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The purpose of this thesis is to develop a novel objective function that ensures secure links between all nodes in an Internet of Things network when using the Routing Protocol for Low-Power and Lossy Networks (RPL) and only allow nodes in the network that share a key to join the network. We propose the Shared Identifier Secure Link Objective Function (SISLOF) to allow only nodes that share a key to join the network and therefore ensuring that all links between the nodes in the network are secure. SISLOF will look at a route that includes all nodes in the network and if a node shares a key with more than one node, it will then choose the node that has a shorter pathway to the root. We evaluate the overhead of the security keys on the Internet of Things nodes and the routing metrics by measuring the overhead when using first ETX and OF0 objective functions when using either the probabilistic scheme or the deterministic scheme. We then identified that the use of ETX or OF0 with both schemes is not appropriate because of the large overhead it adds on the devices and the link. We show that both ETX and OF0 add a large overhead and they are not suitable to be used with the security schemes. The secure objective function was needed as the existing objective functions add a large overhead on the Internet of Things devices when using two different key distribution schemes to distribute and provide keys between nodes and to create a link. We develop an objective function that only adds nodes that share a key to the routing table without the overhead cost the other objective functions added. We also identify that the probabilistic key distribution scheme outperforms the deterministic key distribution scheme for all objective functions. The significance of this study is that it has identified the need for an objective function that incorporates the security key distributions for the Routing Protocol for Low-Power and Lossy Networks (RPL) in the Internet of Things networks and the Shared Identifier Secure Link Objective Function (SISLOF) was developed to solve this problem.

In this thesis, Multiple-Input Multiple-Output (MIMO) techniques designed for transmission over narrowband Rayleigh fading channels are investigated. Specifically, in order to provide a diversity gain while eliminating the complexity of MIMO channel estimation, a Differential Space-Time Spreading (DSTS) scheme is designed that employs non-coherent detection. Additionally, in order to maximise the coding advantage of DSTS, it is combined with Sphere Packing (SP) modulation. The related capacity analysis shows that the DSTS-SP scheme exhibits a higher capacity than its counterpart dispensing with SP. Furthermore, in order to attain additional performance gains, the DSTS system invokes iterative detection, where the outer code is constituted by a Recursive Systematic Convolutional (RSC) code, while the inner code is a SP demapper in one of the prototype systems investigated, while the other scheme employs a Unity Rate Code (URC) as its inner code in order to eliminate the error floor exhibited by the system dispensing with URC. EXIT charts are used to analyse the convergence behaviour of the iteratively detected schemes and a novel technique is proposed for computing the maximum achievable rate of the system based on EXIT charts. Explicitly, the four-antenna-aided DSTSSP system employing no URC precoding attains a coding gain of 12 dB at a BER of 10−5 and performs within 1.82 dB from the maximum achievable rate limit. By contrast, the URC aided precoded system operates within 0.92 dB from the same limit. On the other hand, in order to maximise the DSTS system’s throughput, an adaptive DSTSSP scheme is proposed that exploits the advantages of differential encoding, iterative decoding as well as SP modulation. The achievable integrity and bit rate enhancements of the system are determined by the following factors: the specific MIMO configuration used for transmitting data from the four antennas, the spreading factor used and the RSC encoder’s code rate. Additionally, multi-functional MIMO techniques are designed to provide diversity gains, multiplexing gains and beamforming gains by combining the benefits of space-time codes, VBLAST and beamforming. First, a system employing Nt=4 transmit Antenna Arrays (AA) with LAA number of elements per AA and Nr=4 receive antennas is proposed, which is referred to as a Layered Steered Space-Time Code (LSSTC). Three iteratively detected near-capacity LSSTC-SP receiver structures are proposed, which differ in the number of inner iterations employed between the inner decoder and the SP demapper as well as in the choice of the outer code, which is either an RSC code or an Irregular Convolutional Code (IrCC). The three systems are capable of operating within 0.9, 0.4 and 0.6 dB from the maximum achievable rate limit of the system. A comparison between the three iteratively-detected schemes reveals that a carefully designed two-stage iterative detection scheme is capable of operating sufficiently close to capacity at a lower complexity, when compared to a three-stage system employing a RSC or a two-stage system using an IrCC as an outer code. On the other hand, in order to allow the LSSTC scheme to employ less receive antennas than transmit antennas, while still accommodating multiple users, a Layered Steered Space-Time Spreading (LSSTS) scheme is proposed that combines the benefits of space-time spreading, V-BLAST, beamforming and generalised MC DS-CDMA. Furthermore, iteratively detected LSSTS schemes are presented and an LLR post-processing technique is proposed in order to improve the attainable performance of the iteratively detected LSSTS system. Finally, a distributed turbo coding scheme is proposed that combines the benefits of turbo coding and cooperative communication, where iterative detection is employed by exchanging extrinsic information between the decoders of different single-antenna-aided users. Specifically, the effect of the errors induced in the first phase of cooperation, where the two users exchange their data, on the performance of the uplink in studied, while considering different fading channel characteristics.

Globalisation and fierce competition necessitate MEs to rethink their strategies as well as their organisational structure in order to survive in an increasingly complex business environment. This has been accompanied with an overt wave of strategic alliances signaling that these strategic means represent an important weapon for the multinational company to use in the Global battle. However, the British involvement in such a wave is not as clear as that of other countries, for most of previous research has been done on US and Japanese firms. The current study rectify this shortfall by revealing the incidence of British-foreign alliances during the period 1980-1989. The main objectives of the study are to examine the motivations of these alliances, to uncover the management practices of such strategies, and to determine their impacts on the international competitiveness of the British partners. During the course of the last decade British MEs have formed 337 strategic alliances with foreign firms, mainly belonging to the "Triad Region" of the US, Europe, and Japan. Most of the alliances were concentrated in just four industries; electronics, aerospace, telecommunications, and automotives. Technological complexity and the high cost of R&D as well as globalisation and fierce competition are the main motivations for British firms forming strategic alliances. Strategic alliances' success lies in two main considerations: one is balancing the attention in the three crucial stages of the management process (planning, formation, and operation and control), and two is understanding the issues that link one stage to the other, i.e. preparation for the formation stage and development of the plan and management team of the alliance. The alliance performance is influenced by the scope of its activities as well as the ability of the firm to effectively manage such a strategy. Strategic alliances have positive impacts on three significant issues, namely; the international competitiveness of the British partners, the management of the firms, and their technological capabilities. Further, firms that equally importantly consider the three management stages of their alliances or network of alliances are more likely to ensure the improvement and/or enhancement of their international competitiveness.

The escalating demand for faster, reliable, and energy-efficient wireless communications has steered researchers towards millimetre-wave (mm Wave) frequencies, offering immense bandwidth and high data rates. To adapt to the increasing complexity of such networks, machine learning (ML)-assisted techniques are used for efficient adaptation without complete parameter dependence knowledge. ML-assisted adaptive techniques are applied to an OFDM-CSIM system over amm Wave channel, utilising index modulation and compressed sensing for improved spectral efficiency, energy efficiency, and system design freedom. A DNN-based classifier is proposed, enhancing throughput and outperforming traditional adaptive modulations. A novel multi-layer Sparse Bayesian learning algorithm estimates channel state information with lower complexity, providing more accurate estimation and better performance than conventional methods. Then, the ML-assisted techniques are extended to joint radar and communication systems, using radar-derived side information to adjust communication beams, reducing training overhead and complexity for channel estimation. The system employs a uniform rectangular planar array with adaptive adjustment of antenna elements and array configurations via deep neural network and convolutional neural network classifiers. The simulation results show that the proposed method can achieve a satisfactory data rate that approaches the upper bound obtained by the exhaustive search scheme as well as guaranteeing the required sensing performance. In contrast to previous joint radar and communication system designs that separate these functions through different sub-antenna arrays, a more efficient approach integrating both sensing and communication tasks within a single system, called dual functional radar-communication, is introduced. An ML-assisted beamforming design for ultra-dense device-to-device mm Wave networks uses a convolutional long short-term memory-integrated graph neural network (CL-GNN) to learn historical channel characteristics and predict the beamforming matrix. Our findings show that this design meets the required sensing performance and achieves a near-optimal sum rate. The adaptable CL-GNN can be generalised for networks of varying sizes and densities.

Future smart cities will rely on Vehicular Networks (VNs) to provide the communications capabilities needed for a wide range of safety, eciency and entertainment urban applications. As multi-hop communications are employed to assure the coverage and throughput required by such applications, ecient routing design proves to be essential in VNs. However, signicant challenges are encountered when large and dense urban VNs are considered. In such cases, existing communications systems cannot meet all qualityof-service (QoS) requirements of the large spectrum of VN applications and, as a consequence, new solutions are needed. This thesis addresses VN routing design and analysis. A characterization of VNs is provided both from a radio-frequency propagation perspective as well as from a mobility evaluation perspective. Based on this, a network simulation environment is built, namely using a merge of MAT L A B and Simulation of Urban Mobility (SUMO) through the TraCI4Matlab package found within SUMO. The simulator is used to rst evaluate the eect of road-network structure size and complexity on the induced overhead. Then, a framework for route lifetime analysis is provided based on an exhaustive search routing technique to set an upper bound of the route lifetime as well as a shortest-path routing technique, as a routing example. The route lifetime is measured in order to further reveal the trade-o between the structure and dimension of the road-network and performance requirements as a fundamental research baseline for investigating and developing sophisticated routing models for vehicular networks. Results reveal that, on average, the shortest-path route lifetime is sub-optimal 11.56% of the time compared to the upper bound route lifetime. Then, a trade-o between the road-network dimensions and the QoS requirements is provided, where it is demonstrated that wireless channel fading has signicant eect on the route lifetime and that, on average, 7 times higher route lifetime could be achieved in a LOS scenario compared to its NLOS counterpart. Additionally, a Q-learning-based routing algorithm is proposed, namely Infrastructureaided Trac-Aware Routing (I-TAR), which introduces and leverages the static wired RSU infrastructure for packet forwarding. Then, the focus shifts towards a multi-source, multi-destination problem and the eect this imposes on node availability, as nodes also participate in other communications paths. This motivates the use of a hybrid routing approach, namely Hybrid Infrastructure-aided Trac Aware Routing (HI-TAR) that aims to select the best vehicle-to-vehicle/infrastructure (V2V/I) route by also considering Vehicle-to-Vehicle (V2V) only connections when an available RSU is not found and thus, vehicle-to-infrastructure (V2I) links are not available. Results demonstrate that I-TAR can achieve up to 19% higher average packet-delivery-ratio (APDR) compared to the state-of-the-art. Additionally, the hybrid approach improves the APDR and average end-to-end delay (AEED) performances of I-TAR by 4% and about 8%, respectively. Under a more realistic scenario, where node availability is considered, a decline of up to 51% decline in APDR performance is observed, whereas the proposed HITA R can increase the APDR performance by up to 50% compared to both I- TA R and the state-of-the-art. Finally, when multiple source-destination vehicle pairs are considered simultaneously, all the schemes that model and consider node availability, i.e. limited-availability, achieve from 72.2% to 82.3% lower APDR, when compared to those that do not, i.e. assuming full-availability. However, HI-TAR still provides 34.6% better APDR performance than I-TAR, and 40% more than the state-of-the-art.

Unmanned aerial vehicles (UAVs) have been attracting a lot of attention in recent years for its potential in numerous applications. Due to the flexibility and mobility of UAVs, UAV-mounted base stations are effective and cost-efficient to provide wireless connectivity and to improve the performance of terrestrial wireless network when fixed infrastructure is not available. In particular, UAVs can timely adjust their locations according to the movement of ground users (GUs). Against this background, we firstly propose a method to apply mobile edge caching on UAVs in wireless communication systems. By investigating the user request preference with the aid of latent Dirichlet allocation (LDA), the caching strategy can be optimized. In the proposed system, we consider the design of intelligent caching strategies when a number of UAVs are deployed to serve the GUs, where each UAV has a limited storage capacity for caching useful user contents. We use LDA to extract the user request preferences in order to intelligently caching data in the UAVs, while the k-means clustering is utilized to classify GUs and to assist in deploying the UAVs. Additionally, we consider three user-UAV association criteria, namely the user received signal to noise ratio (SNR), user preferences and the delay. Our simulation results show that, when compared to random caching, the average caching efficiency could be significantly improved from 50% to 70%, while the latency of our proposed system can also be greatly reduced. Then, we propose an optimized UAV-user association technique that can mitigate the challenge of the existence of "outliers" caused by limited UAV resources. More specifically, in order to solve the sudden-shift of UAVs position, which is a challenge for traditional deployment algorithm such as the k-means clustering, we propose a reinforcement learning based method to eliminate the challenge of sudden-shift requirement as well as to provide an improved performance, when we have limited UAV resources. Our simulation results show that the proposed UAV-user association can provide a solution for the "outliers" problem. Meanwhile, the average downlink rate of the system, which employs the proposed deep Q-learning (DQL) based UAV deployment, is close iv to the ideal system that employs a k-means clustering with infinite UAV speed. Additionally, we show that the DQL provides an improved performance, when the number of UAV is limited for a given coverage area. On the other hand, given the limited availability of on-board energy, we design an energy efficient communication scheme. Explicitly, we propose a method that combines the concept of index modulation (IM) with the UAV communications systems, which we refer to as IM-UAV, to attain an improved energy efficiency (EE). Furthermore, based on the proposed IM-UAV communication system, a gradient descent based UAV deployment scheme is designed to maximize the sum rate of the GUs in the target area. Additionally, the maximum likelihood detection for the IM-UAV requires a high computational complexity for detection at the GUs, while providing the best possible performance. Hence, we propose a low-complexity detection scheme that can separately detect the index symbols and data symbols to reduce the computational complexity at the receiver side. The simulation results demonstrate that the proposed deployment method is capable of attaining the appropriate positions to deploy the UAVs, while the EE is also improved by combining IM with UAV communication system. Finally, we consider a content-aware scenario that employs UAVs as aerial base stations to transmit data to GUs via air to ground communication links. Furthermore, inspired by the concept of rate splitting (RS), we design two subcarrier-sharing (SS) data transmission schemes to improve the average data rate of the GUs. Additionally, based on the proposed transmission schemes, we design the UAV deployment schemes, namely the fixed-point deployment scheme and traverse-search deployment scheme. The simulation results demonstrate that our proposed data transmission techniques significantly increase the average data rate, while the deployment schemes are capable of optimizing the data rate in the respective application scenarios.

In this thesis, we design cost-efficient high-performance communication systems employing analogue radio over fiber (A-RoF) techniques combined with wireless multiple-inputmultiple-output (MIMO) techniques. Our objective is to exploit the A-RoF design for reducing complexity of MIMO signal processing, without degrading the MIMO gains attained. Explicitly, we propose four novel systems involving both A-RoF and wireless MIMO schemes. Firstly, we conceive a novel A-RoF aided beamforming technique beneficially exploiting the fiber's potentially harmful non-linearity, in order to circumvent the excessive insertion loss of electronic phase-shifters. In this system, the phased array antenna elements are fed by the output of the highly non-linear fiber (HNLF), resulting in a beneficial angular beamsteering range depending on the length of the HNLF used, which can be exploited by sophisticated cloud/centralised radio access networks (C-RANs) for reducing the co-channel interference. Secondly, we propose an A-RoF aided spatial modulation (SM) scheme, where the SM's antenna indices are conveyed by the optical side-bands. Furthermore, we experimentally demonstrate the feasibility of our proposed concept by a prototype system using two-antenna based SM, realising an A-RoF aided SM system supporting a downlink rate of 2 Gbps. Then, we further developed the ARoF aided wireless MIMO design to conceive an adaptive C-RAN system, where both the modulation format and the number of connected remote radio heads are selected depending on the channel conditions. Additionally, inspired by our previous designs of amalgamating beamforming and SM using A-RoF, we extend our vision to multi-functional MIMO (MF-MIMO) systems. We subsequently conceive a novel A-RoF system design relying on the sophisticated multiset space-time shift keying (MS-STSK) concept, which is capable of combining diversity and SM. This flexible MF-MIMO design carries out its signal processing tasks in a central unit and it is capable of achieving a rate of 10 Gbps using 16QAM. The above-mentioned three systems rely on using single-mode silicon fiber for outdoor cellular systems, while in our fourth study, we propose an architecture for indoor scenarios in order to meet the increasing demands for in-home services. Thus, we design an all-optical processing aided wireless MF-MIMO architecture employing plastic optical fiber (POF), where both diversity and beamforming gains can be attained. Explicitly, we aim for realising a MF-MIMO system by using radio over POF techniques for RF operating in the 2.4 GHz band. Specifically, Alamouti's twin-antenna space-time block coded symbols are transmitted using a single Mach-Zehnder modulator (MZM). The attainable angular beamsteering range is 150◦ . We also show that this concept can be extended to a multi-user system using mode division multiplexing (MDM). Our research described in this thesis demonstrates the feasibility and the benefits of using A-RoF aided MIMO signal processing, in terms of both its cost-reduction and performance-improvements.

In this thesis, we propose a suite of compressed sensing (CS)-aided index modulation (IM) techniques for transmission over frequency-selective fading channels invoked in orthogonal frequency division multiplexing (OFDM) and multiple-input multiple-output (MIMO) arrangements. The design objective is to improve both the performance and the design flexibility by striking flexible trade-offs among the performance, bandwidth efficiency, energy efficiency and complexity. Specifically, a novel CS-aided frequency-domain IM system is proposed, combining the benefits of space-time shift keying (STSK), OFDM-IM and CS, in order to strike a flexible trade-off between the throughput and bit error rate (BER) performance. The proposed CS-aided frequency-domain IM is termed as CS-OFDM-STSK-IM. Explicitly, the CS-aided signalling strategy is applied to the transmitter for improving the throughput by introducing the concept of virtual domain. Then a pair of reduced-complexity detectors are proposed, which rely on the CS principles. In particular, we derive the discrete-input continuous-output memoryless channel (DCMC) capacity for characterising the achievable performance limit of the CS-OFDM-STSK-IM. Furthermore, in order to attain extra diversity gains at no cost in terms of power or bandwidth, the coordinate interleaving technique is applied. Additionally, for the sake of achieving a near-capacity performance, we conceive a two-stage serially concatenated channel-coded CS-OFDMSTSK-IM scheme employing iterative detection. To attain an improved BER performance as well as system capacity in comparison to the CS-OFDM-STSK-IM scheme, we propose an improved CS-aided multi-dimensional IM system relying on both frequency- and spatial-domain IM. Explicitly, extra implicit information bits are transmitted by detecting the activated indices of both the TAs and subcarriers, while striking a flexible design trade-off between the throughput and the diversity gain. Furthermore, CS is invoked at both the transmitter and the receiver of our multi-dimensional system for the sake of improving the system's design flexibility, whilst reducing the detector's complexity. We first present the maximum likelihood (ML) detector of the CS-aided multi-dimensional IM system for characterising the lower bound of the system's performance. Specifically, an upper bound is derived for the average bit error probability (ABEP) and it is observed that the theoretical upper bound derived becomes tight when compared to the ML detector simulation curves as the value of SNR increases. Although the ML detector is capable of achieving the optimal performance, its prohibitive computational complexity makes it impractical for our multi-dimensional scheme. Hence we propose a reduced-complexity detector based on CS principles, which imposes only a modest BER degradation compared to the ML detector. We also analyse the computational complexities of both the ML and of the reduced-complexity detectors in different system configurations. We show that the CSaided reduced-complexity detector is capable of attaining a flexible trade-off between the throughput and the complexity. Furthermore, soft-input soft-output decoders relying on both the optimal ML and on the reduced-complexity detectors are proposed for attaining near-capacity performances, which are analysed with the aid of EXtrinsic Information Transfer (EXIT) charts. Then we characterise the maximum achievable rate of the CS-aided multi-dimensional IM using both the ML and our reduced-complexity detectors by formulating the DCMC capacity and by invoking EXIT charts. Finally, attracted by the benefits of large-scale multi-user MIMO (LS-MU-MIMO) systems in terms of their high bandwidth efficiency, high energy efficiency and increased reliability, we conceive a novel two-dimensional IM, namely the CS-aided (generalised) space-frequency IM (CS-(G)SFIM) scheme in our LS-MU-MIMO uplink scenarios. Explicitly, extra information bits are conveyed by mapping both to the activated spatial and frequency-domain indices. More particularly, we employ different transmit antenna (TA) activation patterns for different transmitted symbols instead of using the same active TAs for the whole subcarrier group as proposed in the CS-aided multi-dimensional IM scheme, which improves both the BER performance as well as the diversity gains. For the sake of further increasing the system's achievable rate without sacrificing any bandwidth or energy, CS-aided pre-processing is applied to the frequency-domain IM. Then we design a CS-aided reduced-complexity multi-user detector, namely the reduced search-space based iterative matching pursuit (RSS-IMP), which significantly reduces the detection complexity compared to the ML detector. Furthermore, the RSS-IMP detector is capable of significantly reducing the detection complexity, while attaining better performances than both the conventional MU-MIMO-OFDM system and the proposed system using the classical minimum mean square error (MMSE) detector. We also characterise the performances of the CS-(G)SFIM system in the presence of channel estimation errors. Our simulation results show that the CS-(G)SFIM system is more robust to imperfect channel estimation than the conventional MU-MIMO-OFDM system. More importantly, owing to the highly flexible structure of our proposed scheme, improved trade-offs may be struck among the bandwidth efficiency, energy efficiency, BER performance, as well as the computational complexity by beneficially configuring our design, depending on the associated system requirements and channel conditions.