operates through a two-step process: (i) Information extraction, where a convolutional neural network (CNN) utilizing soft Hirschfeld-Gebelein-Rényi correlation (HGR) as the loss function extracts informative features from RF spectrograms; and (ii) Anomaly detection, where the extracted features are applied to a one-class support vector machine (SVM) classifier to infer RF anomalies. To validate the effectiveness of WANDA, we present a case study focused on detecting unknown Bluetooth signals within the WiFi spectrum using a practical dataset. Experimental results demonstrate that WANDA outperforms other methods in detecting anomaly signals across a range of SIR values (-10 dB to 20 dB).

Autonomous Vehicle System (AVS) is rapidly advancing and is expected to completely transform the transportation industry, bringing about a new era of mobility. As digital data proliferation strains network resources, the demand for performing resource-intensive edge-assisted Deep Learning tasks in AVS with limited computational resources becomes increasingly challenging. The advent of 5G and future cellular networks (NextG) offers the promise of facilitating the seamless execution of these tasks. This research aims to diminish dataset size, in harmony with the integration capabilities of the Semantic and Flexible Open Radio Access Network (SEMO-RAN) framework, which is anticipated to reduce latency and refine resource allocation in vehicle image processing. The focus of this study is on optimizing image compression without compromising the accuracy of vehicle classification, leveraging esteemed Convolutional Neural Network models like YOLOv5 . By employing Generative Adversarial Network compression and Wavelet Image Compression methods, our study achieves an impressive 81.82% reduction in data size while maintaining a 96.97% accuracy in classification tasks. This underscores the potential for significant efficiency gains in AVS through improved data management, supported by Digital Twins (DT), Integrated Sensing and Communication (ISAC), and O-RAN.

In the digital age, the agricultural industry faces unique challenges, including the efficient management and maintenance of farm vehicles and tools. This paper introduces a blockchain-based agricultural vehicle and tool rental system leveraging Hyperledger Fabric. By utilizing blockchain technology, the system ensures immutability, transparency, and trust among all parties involved. The primary focus is on creating a decentralized and secure platform that not only facilitates the rental process but also introduces next-generation farm vehicle and tool maintenance capabilities. Throughout the development process, we faced and overcame significant challenges, including the need for a custom-tailored blockchain solution to meet the unique requirements of agricultural asset management. The proposed solution enhances traceability and accountability, thereby reducing fraudulent activities and improving overall operational efficiency. A working demo of the system is available on GitHub, providing an open-source resource for researchers and developers. This accessibility enables further exploration and adaptation of the system across various fields, from supply chain management to peer-to-peer marketplaces. The blockchain’s immutability aspect serves as a cornerstone for building trust in digital transactions, offering potential applications in diverse sectors such as finance, healthcare, and government services. The implementation details, innovative approaches to technical obstacles, and practical implications of our solutions are thoroughly discussed, providing valuable insights for researchers and practitioners in blockchain technology and agricultural systems.

The detection of unauthorized frequency hopping (FH) signals has several applications in securing the radio frequency spectrum and achieving spectrum awareness in both tactical and cyber-physical systems. However, the blind detection of adversary FH signals is a challenging task, particularly in low signal-to-noise ratio (SNR) regimes, due to the adoption of dynamic hopping patterns. In this study, we propose a cyclo-stationary signal features-based blind FH signal detection scheme to address this challenge. Our proposed scheme consists of two steps: (i) feature extraction, where cyclic features are extracted from the spectral correlation function of the signals, and (ii) feature classification, where the extracted features are associated with ON/OFF detection states using a trained support vector machine (SVM) classifier. We leverage both binary and one-class SVM classifiers to enable adversary FH signal detection with and without pre-existing signal labels. Extensive simulations are conducted to verify the efficacy of the proposed FH signal detection scheme in low SNR regimes. Simulation results also provide insights into the interplay of various system parameters, such as the numbers of cyclic features and emission bandwidth, on the detection performance of the proposed SVM classifiers.

Pilot contamination poses a critical challenge for channel estimation in dense cell-free (CF) distributed multiple-input multiple-output (CF-DMIMO) wireless networks. State-of-the-art channel estimation schemes require inversion of a high-dimensional channel covariance matrix, which is practically infeasible for dense CF-DMIMO networks owing to the requirement of large storage and high dimensional computational complexity. In this work, we investigate channel estimation problem for a CF-DMIMO network, where both terrestrial and aerial users are jointly supported by distributed access points. We formulate the problem of estimating channel coefficients from the received in-phase/quadrature (I/Q) samples as a non-linear regression problem and propose two deep-learning aided channel estimation schemes for the considered network, namely, deep model-agnostic neural network (DMANN) and deep successive contamination cancellation (DSCC) schemes. Compared to the state-of-the-art channel estimation schemes for CF-DMIMO networks, the proposed schemes (i) tackle the unavoidable pilot contamination issue in dense CF-DMIMO networks while estimating the channel gains for both terrestrial and aerial users; (2) does not require prior knowledge of signal-to-noise ratios; and (3) works well in the presence of non-Gaussian correlated noise. Simulation results demonstrate the effectiveness of the proposed schemes over state-of-the-art channel estimation schemes in various use cases of the CF-DMIMO networks.

Fog radio access network (F-RAN) is a promising architecture that leverages edge computing and caching to improve devices’ latency and quality of service. However, interference, which arises when multiple devices are concurrently scheduled on the same radio resource block (RRB), limits the performance of a dense F-RAN. This article considers a multi-cell F-RAN in which the devices of each small cell receive data from the associated fog access point (F-AP) over the same RRB(s). The F-APs transmit data to the associated devices using rate-splitting multiple access (RSMA) schemes to manage co-channel interference within the small cells efficiently. A transmit power control scheme is proposed to maximize the network’s spectral efficiency (SE) while considering the devices’ hardware impairments (HWIs). The considered transmit power control scheme is an NP-hard problem, which is highly challenging to solve using the legacy optimization approach. To address this challenge, we propose a distributed deep-reinforcement-learning (DRL)-based power allocation (DDPA) scheme that takes the time-varying dynamics of the network and the HWIs of devices into account. Each F-AP in the proposed framework is equipped with a DRL agent that collects signal-to-interference-plus-noise ratio and channel state information from connected devices and adapts the transmit power allocation each scheduling interval. In addition, the ensemble learning framework is exploited to further improve the proposed DDPA scheme’s performance. We use extensive simulations to demonstrate that the DDPA scheme achieves greater SE than contemporary transmit power control schemes. In particular, the proposed DDPA scheme is, especially, suited to scenarios with non-negligible HWIs-induced distortion.

Resource allocation is investigated for offloading computational-intensive tasks in dual-hop mobile edge computing (MEC) system. The envisioned system has both the cooperative access points (APs) with the computing capability and the MEC servers. A user-device (UD), therefore, first uploads a computing task to the nearest AP, and the AP can either locally process the received task or offload to MEC server. To utilize the radio resource blocks (RRBs) in the APs efficiently, we exploit the non-orthogonal multiple access (NOMA) for offloading the tasks from the UDs to the AP(s). In order to investigate the trade-off between latency and energy consumption, this work considers minimizing a weighted-sum that consists of latency and energy consumption, subject to UDs’ rate threshold, tasks’ time-delay, computational frequency scaling, and transmit power allocation constraints. With a joint consideration of all such factors, the problem is NP-hard and its global optimal solution is computationally intractable. A graph-theoretical approach is employed to solve the problem efficiently. Specifically, a novel joint MEC graph-based approach is devised, which solves the scheduling among the UDs, APs, and RRBs, the transmit power control, and the local computational frequency scaling problem(s) jointly. The joint MEC approach achieves near-optimal performance with high computational complexity. To strike a suitable balance between the performance and computational complexity of the resource allocation, a low complexity, yet efficient, pruning graph approach is also devised. The efficiency of the proposed graph-based approaches over several benchmark schemes is verified via extensive simulations.

Distributed machine learning (ML) algorithms are imperative for the next-generation Internet of Things (IoT) networks, thanks to preserving the privacy of users’ data and efficient usage of the communication resources. Federated learning (FL) is a promising distributed ML algorithm where the models are trained at the edge devices over the local data sets, and only the model parameters are shared with the cloud server (CS) to generate global model parameters. Nevertheless, due to the limited battery life of the edge devices, improving the energy-efficiency is a prime concern for FL. In this work, we investigate a resource allocation scheme to reduce the overall energy consumption of FL in the relay-assisted IoT networks. We aim at minimizing the overall energy consumption of IoT devices subject to the FL time constraint. FL time consists of model training computation time and wireless transmission latency. Toward this goal, a joint optimization problem, considering scheduling the IoT devices with the relays, transmit power allocation, and computation frequency allocation, is formulated. Due to the NP-hardness of the joint optimization problem, a global optimal solution is intractable. Therefore, leveraging graph theory, joint near-optimal, and low-complexity suboptimal solutions are proposed. Efficiency of our proposed solutions over several benchmark schemes is verified via extensive simulations. Simulation results show that the proposed near-optimal scheme achieves 6, 4, and 2 times lower energy consumption, respectively, compared to the considered fixed, computation adaptation, and power adaptation schemes. Such an appealing energy efficiency comes at the cost of slightly increased FL time compared to the fixed and computation only adaptation schemes.

Interference management is a key challenge for cellular-connected Internet of Drones (IoD) networks that employ multiple cellular-connected hovering drones for data acquisition in surveillance and monitoring applications. This article proposes a novel resource optimization framework for managing interference in cellular-connected IoD networks. Specifically, the envisioned system divides the set of transmitting drones into distinct drone pairs, where the paired drones simultaneously transmit over the same radio resource blocks (RRBs). Each drone pair is assigned a set of orthogonal RRBs for data transmission, where these RRBs are shared with the terrestrial cellular network as well. An uplink rate-splitting multiple access scheme is employed to mitigate the interdrone interference at the drone pairs, and an RRB pricing method is exploited to control the interference between the aerial and cellular communication links. Our goal is to maximize the uplink capacity of the IoD network while reducing interference over the shared RRBs between the IoD and cellular networks. Toward this goal, a joint optimization of the drones’ transmit power allocation, drone pairing, and RRB scheduling among the drone pairs is presented. In order to obtain an efficient suboptimal solution, an iterative optimization is devised. Particularly, the presented joint optimization problem is decomposed into three subproblems for transmit power allocation, drone pairing and RRB scheduling, and RRB price update. By solving theses subproblems iteratively, a convergent rate-splitting-empowered resource allocation and clustering for interference management (REACT) algorithm is proposed. Extensive simulations are conducted to verify the effectiveness of the proposed REACT algorithm over several benchmark schemes.

In this paper, we leverage reinforcement learning (RL) and cross-layer network coding (CLNC) for efficiently pre-fetching requested contents to the local caches and delivering these contents to requesting users in a downlink fog-radio access network (F-RAN) with device-to-device (D2D) communications. In the considered system, fog access points (F-APs) and cache-enabled D2D (CE-D2D) users are equipped with local caches that alleviate traffic burden at the fronthaul and facilitate rapid delivery of the users’ contents. To this end, the CLNC scheme optimizes the coding decisions, transmission rates, and power levels of both F-APs and CE-D2D users, and RL scheme optimizes caching strategy. A joint content placement and delivery problem is formulated as an optimization problem with a goal to maximize system sum-rate. The problem is an NP-hard problem. To efficiently solve it, we first develop an innovative decentralized CLNC coalition formation (CLNC-CF) switch algorithm to obtain a stable solution for the content delivery problem, where F-APs and CE-D2D users utilize CLNC resource allocation. By considering statistics of channel and users’ content request into account, we then develop a multi-agent RL algorithm for optimizing the content placement at both F-APs and CE-D2D users. Simulation results show that the proposed joint CLNC-CF-RL framework can effectively improve the sum-rate by up to 30%, 60%, and 150%, respectively, compared to: 1) an optimal uncoded algorithm, 2) a standard rate-aware-NC algorithm, and 3) a benchmark classical NC with network-layer optimization.

Multi-objective resource allocation is studied for edge-caching enabled fog-radio access network. Notably, joint maximization of the energy-efficiency (EE) and spectrum-efficiency (SE) and interference management are investigated for distributing contents from the cache-enabled fog access points (F-APs) and cloud base station (CBS) to the user devices (UDs). In our envisioned system, the UDs are grouped into multiple non-overlapping device-clusters based on their locations. A rate-splitting with common message decoding based transmission strategy is applied to enable UDs of each device-cluster to receive data from a suitably selected F-AP and CBS over the same radio resource blocks. To maximize system EE and SE jointly, a multi-objective optimization problem (MOOP) is formulated and it is solved in three stages. At first, by employing the$\epsilon $-constraint method, the MOOP is converted to an EE-SE trade-off optimization problem. Then, by leveraging iterative function evaluation based power control and generalized 3D-resource matching, the EE-SE trade-off optimization problem is solved and a novel resource allocation algorithm is proposed to obtain near-optimal Pareto-front for the proposed MOOP. To reduce the complexity of obtaining near-optimal Pareto-front, a sub-optimal resource allocation algorithm is proposed as well. Finally, a low-complexity algorithm is devised to select a suitable operating EE-SE pair from the obtained Pareto-front. The conducted simulations demonstrate that the proposed resource allocation schemes achieve substantial improvement of system EE and SE over the benchmark schemes.

We investigate adaptive transmission schemes over a hybrid radio frequency (RF)/free space optical (FSO) backhaul network for connecting macro-cell base station with small-cell base station through multiple parallel buffer-aided relay nodes. The proposed adaptive transmission schemes improve delay-throughput trade-off of backhaul network by exploiting the degrees-of-freedom over both RF and FSO links and capturing quality-of-service (QoS) requirements. We study two different system configurations, namely, single-carrier and multi-carrier hybrid RF/FSO systems. Our goal is to maximize the constant data arrival rate to the network such that the total queue occupancy in the network is bounded with certain acceptable queue-length bound violation probability. Towards this goal, we formulate novel optimization problems in order to maximize the end-to-end effective capacity over RF and FSO links of both system configurations. Efficient solutions are derived by employing the Lagrangian optimization technique. Some interesting insights are also revealed by considering special cases on link conditions and QoS requirements. Simulation results provide the following two observations: (i) the proposed adaptive transmission scheme outperforms the conventional switch-over hybrid RF/FSO transmission, especially in clear to moderately harsh weather conditions; and (ii) compared to several non-buffer-aided and buffer-aided relaying with non-adaptive transmission schemes, the proposed adaptive transmission scheme substantially improves the supportable data arrival for the hybrid RF/FSO backhaul network.

We investigate resource optimization for the downlink cloud small cell network, where the baseband unit pool communicates with the buffer-aided small remote radio heads (SRRHs) through free space optical fronthaul, and SRRHs transmit to the user equipments (UEs) by using time division multiplexing-based millimeter wave access links. Our objective is to maximize the supportable aggregate data arrival rate in the network by exploiting the inter-dependence of fronthaul and access links. Toward this objective, we consider maximum acceptable end-to-end queue-length bound violation probability constraints, load-balancing constraints in the access link, fronthaul link selection constraints, and transmit power budget constraints of fronthaul and access links. Since the joint fronthaul and access link optimization is a non-convex and combinatorial problem, we develop an iterative solution by decomposing the original optimization problem into two sub-problems. The first sub-problem optimally obtains fronthaul and access link power allocation and fronthaul link selection by using Lagrangian dual decomposition and canonical one-to-one matching techniques. By employing the Lagrangian dual decomposition and alternating optimization techniques, the second sub-problem obtains near optimal data arrival rate for each UE, UE-SRRH associations, fronthaul rate allocation among the transmitted data for the UEs, and the transmission duration scheduling in millimeter wave access link. An algorithm of polynomial complexity is developed in order to determine the supportable aggregate data arrival rate by considering the statistical quality-of-service requirements, and its convergence is proved. The simulation results depict that the proposed scheme significantly improves the aggregate data arrival rate over several benchmark schemes.

We propose a statistical delay quality-of-service (QoS) aware joint power allocation and relaying link selection scheme for uplink of a multichannel coherent free space optical communication-based fronthaul network. The proposed scheme assigns suitable relays and aggregation nodes to the remote radio heads and allocates transmit power among the orthogonal optical channels. Specifically, the proposed scheme maximizes total end-to-end effective capacity of a fronthaul network subject to certain transmit power budgets at both remote radio heads and relay nodes. The joint power allocation and relaying link selection are formulated as a mixed integer non-linear programing problem. We obtain near optimal solution to such an optimization problem by using Lagrangian dual decomposition and minimum weight matching techniques. Our analysis reveals that in order to maximize the aggregate end-to-end effective capacity, transmit power allocation and relaying link selection should consider both statistical delay-QoS requirements of the transmitted traffics and channel gains of the transmission links. Simulation results demonstrate that our proposed scheme improves statistical delay-QoS aware throughput in presence of atmospheric turbulence fading and pointing error.

Statistical delay quality-of-service (QoS) aware adaptive resource allocation scheme is proposed for a multi-carrier coherent free space optical (FSO) communications based fronthaul network. The proposed resource allocation assigns remote radio heads (RRHs) to the suitable aggregation nodes (ANs) and allocates the transmit power to the orthogonal optical carriers. Specifically, the proposed resource allocation provides delay-QoS at the link layer by maximizing the effective sum capacity of the all the RRHs subject to transmit power budgets at the RRHs and capacity constraint of the wired fronthaul links connecting the ANs with the baseband unit (BBU) pool. The considered resource allocation is formulated as a mixed-integer non-linear programing (MINLP) problem. We use two transmission link optimization techniques, namely, independent link optimization (ILO) and joint link optimization (JLO), in order to solve the proposed MINLP problem. Under both optimization techniques, the proposed MINLP problem is decomposed into two subproblems which are iteratively solved in order to obtain the optical transmit power allocation and assignments of RRHs to the ANs. Our analysis reveals that the optical transmit power allocation and RRH-AN assignments depend on both the atmospheric turbulence fading and delay-QoS requirements. Numerical results demonstrate that the JLO technique achieves significant higher effective capacity (EC) compared to the ILO technique in the strict statistical delay-QoS constraints. However, the EC performance gap between the JLO and ILO techniques is reduced in the loose statistical delay-QoS constraints.

In order to improve the delay quality-of-service aware throughput performance of the optical wireless communications, a joint power allocation and relay selection scheme is proposed for a relay assisted multichannel coherent optical wireless communication system. The proposed joint power allocation and relay selection scheme provides the delay aware quality-of-service guarantee by maximizing the end-to-end effective capacity under the average transmit power constraints at both source as well as relay nodes. The joint power allocation and relay selection problem is formulated as a non-convex and combinatorial optimization problem, and the solution of this optimization problem is obtained using a time-sharing relaxation technique. Subsequently, a fast convergent algorithm is proposed for the joint power allocation and relay selection by considering the delay aware quality-of-service requirements of the services transmitted over the optical wireless communication channels. Numerical results demonstrate that the proposed joint power allocation and relay selection scheme provides the strict statistical delay aware quality-of-service with an improved throughput without increasing the transmit power requirements.

In order to improve the energy efficiency of the optical wireless communications, a joint power allocation and relay selection scheme is proposed for a relay assisted non-line-of-sight optical wireless communication system employing coherent detection with a dual optical channel multiplexing technique. The proposed joint power allocation and relay selection scheme aims to improve energy efficiency of the optical wireless communication system by minimizing the total average transmit power, and provides the delay aware quality-of-service guarantee through maintaining a minimum required effective capacity over the atmospheric turbulence fading channels. The joint power allocation and relay selection problem is formulated as a non-convex and combinatorial optimization problem, and the solution of this optimization problem is obtained using a time-sharing relaxation technique. Subsequently, a fast-convergent algorithm is proposed for the joint power allocation and relay selection by considering the delay aware quality-of-service requirements of the services transmitted over the optical wireless communication channels. Numerical results demonstrate the advantages of the proposed joint power allocation and relay selection scheme in terms of the energy saving for different statistical delay constraints.

We investigate a quality-of-service-aware and energy-aware adaptive power allocation scheme for a point-to-point and line-of-sight optical wireless communication system employing the coherent detection and polarization multiplexing. The proposed power allocation scheme minimizes the average transmit power and provides the delay aware quality-of-service guarantee through maintaining a required effective capacity over the fading channels. The power allocation scheme is formulated as a convex optimization problem, and using the sub-gradient method, a fast convergent algorithm for the optimal power allocation is proposed. Numerical results demonstrate the advantages of the proposed power allocation algorithm in terms of the energy saving for the case of having strict statistical delay constraints.

Effective capacity is defined as the maximum constant traffic arrival rate that a communication channel can support in order to guarantee a certain statistical delay constraint. We investigate the effective capacity performance of the optical wireless communications employing the coherent detection and polarization multiplexing over the Gamma-Gamma turbulence fading channels for two different power adaptation techniques, namely, independent power adaptation and joint power adaptation. In a polarization multiplexing system, independent power adaptation allocates transmit power to a particular channel considering only the signal-to-noise ratio statistics of that particular channel whereas the joint power adaptation allocates transmit power to a particular channel considering the joint signal-to-noise ratio statistics of both channels. Our analysis reveals the superiority of the joint power adaptation technique in order to support the stringent statistical delay constraint services over the strong turbulence fading channels compared to an independent power adaptation technique. However, the numerical results demonstrate that the performance gap between the joint and independent power adaptations gets significantly reduced as the statistical delay constraints become loose and/or the turbulence fading gets weaker.

An adaptive subcarrier intensity modulation employing rectangular quadrature amplitude modulation is investigated for optical wireless communication over the Gamma-Gamma turbulence channels. The adaptive scheme is implemented by adapting the modulation order according to the received signal-to-noise ratio and a pre-defined target bit-error rate requirement. Highly accurate series approximations for the achievable spectral efficiency, average bit-error rate, and outage probability are derived using a series expansion of the modified Bessel function. In addition, asymptotic bit-error rate and outage probability analyses are also presented. Our asymptotic analysis reveals that the diversity order of the considered system depends only on the smaller channel parameter of the Gamma-Gamma turbulence.

We investigate a variable-rate, constant-power adaptive subcarrier intensity modulation employing M-ary phase shift keying and rectangular quadrature amplitude modulation for optical wireless communication over the Gamma-Gamma turbulence channels. The adaptive schemes offer efficient utilization of optical wireless communication channel capacity by adapting the modulation order according to the received signal-to-noise ratio and a pre-defined target bit-error rate requirement. Novel closed-form series solutions are presented for the achievable spectral efficiency, average bit-error rate, and outage probability using a series expansion approach of the modified Bessel function. In addition, asymptotic bit-error rate and outage probability analyses are presented. Our asymptotic bit-error rate analysis shows that the diversity order of both non-adaptive and adaptive systems depends only on the smaller channel parameter of the Gamma-Gamma turbulence. Numerical results demonstrate high accuracy of our series solutions with finite number of terms and improved spectral efficiency achieved by the adaptive systems without increasing the transmitter power or sacrificing bit-error rate requirements.

Subcarrier intensity modulation is analyzed for general order rectangular quadrature amplitude modulation over the Gamma-Gamma turbulence channels. Closed-form expressions of average symbol error rate are derived using a series expansion of the modified Bessel function and a moment generating function approach. Truncation error analysis and asymptotic error rate analysis are also presented. Numerical results demonstrate that our closed-form expressions are highly accurate.