Dynamic Radio Cooperation for Downlink Cloud-RANs with Computing Resource Sharing

A novel dynamic radio-cooperation strategy is proposed for Cloud Radio Access Networks (C-RANs) consisting of multiple Remote Radio Heads (RRHs) connected to a central Virtual Base Station (VBS) pool. In particular, the key capabilities of C-RANs in computing-resource sharing and real-time communication among the VBSs are leveraged to design a joint dynamic radio clustering and cooperative beamforming scheme that maximizes the downlink weighted sum-rate system utility (WSRSU). Due to the combinatorial nature of the radio clustering process and the non-convexity of the cooperative beamforming design, the underlying optimization problem is NP-hard, and is extremely difficult to solve for a large network. Our approach aims for a suboptimal solution by transforming the original problem into a Mixed-Integer Second-Order Cone Program (MI-SOCP), which can be solved efficiently using a proposed iterative algorithm. Numerical simulation results show that our low-complexity algorithm provides close-to-optimal performance in terms of WSRSU while significantly outperforming conventional radio clustering and beamforming schemes. Additionally, the results also demonstrate the significant improvement in computing-resource utilization of C-RANs over traditional RANs with distributed computing resources.Comment: 9 pages, 6 figures, accepted to IEEE MASS 201

Spectrum sharing backhaul satellite-terrestrial systems via analog beamforming

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Current satellite and terrestrial backhaul systems are deployed in disjoint frequency bands. This fact precludes an efficient use of the spectrum and limits the evolution of wireless backhauling networks. In this paper, we propose an interference mitigation technique in order to allow the spectrum coexistence between satellite and terrestrial backhaul links. This interference reliever is implemented at the terrestrial backhaul nodes, which are assumed to be equipped with multiple antennas. Due to the large bandwidth and huge number of antennas required in these systems, we consider pure analog beamforming. Precisely, we assume a phased array beamforming configuration so that the terrestrial backhaul node can only reduce the interference by changing the phases of each beamforming weight. Two cases are considered: the 18 and 28 GHz band where transmit and receive beamforming optimization problems shall be tackled, respectively. In both cases, the optimization problem results in a nonconvex problem that we propose to solve via two alternative convex approximation methods. These two approaches are evaluated and they present less than 1 dB array gain loss with respect to the upper bound solution. Finally, the spectral efficiency gains of the proposed spectrum sharing scenarios are validated in numerical simulations.Peer ReviewedPostprint (published version

Latency and Reliability Aware Edge Computation Offloading in 5G Networks

Empowered by recent technological advances and driven by the ever-growing population density and needs, the conception of 5G has opened up the expectations of what mobile networks are capable of to heights never seen before, promising to unleash a myriad of new business practices and paving the way for a surging number of user equipments to carry out novel service operations. The advent of 5G and networks beyond will hence enable the vision of Internet of Things (IoT) and smart city with its ubiquitous and heterogeneous use cases belonging to various verticals operating on a common underlying infrastructure, such as smart healthcare, autonomous driving, and smart manufacturing, while imposing extreme unprecedented Quality of Service (QoS) requirements in terms of latency and reliability among others. Due to the necessity of those modern services such as traffic coordination, industrial processes, and mission critical applications to perform heavy workload computations on the collected input, IoT devices such as cameras, sensors, and Cyber-Physical Systems (CPSs), which have limited energy and processing capabilities are put under an unusual strain to seamlessly carry out the required service computations. While offloading the devices' workload to cloud data centers with Mobile Cloud Computing (MCC) remains a possible alternative which also brings about a high computation reliability, the latency incurred from this approach would prevent from satisfying the services' QoS requirements, in addition to elevating the load in the network core and backhaul, rendering MCC an inadequate solution for handling the 5G services' required computations. In light of this development, Multi-access Edge Computing (MEC) has been proposed as a cutting edge technology for realizing a low-latency computation offloading by bringing the cloud to the vicinity of end-user devices as processing units co-located within base stations leveraging the virtualization technique. Although it promises to satisfy the stringent latency service requirements, realizing the edge-cloud solution is coupled with various challenges, such as the edge servers' restricted capacity, their reduced processing reliability, the IoT devices' limited offloading energy, the wireless offloading channels' often weak quality, the difficulty to adapt to dynamic environment changes and to under-served networks, and the Network Operators (NOs)' cost-efficiency concerns. In light of those conditions, the NOs are consequently looking to devise efficient innovative computation offloading schemes through leveraging novel technologies and architectures for guaranteeing the seamless provisioning of modern services with their stringent latency and reliability QoS requirements, while ensuring the effective utilization of the various network and devices' available resources. Leveraging a hierarchical arrangement of MEC with second-tier edge servers co-located within aggregation nodes and macro-cells can expand the edge network's capability, while utilizing Unmanned Aerial Vehicles (UAVs) to provision the MEC service via UAV-mounted cloudlets can increase the availability, flexibility, and scalability of the computation offloading solution. Moreover, aiding the MEC system with UAVs and Intelligent Reflecting Surfaces (IRSs) can improve the computation offloading performance by enhancing the wireless communication channels' conditions. By effectively leveraging those novel technologies while tackling their challenges, the edge-cloud paradigm will bring about a tremendous advancement to 5G networks and beyond, opening the door to enabling all sorts of modern and futuristic services. In this dissertation, we attempt to address key challenges linked to realizing the vision of a low-latency and high-reliability edge computation offloading in modern networks while exploring the aid of multiple 5G network technologies. Towards that end, we provide novel contributions related to the allocation of network and devices' resources as well as the optimization of other offloading parameters, and thereby efficiently utilizing the underlying infrastructure such as to enable energy and cost-efficient computation offloading schemes, by leveraging several customized solutions and optimization techniques. In particular, we first tackle the computation offloading problem considering a multi-tier MEC with a deployed second-tier edge-cloud, where we optimize its use through proposed low-complexity algorithms, such as to achieve an energy and cost-efficient solution that guarantees the services' latency requirements. Due to the significant advantage of operating MEC in heterogeneous networks, we extend the scenario to a network of small-cells with the second-tier edge server being co-located within the macro-cell which can be reached through a wireless backhaul, where we optimize the macro-cell server use along with the other offloading parameters through a proposed customized algorithm based on the Successive Convex Approximation (SCA) technique. Then, given the UAVs' considerable ability in expanding the capabilities of cellular networks and MEC systems, we study the latency and reliability aware optimized positioning and use of UAV-mounted cloudlets for computation offloading through two planning and operational problems while considering tasks redundancy, and propose customized solutions for solving those problems. Finally, given the IRSs' ability to also enhance the channel conditions through the tuning of their passive reflecting elements, we extend the latency and reliability aware study to a scenario of an IRS-aided MEC system considering both a single-user and multi-user OFDMA cases, where we explore the optimized IRSs' use in order to reveal their role in reducing the UEs' offloading consumption energy and saving the network resources, through proposed customized solutions based on the SCA approach and the SDR technique

Transmit optimization techniques for physical layer security

PhD ThesisOver the last several decades, reliable communication has received considerable attention in the area of dynamic network con gurations and distributed processing techniques. Traditional secure communications mainly considered transmission cryptography, which has been developed in the network layer. However, the nature of wireless transmission introduces various challenges of key distribution and management in establishing secure communication links. Physical layer security has been recently recognized as a promising new design paradigm to provide security in wireless networks in addition to existing conventional cryptographic methods, where the physical layer dynamics of fading channels are exploited to establish secure wireless links. On the other hand, with the ever-increasing demand of wireless access users, multi-antenna transmission has been considered as one of e ective approaches to improve the capacity of wireless networks. Multi-antenna transmission applied in physical layer security has extracted more and more attentions by exploiting additional degrees of freedom and diversity gains. In this thesis, di erent multi-antenna transmit optimization techniques are developed for physical layer secure transmission. The secrecy rate optimization problems (i.e., power minimization and secrecy rate maximization) are formulated to guarantee the optimal power allocation. First, transmit optimization for multiple-input single-output (MISO) secrecy channels are developed to design secure transmit beamformer that minimize the transmit power to achieve a target secrecy rate. Besides, the associated robust scheme with the secrecy rate outage probability constraint are presented with statistical channel uncertainty, where the outage probability constraint requires that the achieved secrecy rate exceeds certain thresholds with a speci c probability. Second, multiantenna cooperative jammer (CJ) is presented to provide jamming services that introduces extra interference to assist a multiple-input multipleoutput (MIMO) secure transmission. Transmit optimization for this CJaided MIMO secrecy channel is designed to achieve an optimal power allocation. Moreover, secure transmission is achieved when the CJ introduces charges for its jamming service based on the amount of the interference caused to the eavesdropper, where the Stackelberg game is proposed to handle, and the Stackelberg equilibrium is analytically derived. Finally, transmit optimization for MISO secure simultaneous wireless information and power transfer (SWIPT) is investigated, where secure transmit beamformer is designed with/without the help of arti - cial noise (AN) to maximize the achieved secrecy rate such that satisfy the transmit power budget and the energy harvesting (EH) constraint. The performance of all proposed schemes are validated by MATLAB simulation results

Handling Interference in Integrated HAPS-Terrestrial Networks through Radio Resource Management

Vertical heterogeneous networks (vHetNets) are promising architectures to bring significant advantages for 6G and beyond mobile communications. High altitude platform station (HAPS), one of the nodes in the vHetNets, can be considered as a complementary platform for terrestrial networks to meet the ever-increasing dynamic capacity demand and provide sustainable wireless networks for future. However, the problem of interference is the bottleneck for the optimal operation of such an integrated network. Thus, designing efficient interference management techniques is inevitable. In this work, we aim to design a joint power-subcarrier allocation scheme in order to achieve fairness for all users. We formulate the max-min fairness (MMF) optimization problem and develop a rapid converging iterative algorithm to solve it. Numerical results validate the superiority of the proposed algorithm and show better performance over other conventional network scenarios.Comment: 7 pages, 3 figures, Accepted by IEEE Wireless Communications Letter

General Rank Multiuser Downlink Beamforming With Shaping Constraints Using Real-valued OSTBC

In this paper we consider optimal multiuser downlink beamforming in the presence of a massive number of arbitrary quadratic shaping constraints. We combine beamforming with full-rate high dimensional real-valued orthogonal space time block coding (OSTBC) to increase the number of beamforming weight vectors and associated degrees of freedom in the beamformer design. The original multi-constraint beamforming problem is converted into a convex optimization problem using semidefinite relaxation (SDR) which can be solved efficiently. In contrast to conventional (rank-one) beamforming approaches in which an optimal beamforming solution can be obtained only when the SDR solution (after rank reduction) exhibits the rank-one property, in our approach optimality is guaranteed when a rank of eight is not exceeded. We show that our approach can incorporate up to 79 additional shaping constraints for which an optimal beamforming solution is guaranteed as compared to a maximum of two additional constraints that bound the conventional rank-one downlink beamforming designs. Simulation results demonstrate the flexibility of our proposed beamformer design