Distributed quantum computing (DQC) is being actively investigated as a means of scaling the number of qubits across multiple connected quantum devices. This includes quantum circuit compilation and execution management on multiple quantum devices in the network. The latter aspect is very challenging because, while reducing the makespan of job batches remains a relevant objective, novel quantum-specific constraints must be considered, including QPU utilization, non-local gate rate, and the latency associated with queued DQC jobs. In this work, a range of scheduling strategies is proposed, simulated, and evaluated, including heuristics that prioritize resource maximization for QPU utilization, node selection based on heterogeneous network connectivity, asynchronous node release upon job completion, and a scheduling strategy based on reinforcement learning with proximal policy optimization. These approaches are benchmarked against traditional FIFO and LIST schedulers under varying DQC job types and network conditions for the allocation of DQC jobs to devices within a network.

In this paper, a novel Deep Q-Network (DQN) based scheduling method to optimize delay time and fairness among entanglement requests in quantum repeater networks is proposed. The scheduling of requests determines which pairs of end nodes should be entangled during the current time slot, while other pairs are placed in a queue for future slots. However, existing research on quantum networking often relies on simple statistical models to capture the behavior of quantum hardware, such as the failure rate of establishing entanglement. Moreover, current quantum simulators do not support network behaviors, including handling, pending, and dropping requests. To bridge the gap between quantum deployments and network behaviors, in this paper a dynamic network model is presented, encompassing quantum simulations, random topologies, and user modeling. The DQN based scheduling scheme allows us to balance the conflicting objectives of minimizing delay time and maximizing fairness among these entanglement requests. The proposed technique was evaluated using simulations, with results showing that the proposed DQN achieves higher performance compared to Greedy, Proportional fair and FIFO scheduling schemes.

In this paper, a joint optimal allocation of transmit power at the source and jamming power at the destination is proposed to maximize the average secrecy energy efficiency (SEE) of a wireless network within a finite time duration. The destination transmits the jamming signal to improve secrecy by utilizing full-duplex capability. The source and destination both have energy harvesting (EH) capability with limited battery capacity. Due to the Markov nature of the system, the problem is formulated as a finite-horizon reinforcement learning (RL) problem. We propose the finite-horizon joint power allocation (FHJPA) algorithm for the finite-horizon RL problem and compare it with a low-complexity greedy algorithm (GA). An infinite-horizon joint power allocation (IHJPA) algorithm is also proposed for the corresponding infinite-horizon problem. A comparative analysis of these algorithms is carried out in terms of SEE, expected total transmitted secure bits, and computational complexity. The results show that the FHJPA algorithm outperforms the GA and IHJPA algorithms due to its appropriate modelling in finite horizon transmission. When the source node battery has sufficient energy, the GA can yield performance close to the FHJPA algorithm despite its low-complexity. When the transmission time horizon increases, the accuracy of the infinite-horizon model improves, resulting in a reduced performance gap between FHJPA and IHJPA algorithms. The computational time comparison shows that the FHJPA algorithm takes 16.6 percent less time than the IHJPA algorithm.

Unmanned Aerial Vehicles (UAVs) are becoming an indispensable tool in civilian and military data-gathering and reconnaissance tasks, due to their ability to freely fly over a mission area and collect data. In this paper, we experimentally demonstrate how off-the-shelf Software-Defined Radio receivers can be used as part of a UAV payload, to create a cost-effective system to locate the origin of a radio transmitter. A uniform linear array of antennas is used to measure the angle-of-arrival of the signal, which is then combined with the GPS coordinates of the UAV and processed with an Unscented Kalman Filter algorithm to estimate the location of the transmitter. We demonstrate this system in field conditions, and show that the system can accurately estimate the angle-of-arrival. We demonstrate that a low-power handset can be located to within 100m from over 1.5km away within several minutes of flight. While our experiment is based around a search and rescue scenario, this localisation approach has wider applicability for wireless communications applications.

Unmanned aerial vehicles (UAVs) can extend the coverage of wireless networks due to their high mobility and the favorable radio propagation characteristics. This paper studies the trajectory optimization of UAV that are acting as radio relays. The optimization is based on predicted user locations (UTO-PUL) to assist communication to the ground users who are unable to get coverage from the base station (BS). The existing work on trajectory design has considered several optimization approaches as well as reinforcement learning (RL) algorithms. All the algorithm takes into consideration the existing state of the network such as the channel conditions, initial positions and computes the destination of the UAV based on it. The proposed algorithm is designed to also consider the predicted user mobility of the future instances. The objective is to ensure the ground users are connected to the BS. The proposed UTO-PUL algorithm's performance is evaluated using simulations in a scenario with challenging terrain, where the proposed algorithm reduced the probability of users having no coverage by between 45% to 85% compared to non-predictive approaches, and achieved gains in median downlink signal power of 14 dB compared with a deep reinforcement learning (DRL) algorithm.

In addition to high coverage and capacity, localization capabilities are becoming increasingly important for WLAN access points as enabler for additional services. In this paper the joint optimization of coverage and localization is investigated using a switched directional antenna array configuration. It is shown that switched directional antenna arrays can significantly outperform omni-directional antenna arrays in both coverage and angle estimation. In addition, it is shown that optimizing the antenna array for coverage also provides near-optimal localization performance. This is of great practical importance, as coverage optimization does not require manual calibration based on ground truth, and a good joint solution can be autoconfigured easily.

The accurate localisation and tracking of objects is crucial in many domains. In this paper, we focus on location tracking in wireless networks. Reliable localisation will be essential for self-driving, future factories, and beamforming in 5G deployments. Time-of-arrival (TOA) based localisation systems use synchronised nodes to receive radio signals sent by transmitters at the object to be located. The time it takes for the signal to propagate in a straight line to the receiver is used to trilaterate the position of the transmitter. However, the correct TOA can be difficult to identify due to multiple reflected copies of the same signal arriving at different times. This paper presents a bidirectional recurrent neural network (BiRNN) for estimating the TOA from the channel impulse response (CIR) of the signal, and a multilayer perceptron (MLP) to trilaterate the position of the transmitter from the TOA. The BiRNN and MLP are trained using measured CIR data, and outperform the conventional approaches for TOA estimation and trilateration.

Wi-Fi technology is continuously evolving to keep the pace with the fast-increasing demand for new digital services and applications. In particular, Wi-Fi 6 is becoming widely available in residential and enterprise scenarios supporting up to eight spatial streams. At the same time, large antenna arrays have already shown their ability and benefits when serving a large number of devices simultaneously. Since enterprise owners are already upgrading their indoor private networks with the next generation of Wi-Fi products, it becomes crucial to understand the capacity limits and the benefits associated to different deployment strategies and large antenna array configurations. The scope of this paper is to provide concrete insights to internet service providers and enterprise owners on how to upgrade, deploy, and efficiently operate their networks with next-generation Wi-Fi access points, showing their potential performance improvements and providing a set of clear guidelines.

In this paper, we investigate the use of highly directional, hybrid antenna arrays in residential Wi-Fi mesh deployments to further improve their performance in challenging conditions where high wall losses are encountered. We also formulate a metric to quantify the quality of the Wi-Fi coverage, and present a deployment procedure to automatically select extender AP locations to maximise such metric. Using the proposed deployment procedure, the performance of highly directional, hybrid antenna arrays in residential Wi-Fi mesh deployments is evaluated through simulations. Compared with the benchmark of omni-directional antennas, we show that using the proposed array increases the areas achieving peak throughput by more than 3 times.

IEEE 802.11ac enables channel bonding, where several channels form wide bandwidths up to 160 MHz in a very flexible manner, and can be used to increase the transmission rates. This Dynamic Channel Bonding (DCB) is highly dependent on the channel allocation, because of the pre- defined bonding patterns. In this paper we propose a channel allocation method that considers the constraints imposed by bonding, and therefore enables the use of wider bandwidths more often resulting also in higher throughput. A simulation study was performed for two typical scenarios: enterprise and residential. The results show significant gains of up to 38% wider bandwidth in use and 46% increase of median throughput for the proposed allocation scheme when compared with the baseline approach.

With the ever increasing cell densities in wireless networks, it is desired to enable more self-X features, such as self-configuration. Setting the network parameters in an autonomous manner is not only more time efficient but also increases network performance and reduces the probability of a human error. Among numerous network settings, one of the key parameters that requires such autonomous configuration is the cell ID (CID). It is used during fundamental procedures, such as network access, decoding, or handover, and therefore, its configuration is crucial for network operation. A complete CID management framework together with a centralized method for CID assignment is presented in this paper. It is not only applicable to multiple mobile standards but is also compatible with multi-vendor equipment, since it is based on the TR-069 management protocol. The proposed approach mitigates CID conflicts when there is a high level of reuse. Moreover, in the 4G case it also prevents neighbors from using different CID but with the same reference signal pattern, which avoids significant interference. The proposed method is evaluated in an enterprise small cell scenario, and in comparison with the baseline third generation partnership project approach, significant reductions of assignment conflicts are demonstrated.

Future indoor networks need to provide broadband services either by increasing bandwidth, using higher order modulations and high spatial reuse. To this end, ultra-dense Wi-Fi networks need to be deployed. This work discusses the challenges of ultra-dense Wi-Fi networks, where inter-AP distances are 5 m or less, and discuss necessary requirements to maximize the high spatial reuse of radio resources through the use of adaptive Clear Channel Assessment (CCA) thresholds and downward facing directional antennas. We also examine the significant impact of uplink transmissions, and present the necessary uplink transmit power control in ultra-dense deployments to avoid the interference coming from hidden nodes in neighboring APs. With the proposed techniques and antennas in place, we show that the limitations placed by high contention and interference in ultra-dense deployments can be overcome to provide over 8 χ gains in downlink throughput when compared with APs with omni-directional and unadjusted CCA thresholds.

The use of small cells is a promising way of providing the required capacity in future cellular networks through densification. Self-organizing network (SON) techniques are required in small cells, particularly in femtocells where plug-and-play user deployments are used. However, these techniques have tended to be implemented by vendors with proprietary algorithms that use low-level UE measurements which are not easily accessible to network operators. This makes the management of femtocells in a multi-vendor environment difficult. In this paper, we present a centralized SON technique to perform load balancing, coverage optimization and manage sleep modes that utilizes high-level measurements made at the femtocells which are easily available through the widely used TR-069 device management protocol standard. This allows network operators the flexibility of implementing and coordinating the SON techniques in multi-vendor femtocell deployments. We show that, despite the limited amount of information used, the proposed solution is able to effectively optimize the coverage in order to balance load and manage sleep modes whilst ensuring that no coverage gaps occur.

This study examines the trade-offs related to the problem of coverage auto-configuration of open access small cells (SC) that (i) are on-demand deployed in the macro-cells (MC) of a W-CDMA network, and (ii) operate in the same frequency channel as the hosting/neighboring MCs. The goal is to identify stable and practically detectable equilibrium states whose achievement can represent an optimality criterion for implementing automatic coverage adaptation of the SCs. It is shown that, on the downlink (DL), the offloading of MC hotspot users by a nearby SC improves the DL signal-to-interference-and-noise ratio (SINR) and the DL effective throughput of the MC users at the expense of the SC users. Similarly on the uplink (UL), the maximum achievable UL SINR of the SC (MC) users decreases (increases) with a growing SC coverage, but a distinct equilibrium state of the MC-vs-SC trade-off defined on the basis of the spectral radius of a network information matrix can be observed. However, the DL and UL equilibria do not generally coincide unless the MC and SC performance requirements are identical. This fact complicates autonomous optimum partitioning of the SC power budget between data transmissions and coverage-defining pilot signals. Numerical simulations are based on a 3GPP-compliant system model.

Macrocellular networks with frequency reuse across cells suffer from high interference at the cell boundaries, resulting in a very uneven distribution of user throughput. Recently it has been shown that for multi-carrier networks a horizontal angular offset configuration for the second carrier can significantly improve the performance and call drop rate of the network. In this paper this concept is extended to the vertical dimension by providing additional cells with higher downtilt while retaining the naturally resulting interference avoidance properties. Simulations for UMTS HSDPA show that with the additional cell layer with vertical downtilt can increase the average user throughput by 126% and the throughput of the worst 5th percentile by 205%, compared to a traditional 3 sector macrocell configuration. It is shown that the proposed cell configuration also outperforms conventional 6 sector configurations with the same number of antennas and cells per site. Furthermore, it is shown that the addition of the second cell layer does not significantly increase the handover failure rates over conventional cell configurations. The concept scales well to higher sectorizations.

The wide adoption of smartphones has resulted in an exponential increase in the demand for wireless data. To address this problem, operators have started deploying large numbers of small cells. In order to operate such small cell network cost-effectively they need to be able to intelligently optimize their configuration, which can be achieved by applying machine learning techniques such as genetic programming. The use of genetic programming has previously been used to derive joint coverage algorithms for a group of enterprise femtocells. However, the evolution of the algorithms was performed in an offline manner, on a pre-defined simulation model of the deployment scenario. In this paper, an approach to perform the evolution in an online manner using an automated model building process is presented. The model building process uses network traces as inputs to create a hierarchical Markov model that is shown to be able to capture the behavior of the femtocell network well. It is shown that the resulting environment model can effectively drive the on-line evolution of coverage optimization algorithms.

Macrocellular networks with frequency reuse across sectors of the same eNodeB and neighbouring eNodeBs suffer from high interference at the cell boundaries, resulting in an uneven distribution of User Equipment (UE) throughputs. Recently, it has been shown that for two-carrier HSDPA networks a horizontal sector offset configuration for the second carrier can significantly enhance network performance at the cost of an increased number of handovers. In this paper, this sector offset configuration is extended to LTE through a novel approach, which allows deploying offset antennas in eNodeBs without significantly increasing the number of handovers. The proposed sector offset configuration is compatible with current channel dependent schedulers, and provides significant gains. Simulation results show that for LTE the proposed sector offset configuration can increase the average UE throughput and 5%-tile UE throughput by up to 22% and 32%, respectively, while slightly increasing the number of handovers compared to the traditional eNodeB configuration. Moreover, results show that the new configuration can significantly reduce the handover failure rate by up to 69%.

Macrocellular networks with frequency reuse across cells suffer from high interference at the cell boundaries, resulting in a very uneven distribution of user throughput. In this paper a sector offset configuration for multi-carrier networks is proposed that can significantly improve the cell edge performance without reducing the frequency re-use factor. Simulations for UMTS HSDPA show that for a 3 sector base station transmitting on two carriers the proposed approach can increase the average user throughput by 38% and the throughput of the worst 5th percentile by 94%, with a small increase in the total number of handovers. In addition it is shown that the new configuration results in a significantly reduced handover failure rate.

Radio coverage optimization is a key element in improving the overall performance of femtocell cellular networks. Owing to the increased size and complexity of femtocell networks, there is an imperative need to develop decentralized coverage optimization algorithms, which is a highly challenging task as these algorithms have to work without global information and coordinated central control of network nodes. In this paper, we consider a deployment scenario of a group of N femtocells in an enterprise environment and propose a decentralized algorithm for joint femtocell coverage area optimization. The algorithm updates the femtocell's pilot Tx power only and serves to balance the user load amongst the collocated femtocells as well as minimizing the coverage holes and pilot Tx power. Compared to the fixed pilot Tx power allocation, the algorithm introduces an improvement of approximately 18% in terms of supported user traffic, in addition to a significant reduction of pilot leakage to neighboring cells.

Femtocells, which operate in licensed spectrum and provide improved cellular coverage inside homes and offices, have attracted significant interest in the wireless industry. As a result, an extensive deployment of femtocells is expected in the near future. One prime concern with a large-scale femtocell deployment is the resultant substantial energy consumption. In this paper, we address this issue by proposing a novel energy saving procedure which allows the femtocell base station (BS) to completely switch off its radio transmissions and associated processing when not involved in an active call. The results indicate that based on a certain voice traffic model, the proposed procedure introduces an average reduction of approximately 37.5% in the femtocell's power consumption. Moreover, for a high femtocell traffic scenario, a fivefold reduction in the occurrence of mobility events can also be achieved, compared to a fixed pilot transmit power strategy.

The use of a group of femtocells to jointly provide coverage in an enterprise environment introduces several challenges in the introduction of self-configuration and self-optimization capabilities required for plug-and-play styles of deployment. In this paper, an approach to automatically derive a distributed algorithm to dynamically optimize the coverage of a femtocell group using genetic programming is described. The resulting evolved algorithm showed the ability to optimize the coverage well, and is able to offer increased overall network capacity compared with a fixed coverage femtocell deployment.

Rising energy costs and the recent international focus on climate change issues has resulted in a high interest in improving the energy efficiency in the telecommunications industry. In this paper the effects of a joint deployment of macrocells for area coverage and publicly accessible residential picocells on the total energy consumption of the network is investigated. The decreasing energy efficiency of todaypsilas macrocellular technologies with increasing user demand for high data rates is discussed. It is shown that a joint deployment of macro- and publicly accessible residential picocells can reduce the total energy consumption by up to 60% in an urban area with todaypsilas technology. Furthermore the impact of future technologies for both macro- and femtocells on the energy efficiency is investigated. It is shown that the benefits of a joint macro- and picocell deployment will increase further as both technologies mature, and will result in a significant reduction of the network energy consumption as the user demand for high data rates increases.

The increasing demand for higher data rates in cellular networks has resulted in a trend to smaller cell sizes (femto cells) and pico-cellular hot spot coverage. In this paper the financial impact of pico-cellular home base station deployment in a macro-cellular network is explored. For increasing data rates, it becomes evident that current macro-cellular network deployment becomes less economically viable. It is shown that in urban areas a combination of publicly accessible home base stations, randomly deployed by the end user, and macro-cells for area coverage, deployed by an operator, can result in significant reductions (up to 70% in the investigated scenario) of the total annual network costs compared to a pure macro-cellular network.

The femtocell concept aims to combine fixed-line broadband access with cellular telephony using the deployment of low-cost, low-power 3G base stations in the subscriber's homes. These plug-and-play residential base stations would be deployed without much consideration to cell planning on the part of the user, relying instead on inbuilt auto-configuration abilities to minimise the impact on the macro cellular network by self-provisioning parameters such as the transmit and pilot power levels. In this paper, simulations of the deployment of such femtocells in a residential scenario were performed to study its effects on the service experienced by users that are connected to the underlay macrocells. The results show that with auto-configuration, the deployment of the femtocells would not pose a significant impact on the dropped call rate, causing an additional 0.45% increase in chance of a macrocell user's call dropping in the simulation's worst case scenario. In addition the impact of such femtocell deployment on the network signalling is discussed.

The idea of a completely independent, self-healing, self-deploying, and self-optimising communication network is an appealing one. This paper outlines why financial pressures will drive wireless communication networks towards the adoption of autonomic systems with the above characteristics, that will eventually become cognisant, exhibiting some degree of self- awareness. As a step in this direction, we explore the concept of robotic wireless base stations and discuss their behaviour subject to principles inspired by Asimov's Laws of Robotics. Finally, a discussion of how such laws would be applicable in current and future scenarios is presented.

This paper presents initial findings on how ambient networks technology will enable new ways to establish and manage access and service provisioning from a business perspective. The concepts of network composition and multi-radio multi-operator wireless access provide the framework for network advertising and discovery, which are essential first steps for allowing "any" user to be able to connect to "any" network. Technology support for different options of advertising are described followed by a discussion of the business related aspects including: i) payment schemes, ii) dynamic pricing, iii) use of identifiers for "frequent users", iv) use of public or "private" directories of offers and v) possibilities with alliances between providers. Finally, results of a case study on dynamic pricing are presented. The conclusion is that the proposed concepts of advertising and discovery indicate a very large potential for enabling a competitive market for both access and service provisioning including support for establishment of agreements on the fly

This paper provides a broad overview of a number of approaches to dynamic spectrum sharing, and presents a specific case study involving hierarchical co-channel macro- and pico-cells. This case study demonstrates the feasibility of implementing a private commons approach to spectrum sharing in a near term, commercial application. Finally, a roadmap for evolution towards dynamic spectrum sharing is put forward.

With the emergence of multiple wireless operators using a variety of access technologies, congested spectrum bands, and multi-standard user terminals, there is a pressing need to see what implications autonomic communication systems can have on business models in the wireless access business. This paper outlines and discusses some perspectives on the future impact of autonomicity in a demand-, resource-, and competition-driven environment and on the strategies of wireless access providers. The effects are investigated for a standards-neutral, multi-agent architecture that is capable of per-session/call trading of access to wireless network and spectrum resources, exploiting the capability of autonomous agents to represent the business interests of individual players. Results from a joint radio-resource/inter-agent-negotiation simulator are used to quantitatively assess the economic outcomes in various multi-operator competitive scenarios in a realistic environment (an international airport).