We propose a resource allocation optimisation framework that integrates the O-RAN architecture with out-band Integrated Access and Backhaul (IAB). Using mixed-integer linear programming (MILP), we coordinate multiple control functions across O-RAN to achieve global optimality. The MILP model jointly optimises: (i) radio-resource allocations among users (power, numerology, and resource block) at the DU level to satisfy diverse data rate and latency requirements in frequency-selective channels; (ii) carrier bandwidth partitioning among numerology based Bandwidth Parts (BWPs) at the CU control-plane level as a longer-term policy; and (iii) wireless backhaul routing over mmWave links from IAB Donors to IAB Nodes at the CU user-plane level. Our framework leverages mmWave frequency bands for backhaul despite their path-loss and penetration-loss challenges. The main objectives are to minimise downlink transmit power and co-channel interference (CCI) while maximising the number of simultaneously served users. Results show that integrating IAB reduces average total transmit power by 35%. We also compare flexible and non-flexible numerology allocation, with IAB and flexible numerology demonstrating significant improvements in both CCI and downlink energy efficiency. For faster implementation, we develop two hybrid genetic algorithms that converge rapidly, delivering near-optimal solutions far more quickly than the MILP model. This makes them well-suited to real-time resource allocation and dynamic network management in O-RAN with out-of-band IAB.

Quantum data centres (QDCs) could overcome the scalability challenges of modern quantum computers. Single-processor monolithic quantum computers are affected by increased cross talk and difficulty of implementing gates when the number of qubits is increased. In a QDC, multiple quantum processing units (QPUs) are linked together over short distances, allowing the total number of computational qubits to be increased without increasing the number of qubits on any one processor. In doing so, the error incurred by operations at each QPU can be kept small, however additional noise will be added to the system due to the latency cost and errors incurred during inter-QPU entanglement distribution. We investigate the relative impact of these different types of noise using a classically simulated QDC with two QPUs and compare the robustness to noise of the two main ways of implementing remote gates, cat-comm and TP-comm. We find that considering the quantity of gates or inter-QPU entangled links is often inadequate to predict the output fidelity from a quantum circuit and infer that an improved understanding of error propagation during distributed quantum circuits may represent a significant optimisation opportunity for compilation.

Nano-networks are envisioned to allow several nanoscale devices to transmit and receive information. One form of such networks is electromagnetic nano-networks working within the THz band. However, high overall path loss and molecular noise experienced in the THz band, as well as limited energy storage capabilities, restrict the communication range of nano-nodes and impact network efficiency. Therefore, optimizing the nano-network resources is necessary. In this paper, we present an optimization framework employing mixed-integer linear programming (MILP) to determine the most energy-efficient routing, bandwidth, and sub-band allocation for each nano-node in an electromagnetic nano-network operating within the THz band. Our model was tested for two different scenarios related to the priority of energy saving. We also compare our proposed optimal bandwidth, routing, and sub-band allocation against less complex designs where sub-bands with fixed bandwidth are employed in nano-nodes. Furthermore, we investigate the impact of nano-node’s processing and sensing units on the overall network energy consumption and the associated optimal bandwidth allocation and routing strategy. Given the considered parameters and the model’s assumptions, the results show that using the optimal multi-hops paths with higher bandwidth allocation for the considered sub-bands can be more energy efficient than sending the traffic using a single hop and lower bandwidths, especially when the transmission power dominates in the nano-network. On the other hand, when the processing and sensing unit’s energy consumption is dominant, then single hop schemes with lower bandwidth allocation result in the minimum network energy consumption. Finally, we discuss the limitations of the proposed energy-efficient strategies and point toward possible future research directions to which the model can be adapted.

Continuous-variable (CV) quantum key distribution (QKD) in optical multiple-antenna settings can be useful in harsh channel conditions despite the presence of cross-talk between channels. This is, for instance, relevant to satellite-based QKD with multiple transmitting antennas on the satellite and multiple ground stations. In such a case, each transmitter independently exchanges data with its intended receiver, where the received signal may contain components from other transmitters. In particular, for a 2x2 multiple-input multiple-output (MIMO) setting with Gaussian encoding and heterodyne detection, preliminary key rate analysis has recently been presented and compared with single-mode and multiplexed CV-QKD scenarios. In this work, we study the effect of different MIMO channels on the performance by accounting for the cross-talk parameter in slow fading channels.

Electromagnetic nano-networks operating in the THz band offer a promising solution for enabling communication among many nanoscale devices. However, the inherent limitations of nano-nodes, such as restricted energy and processing resources and short communication range, pose significant challenges for efficient data transmission. While prior research has explored Reinforcement Learning (RL) for optimising traffic routing in electromagnetic nano-networks, this paper proposes a novel approach that jointly optimises routing and sub-channel bandwidth allocation to minimise network energy consumption using RL. We leverage the Q-learning algorithm to develop a dynamic single-hop or multi-hop routing scheme that considers each node's location, energy storage capability, and the available sub-channel bandwidth. Our model formulates a reward function that balances these multiple objectives and enables the selection of optimal transmission policies for each nano-node. Our findings suggest carefully choosing the number of hops and increasing bandwidth in sub-channels can lead to substantial energy savings in nano-networks.

This study presents a novel power allocation optimisation strategy that does not rely on traditional power constraints. Unlike previous works in the literature, we introduce a ratio between the allocated and requested data rates and incorporate this ratio into the reward function of our deep reinforcement learning algorithm. The highest reward of 1 is achieved when the allocated and requested data rates are equal. Additionally, we jointly optimise power and numerology allocation, considering the users' delay and data rate requirements. Any numerology can be allocated to users as long as their requirements are satisfied. This approach enables users to be allocated optimum numerology and transmit power. By addressing the challenge posed by greedy users, our approach enhances the flexibility and performance of the power and numerology allocation process.

We investigate quantum key distribution (QKD) in optical multiple-input-multiple-output (MIMO) settings. Such settings can prove useful in dealing with harsh channel conditions as in, e.g., satellite-based QKD. We study a $2\times2$ setting for continuous variable (CV) QKD with Gaussian encoding and heterodyne detection and reverse reconciliation. We present our key rate analysis for this system and compare it with single-mode and multiplexed CV QKD scenarios. We show that we can achieve multiplexing gain using multiple transmitters and receivers even if there is some crosstalk between the two channels. In certain cases, when there is nonzero correlated excess noise in the two received signals, we can even surpass the multiplexing gain.

Achieving an end-to-end low-latency for computations offloading, in Mobile Edge Computing (MEC) systems, is still a critical design problem. This is because the offloading of computational tasks via the MEC servers entails the use of uplink (UL) and downlink (DL) radio links that are usually assumed to be coupled to a single base station (BS). However, for heterogeneous networks, a new architectural paradigm whereby UL and DL are not associated with the same BS is proposed and seen to provide gains in network throughput due to the improved UL performance. Motivated by such gains, and by using typical results from stochastic geometry, we formulate the offloading latency for the MEC-based scheme with decoupled UL/DL association, or decoupled access, and compare its performance to the conventional coupled access scheme. Despite the backhaul delay necessary for the communication between the two serving BSs in UL and DL, the offloading scheme with decoupled access is still capable of providing a fairly lower offloading latency compared to the conventional offloading scheme with coupled access.

Network function virtualization (NFV) and content caching are two promising technologies that hold great potential for network operators and designers. This paper optimizes the deployment of NFV and content caching in 5G networks and focuses on the associated power consumption savings. In addition, it introduces an approach to combine content caching with NFV in one integrated architecture for energy aware 5G networks. A mixed integer linear programming (MILP) model has been developed to minimize the total power consumption by jointly optimizing the cache size, virtual machine (VM) workload, and the locations of both cache nodes and VMs. The results were investigated under the impact of core network virtual machines (CNVMs) inter-traffic. The result show that the optical line terminal (OLT) access network nodes are the optimum location for content caching and for hosting VMs during busy times of the day whilst IP over WDM core network nodes are the optimum locations for caching and VM placement during off-peak time. Furthermore, the results reveal that a virtualization-only approach is better than a caching-only approach for video streaming services where the virtualization-only approach compared to caching-only approach, achieves a maximum power saving of 7 no CNVMs inter-traffic is considered and 6 inter-traffic at 10 integrated approach has a maximum power saving of 15 without CNVMs inter-traffic compared to the virtualization-only approach, and it achieves a maximum power saving of 21 inter-traffic and 20 compared with the caching-only approach. In order to validate the MILP models and achieve real-time operation in our approaches, a heuristic was developed.

The Internet of Things (IoT) can support a significant number of services including those in smart homes and the automation of industries and public utilities. However, the growth of these deployments has posed a significant challenge especially in terms of how to build such deployments in a highly resilient manner. The IoT devices are prone to unpredicted failures and cyber-attacks, i.e. various types of damage, unreliable wireless connections, limited transmission power, computing ability, and storage space. Thus resilience is essential in IoT networks and in the services they support. In this paper, we introduce a new approach to resilience in IoT service embedding, based on traffic splitting. Our study assesses the power consumption associated with the services embedded and the data delivery time. The results are compared to recent approaches in resilience including redundancy and replication approaches. We constructed an optimization model whose goal is to determine the optimum physical resources to be used to embed the IoT virtual topology, where the latter is derived from a business process (BP). The embedding process makes use of the service-oriented architecture (SOA) paradigm. The physical resources of interest include IoT links and devices. The model made use of mixed integer linear programming (MILP) with an objective function that aimed to minimize both the total power consumption and the traffic latency. The optimization results show that the power consumption is reduced and the data delivery time is reduced in the service embedding approach where the proposed traffic splitting approach is employed resulting in the selection of energy efficient nodes and routes in the IoT network. Our methods resulted in up to 35% power saving compared to current methods and reduced the average traffic latency by up to 37% by selecting energy-efficient nodes and routes in IoT networks and by optimizing traffic flow to minimize latency.

Having a cognitive and self-optimizing network that proactively adapts not only to channel conditions, but also according to its users' needs can be one of the highest forthcoming priorities of future 6G Heterogeneous Networks (HetNets). In this paper, we introduce an interdisciplinary approach linking the concepts of e-healthcare, priority, big data analytics (BDA) and radio resource optimization in a multi-tier 5G network. We employ three machine learning (ML) algorithms, namely, naïve Bayesian (NB) classifier, logistic regression (LR), and decision tree (DT), working as an ensemble system to analyze historical medical records of stroke out-patients (OPs) and readings from body-attached internet-of-things (IoT) sensors to predict the likelihood of an imminent stroke. We convert the stroke likelihood into a risk factor functioning as a priority in a mixed integer linear programming (MILP) optimization model. Hence, the task is to optimally allocate physical resource blocks (PRBs) to HetNet users while prioritizing OPs by granting them high gain PRBs according to the severity of their medical state. Thus, empowering the OPs to send their critical data to their healthcare provider with minimized delay. To that end, two optimization approaches are proposed, a weighted sum rate maximization (WSRMax) approach and a proportional fairness (PF) approach. The proposed approaches increased the OPs' average signal to interference plus noise (SINR) by 57% and 95%, respectively. The WSRMax approach increased the system's total SINR to a level higher than that of the PF approach, nevertheless, the PF approach yielded higher SINRs for the OPs, better fairness and a lower margin of error.

The Internet of Things (IoT) is the cornerstone of smart applications such as smart buildings, smart factories, home automation, and healthcare automation. These smart applications express their demands in terms of high-level requests. Application requests in service-oriented IoT architectures are translated into a business process (BP) workflow. In this paper, we model such a BP as a virtual network containing a set of virtual nodes and links connected in a specific topology. These virtual nodes represent the requested processing and locations where sensing and/or actuation are needed. The virtual links capture the requested communication requirements between nodes. We introduce a framework, optimized using mixed integer linear programming (MILP), that embeds the BPs from the virtual layer into a lower-level implementation at the IoT physical layer. We formulate the problem of finding the optimal set of IoT nodes and links to embed BPs into the IoT layer considering three objective functions: i) minimizing network and processing power consumption only, ii) minimizing mean traffic latency only, iii) minimizing a weighted combination of power consumption and traffic latency to study the trade-off between minimizing the power consumption and minimizing the traffic latency. We have established, as reference, a scenario where service embedding is performed to meet all the demands with no consideration to power consumption or latency. Compared to this reference scenario, our results indicate that the power savings achieved by our energy efficient embedding scenario is 42% compared with the energy-latency unaware service embedding (ELUSE) reference scenario, while our low latency embedding reduced the traffic latency by an average of 47% compared to the ELUSE scenario. Our combined energy efficient low latency service embedding approach achieved high optimality by jointly realizing 91% of the power and latency reductions obtained under the single objective of minimizing power consumption or latency.

In this paper, network function virtualization (NFV) is identified as a promising key technology, which can contribute to energy-efficiency improvement in 5G networks. An optical network supported architecture is proposed and investigated in this paper to provide the wired infrastructure needed in 5G networks and to support NFV toward an energy efficient 5G network. In this paper, the mobile core network functions, as well as baseband function, are virtualized and provided as VMs. The impact of the total number of active users in the network, backhaul/fronthaul configurations, and VM inter-traffic are investigated. A mixed integer linear programming (MILP) optimization model is developed with the objective of minimizing the total power consumption by optimizing the VMs location and VMs servers' utilization. The MILP model results show that virtualization can result in up to 38% (average 34%) energy saving. The results also reveal how the total number of active users affects the baseband virtual machines (BBUVMs) optimal distribution whilst the core network virtual machines (CNVMs) distribution is affected mainly by the inter-traffic between the VMs. For real-time implementation, two heuristics are developed, an energy efficient NFV without CNVMs inter-traffic (EENFVnoITr) heuristic and an energy efficient NFV with CNVMs inter-traffic (EENFVwithITr) heuristic, both produce comparable results to the optimal MILP results. Finally, a genetic algorithm is developed for further verification of the results.

In this paper, we introduce machine learning approaches that are used to prioritize outpatients (OP) according to their current health state, resulting in self-optimizing heterogeneous networks (HetNet) that intelligently adapt according to users' needs. We use a naïve Bayesian classifier to analyze data acquired from OPs' medical records, alongside data from medical Internet of Things (IoT) sensors that provide the current state of the OP. We use this machine learning algorithm to calculate the likelihood of a life-threatening medical condition, in this case an imminent stroke. An OP is assigned high-powered resource blocks (RBs) according to the seriousness of their current health state, enabling them to remain connected and send their critical data to the designated medical facility with minimal delay. Using a mixed integer linear programming formulation (MILP), we present two approaches to optimizing the uplink side of a HetNet in terms of user-RB assignment: a Weighted Sum Rate Maximization (WSRMax) approach and a Proportional Fairness (PF) approach. Using these approaches, we illustrate the utility of the proposed system in terms of providing reliable connectivity to medical IoT sensors, enabling the OPs to maintain the quality and speed of their connection. Moreover, we demonstrate how system response can change according to alterations in the OPs' medical conditions.

Big data analytics is one of the state-of-the-art tools to optimize networks and transform them from merely being a blind tube that conveys data, into a cognitive, conscious, and self-optimizing entity that can intelligently adapt according to the needs of its users. This, in fact, can be regarded as one of the highest forthcoming priorities of future networks. In this paper, we propose a system for Out-Patient (OP) centric Long Term Evolution-Advanced (LTE-A) network optimization. Big data harvested from the OPs' medical records, along with current readings from their body-connected medical IoT sensors are processed and analyzed to predict the likelihood of a life-threatening medical condition, for instance, an imminent stroke. This prediction is used to ensure that the OP is assigned an optimal LTE-A Physical Resource Blocks (PRBs) to transmit their critical data to their healthcare provider with minimal delay. To the best of our knowledge, this is the first time big data analytics are utilized to optimize a cellular network in an OP-conscious manner. The PRBs assignment is optimized using Mixed Integer Linear Programming (MILP) and a real-time heuristic. Two approaches are proposed, the Weighted Sum Rate Maximization (WSRMax) approach and the Proportional Fairness (PF) approach. The approaches increased the OPs' average SINR by 26.6% and 40.5%, respectively. The WSRMax approach increased the system's total SINR to a level higher than that of the PF approach, however, the PF approach reported higher SINRs for the OPs, better fairness and a lower margin of error.

In this paper, an energy efficient IoT virtualization framework with peer-to-peer (P2P) networking and edge processing is proposed. In this network, the IoT task processing requests are served by peers. IoT objects and relays that host virtual machines (VMs) represents the peers in the proposed P2P network. We have considered three scenarios to investigate the saving in power consumption and the system capabilities in terms of task processing. The first scenario is a 'relays only' scenario, where the task requests are processed using relays only. The second scenario is an 'objects only' scenario, where the task requests are processed using the IoT objects only. The last scenario is a hybrid scenario, where the task requests are processed using both IoT objects and VMs. We have developed a mixed integer linear programming (MILP) model to maximize the number of processing tasks served by the system, and minimize the total power consumed by the IoT network. Based on the MILP model principles, we developed an energy efficient virtualized IoT P2P networks heuristic (EEVIPN). Our results show that the hybrid scenario serves up to 77% (57% on average) processing task requests, but with higher energy consumption compared to the other scenarios. The relays only scenario serves 74% (57% on average) of the processing task requests with 8% saving in power consumption compared to the hybrid scenario. In contrast, 28% (22% on average) of task requests can be handled by the objects only scenario with up to 62% power saving compared to the hybrid scenario.

In this work, we investigate the energy efficiency of placing virtual machines (VMs) in geo-distributed data centers taking into account inter-VM traffic in addition to users traffic. The problem of VMs placement is formularized as a mixed integer linear programming (MILP) model with an objective to minimize the network and cloud power consumption taking into consideration cooperation traffic between different VMs and synchronization traffic between replicas of the same VM in addition to the download traffic from VMs to users. The model results show that the number of VMs replicas across geo-distributed clouds is limited by the existence of inter-VM traffic in the core network. The total power consumption can potentially increase by a factor of 39 if inter-VM traffic is not taken into consideration when optimizing the placement of VMs.

Fog computing offers a scalable and effective solution to overcome the increasing processing and networking demands of Internet of Thing (IoT) devices. In this paper, we investigate the use of fog computing for health monitoring applications. We consider a heart monitoring application where patients send their 30 minute recording of Electrocardiogram (ECG) signal for processing, analysis, and decision making at fog processing units within the time constraint recommended by the American Heart Association (AHA) to save heart patients when an abnormality in the ECG signal is detected. The locations of the processing servers are optimized so that the energy consumption of both the processing and networking equipment are minimised. The results show that processing the ECG signal at fog processing units yields a total energy consumption saving of up to 68% compared to processing the at the central cloud.

Currently, the world is witnessing a mounting avalanche of data due to the increasing number of mobile network subscribers, Internet websites, and online services. This trend is continuing to develop in a quick and diverse manner in the form of big data. Big data analytics can process large amounts of raw data and extract useful, smaller-sized information, which can be used by different parties to make reliable decisions. In this paper, we conduct a survey on the role that big data analytics can play in the design of data communication networks. Integrating the latest advances that employ big data analytics with the networks control/traffic layers might be the best way to build robust data communication networks with refined performance and intelligent features. First, the survey starts with the introduction of the big data basic concepts, framework, and characteristics. Second, we illustrate the main network design cycle employing big data analytics. This cycle represents the umbrella concept that unifies the surveyed topics. Third, there is a detailed review of the current academic and industrial efforts toward network design using big data analytics. Forth, we identify the challenges confronting the utilization of big data analytics in network design. Finally, we highlight several future research directions. To the best of our knowledge, this is the first survey that addresses the use of big data analytics techniques for the design of a broad range of networks.

The Internet of Things (IoT) is anticipated to participate in performing diverse and complex tasks in the near future. IoT objects capable of handling multiple sensing and actuating functions will be the corner stone of future IoT systems in smart cities. In this paper, we present an energy efficient service embedding framework in IoT network by using mixed integer linear programming (MILP). This framework addresses a set of metrics such as scalability, flexible resource allocation, cost reduction, and efficient use of resources. We consider the event-driven paradigm of Service Oriented Architecture (SOA) in our framework in order to provide service abstraction of basic services which can be composed into complex services and exploited by the upper application layer. The results show that our optimized network can save an average of 27% and 36% of the processing and network power consumption, respectively, compared to an energy unaware service embedding scheme.