Computation off-loading in mobile edge computing (MEC) systems constitutes an efficient paradigm of supporting resource-intensive applications on mobile devices. However, the benefit of MEC cannot be fully exploited, when the communications link used for off-loading computational tasks is hostile. Fortunately, the propagation-induced impairments may be mitigated by intelligent reflecting surfaces (IRS), which are capable of enhancing both the spectral-and energy-efficiency. Specifically, an IRS comprises an IRS controller and a large number of passive reflecting elements, each of which may impose a phase shift on the incident signal, thus collaboratively improving the propagation environment. In this paper, the beneficial role of IRSs is investigated in MEC systems, where singleantenna devices may opt for off-loading a fraction of their computational tasks to the edge computing node via a multiantenna access point with the aid of an IRS. Pertinent latencyminimization problems are formulated for both single-device and multi-device scenarios, subject to practical constraints imposed on both the edge computing capability and the IRS phase shift design. To solve this problem, the block coordinate descent (BCD) technique is invoked to decouple the original problem into two subproblems, and then the computing and communications settings are alternatively optimized using low-complexity iterative algorithms. It is demonstrated that our IRS-aided MEC system is capable of significantly outperforming the conventional MEC system operating without IRSs. Quantitatively, about 20 % computational latency reduction is achieved over the conventional MEC system in a single cell of a 300 m radius and 5 active devices, relying on a 5-antenna access point.

Characterized by their ease of deployment and bird's-eye view, unmanned aerial vehicles (UAVs) may be widely deployed both in surveillance and traffic management. However, the moderate computational capability and the short battery life restrict the local data processing at the UAV side. Fortunately, this impediment may be mitigated by employing the mobile-edge computing (MEC) paradigm for offloading demanding computational tasks from the UAV through a wireless transmission link. However, the offloaded information may become compromised by eavesdroppers. To address this issue, we conceive an energyefficient computation offloading technique for UAV-MEC systems, with an emphasis on physical-layer security. We formulate a number of energy-efficiency problems for secure UAV-MEC systems, which are then transformed to convex problems. Finally, their optimal solutions are found for both active and passive eavesdroppers. Furthermore, the conditions of zero, partial and full offloading are analyzed from a physical perspective. The numerical results highlight the specific conditions of activating the above three offloading options and quantify the performance of our proposed offloading strategy in various scenarios.

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In this treatise, first of all, we conceive a generic Multiple-Symbol Differential Sphere Detection (MSDSD) solution for both single-and multiple-antenna based noncoherent schemes in both uncoded and coded scenarios, where the high-mobility aeronautical Ricean fading features are taken into account. The bespoke design is the first MSDSD solution in open literature that is applicable to the generic Differential Space-Time Modulation (DSTM) for transmission over Ricean fading. In the light of this development, the recently developed Differential Spatial Modulation (DSM) and its diversity counterpart of Differential Space-Time Block Coding using Index Shift Keying (DSTBC-ISK) are specifically recommended for aeronautical applications owing to their low-complexity single-RF and finite-cardinality features. Moreover, we further devise a noncoherent Decision-Feedback Differential Detection (DFDD) and a Channel State Information (CSI) estimation aided coherent detection, which also take into account the same Ricean features. Finally, the advantages of the proposed techniques in different scenarios lead us to propose for the aeronautical systems to adaptively (1) switch between coherent and non-coherent schemes, (2) switch between single-and multiple-antenna based schemes as well as (3) switch between high-diversity and high-throughput DSTM schemes.

Unmanned Aerial Vehicles (UAVs) are envisioned to be an important part of the device-centric Internet-of-Things (IoT). These bespoke Unmanned Aircraft Systems (UASs) that support UAVs significantly differ from traditional terrestrial and aeronautical networks, both of which are evolving towards their next-generation forms. The major challenges of the UAS include (1) the augmented interference due to strong Line-of-Sight (LoS) (2) the dynamic shadowing effects owing to 3-D aerial maneuvering, (3) the excessive Doppler shift owing to high UAV mobility as well as (4) the Size, Weight, And Power (SWAP) constraints. Against this background, we propose to invoke the recently developed coherent/non-coherent Spatial Modulation (SM) and its diversity-oriented counterpart of Space-Time Block Coding using Index Shift Keying (STBC-ISK). These arrangements employ multiple Transmit Antennas (TAs) in order to improve the network's Quality-of-Service (QoS), but they only use a single RF chain. Furthermore, based on the throughput, delay and power-efficiency, we conceive a novel three-fold adaptivity design, where the UAS may adaptively (I) switch between coherent and non-coherent schemes, (II) switch between single-and multiple-TA based arrangements as well as (III) switch between high-diversity and high-spectral-efficiency multiple-TA based schemes.L. Hanzo would like to acknowledge the financial support of the Engineering and Physical Sciences Research Council projects EP/Noo4558/1,