Energy-Efficient and Overhead-Aware Cooperative Communications

Due to the rapid growth of energy-hungry wireless multimedia services, telecom energy consumption is increasing at an extraordinary rate. Besides negative environmental impacts and higher energy bills for operators, it also affects user experience as improvements in battery technologies have not kept up with increasing mobile energy demands. Therefore, how to increase the energy efficiency (EE) of wireless communications has gained a lot of attention recently. Cooperative communication, where relays cooperatively retransmit the received data from the source to the destination, is seen as a promising technique to increases EE. Nevertheless, it requires more overhead than direct communication that needs to be taken into account for practical wireless cooperative networks. In order to achieve potential energy savings promised by cooperative communications in practical systems, overhead-aware cooperative relaying schemes with low overhead are imperative. For the case that not all relays can hear each other, i.e., hidden relays exist, an energy-efficient and a low-overhead cooperative relaying scheme is proposed. This scheme selects a subset of relays before data transmission, through the proactive participation of available relays using their local timers. Theoretical analysis of average EE under maximum transmission power constraint, using practical data packet length, and taking account of the overhead for obtaining channel state information (CSI), relay selection, and cooperative beamforming, is performed and a closed-form approximate expression for the optimal position of relays is derived. Furthermore, the overhead of the proposed scheme and the impact of data packet lengths on EE, are analysed. The analytical and simulation results reveal that the proposed scheme is significantly more energy-efficient than direct transmission, best relay selection, all relay selection, and a state-of-the-art existing cooperative relaying scheme. Moreover, the proposed scheme reduces the overhead and achieves higher energy savings for larger data packets. The conventional cooperative beamforming schemes rely on the feedback of CSIs of the best relays from the destination, which cause extra energy consumption and are prone to quantization errors in practical systems. In the case of clustered relays with location awareness and timer-based relay selection, where relays can overhear the transmission and know the location of each other, an energy-efficient overhead-aware cooperative relaying scheme is proposed, making CSI feedback from the destination dispensable. In order to avoid possible collisions between relay transmissions during best relays selection, a distributed mechanism for the selected relays to appropriately insert guard intervals before their transmissions is proposed. Average EE of the proposed scheme considering the related overhead is analysed. Moreover, the impact of the number of available relays, the number of selected relays and the location of relay cluster on EE is studied. The simulation results indicate that the proposed cooperative relaying scheme achieves higher EE than direct communication, best relay selection, and all relay selection for relay clusters located close to the source. Independent of the relay cluster location, the proposed scheme exhibits significantly higher EE than an existing cooperative relaying scheme. Device-to-device (D2D) communication in cellular networks that enable direct transmissions between user equipments (UEs) is seen as a promising way to improve both EE and spectral efficiency (SE). If the source UE (SUE) and the destination UE (DUE) are far away from each other or if the channel between them is too weak for direct transmission, then two-hop D2D communications, where relay UEs (RUEs) forward the SUE's data packets to the DUE, can be used. An energy- and spectral-efficient optimal adaptive forwarding strategy (OAFS) for two-hop D2D communications is proposed. In a distributed manner, the OAFS adaptively chooses between the best relay forwarding (BRF) and the cooperative relay beamforming (CRB) with the optimal number of selected RUEs, depending on which of them provides the higher instantaneous EE. In order to reduce the computational complexity of relay selection, a low-complexity sub-optimal adaptive forwarding strategy (SAFS) is proposed that selects between the BRF and the CRB with two RUEs by comparing their instantaneous EE. Theoretical analysis of the average EE and SE for the proposed adaptive forwarding strategies is performed considering maximum transmission power constraints, circuit power consumption and the overhead for the acquisition of CSI, forwarding mode selection and cooperative beamforming. The theoretical and simulation results show that the proposed OAFS and SAFS exhibit significantly higher EE and SE than the BRF, CRB, direct D2D communications and conventional cellular communications. For short to moderate SUE-to-DUE distances, SAFS is almost as energy- and spectral-efficient as OAFS

Relay assisted device-to-device communication with channel uncertainty

The gains of direct communication between user equipment in a network may not be fully realised due to the separation between the user equipment and due to the fading that the channel between these user equipment experiences. In order to fully realise the gains that direct (device-to-device) communication promises, idle user equipment can be exploited to serve as relays to enforce device-to-device communication. The availability of potential relay user equipment creates a problem: a way to select the relay user equipment. Moreover, unlike infrastructure relays, user equipment are carried around by people and these users are self-interested. Thus the problem of relay selection goes beyond choosing which device to assist in relayed communication but catering for user self-interest. Another problem in wireless communication is the unavailability of perfect channel state information. This reality creates uncertainty in the channel and so in designing selection algorithms, channel uncertainty awareness needs to be a consideration. Therefore the work in this thesis considers the design of relay user equipment selection algorithms that are not only device centric but that are relay user equipment centric. Furthermore, the designed algorithms are channel uncertainty aware. Firstly, a stable matching based relay user equipment selection algorithm is put forward for underlay device-to-device communication. A channel uncertainty aware approach is proposed to cater to imperfect channel state information at the devices. The algorithm is combined with a rate based mode selection algorithm. Next, to cater to the queue state at the relay user equipment, a cross-layer selection algorithm is proposed for a twoway decode and forward relay set up. The algorithm proposed employs deterministic uncertainty constraint in the interference channel, solving the selection algorithm in a heuristic fashion. Then a cluster head selection algorithm is proposed for device-to-device group communication constrained by channel uncertainty in the interference channel. The formulated rate maximization problem is solved for deterministic and probabilistic constraint scenarios, and the problem extended to a multiple-input single-out scenario for which robust beamforming was designed. Finally, relay utility and social distance based selection algorithms are proposed for full duplex decode and forward device-to-device communication set up. A worst-case approach is proposed for a full channel uncertainty scenario. The results from computer simulations indicate that the proposed algorithms offer spectral efficiency, fairness and energy efficiency gains. The results also showed clearly the deterioration in the performance of networks when perfect channel state information is assumed

Scaling up virtual MIMO systems

Multiple-input multiple-output (MIMO) systems are a mature technology that has been incorporated into current wireless broadband standards to improve the channel capacity and link reliability. Nevertheless, due to the continuous increasing demand for wireless data traffic new strategies are to be adopted. Very large MIMO antenna arrays represents a paradigm shift in terms of theory and implementation, where the use of tens or hundreds of antennas provides significant improvements in throughput and radiated energy efficiency compared to single antennas setups. Since design constraints limit the number of usable antennas, virtual systems can be seen as a promising technique due to their ability to mimic and exploit the gains of multi-antenna systems by means of wireless cooperation. Considering these arguments, in this work, energy efficient coding and network design for large virtual MIMO systems are presented. Firstly, a cooperative virtual MIMO (V-MIMO) system that uses a large multi-antenna transmitter and implements compress-and-forward (CF) relay cooperation is investigated. Since constructing a reliable codebook is the most computationally complex task performed by the relay nodes in CF cooperation, reduced complexity quantisation techniques are introduced. The analysis is focused on the block error probability (BLER) and the computational complexity for the uniform scalar quantiser (U-SQ) and the Lloyd-Max algorithm (LM-SQ). Numerical results show that the LM-SQ is simpler to design and can achieve a BLER performance comparable to the optimal vector quantiser. Furthermore, due to its low complexity, U-SQ could be consider particularly suitable for very large wireless systems. Even though very large MIMO systems enhance the spectral efficiency of wireless networks, this comes at the expense of linearly increasing the power consumption due to the use of multiple radio frequency chains to support the antennas. Thus, the energy efficiency and throughput of the cooperative V-MIMO system are analysed and the impact of the imperfect channel state information (CSI) on the system’s performance is studied. Finally, a power allocation algorithm is implemented to reduce the total power consumption. Simulation results show that wireless cooperation between users is more energy efficient than using a high modulation order transmission and that the larger the number of transmit antennas the lower the impact of the imperfect CSI on the system’s performance. Finally, the application of cooperative systems is extended to wireless self-backhauling heterogeneous networks, where the decode-and-forward (DF) protocol is employed to provide a cost-effective and reliable backhaul. The associated trade-offs for a heterogeneous network with inhomogeneous user distributions are investigated through the use of sleeping strategies. Three different policies for switching-off base stations are considered: random, load-based and greedy algorithms. The probability of coverage for the random and load-based sleeping policies is derived. Moreover, an energy efficient base station deployment and operation approach is presented. Numerical results show that the average number of base stations required to support the traffic load at peak-time can be reduced by using the greedy algorithm for base station deployment and that highly clustered networks exhibit a smaller average serving distance and thus, a better probability of coverage

Analysis and Ad-hoc Networking Solutions for Cooperative Relaying Systems

Users of mobile networks are increasingly demanding higher data rates from their service providers. To cater to this demand, various signal processing and networking algorithms have been proposed. Amongst them the multiple input multiple output (MIMO) scheme of wireless communications is one of the most promising options. However, due to certain physical restrictions, e.g., size, it is not possible for many devices to have multiple antennas on them. Also, most of the devices currently in use are single-antenna devices. Such devices can make use of the MIMO scheme by employing cooperative MIMO methods. This involves nearby nodes utilizing the antennas of each other to form virtual antenna arrays (VAAs). Nodes with limited communication ranges can further employ multi-hopping to be able to communicate with far away nodes. However, an ad-hoc communications scheme with cooperative MIMO multi-hopping can be challenging to implement because of its de-centralized nature and lack of a centralized controling entity such as a base-station. This thesis looks at methods to alleviate the problems faced by such networks.In the first part of this thesis, we look, analytically, at the relaying scheme under consideration and derive closed form expressions for certain performance measures (signal to noise ratio (SNR), symbol error rate (SER), bit error rate (BER), and capacity) for the co-located and cooperative multiple antenna schemes in different relaying configurations (amplify-and-forward and decode-and-forward) and different antenna configurations (single input single output (SISO), single input multiple output (SIMO) and MIMO). These expressions show the importance of reducing the number of hops in multi-hop communications to achieve a better performance. We can also see the impact of different antenna configurations and different transmit powers on the number of hops through these simplified expressions.We also look at the impact of synchronization errors on the cooperative MIMO communications scheme and derive a lower bound of the SINR and an expression for the BER in the high SNR regime. These expressions can help the network designers to ensure that the quality of service (QoS) is satisfied even in the worst-case scenarios. In the second part of the thesis we present some algorithms developed by us to help the set-up and functioning of cluster-based ad-hoc networks that employ cooperative relaying. We present a clustering algorithm that takes into account the battery status of nodes in order to ensure a longer network life-time. We also present a routing mechanism that is tailored for use in cooperative MIMO multi-hop relaying. The benefits of both schemes are shown through simulations.A method to handle data in ad-hoc networks using distributed hash tables (DHTs) is also presented. Moreover, we also present a physical layer security mechanism for multi-hop relaying. We also analyze the physical layer security mechanism for the cooperative MIMO scheme. This analysis shows that the cooperative MIMO scheme is more beneficial than co-located MIMO in terms of the information theoretic limits of the physical layer security.Nutzer mobiler Netzwerke fordern zunehmend höhere Datenraten von ihren Dienstleistern. Um diesem Bedarf gerecht zu werden, wurden verschiedene Signalverarbeitungsalgorithmen entwickelt. Dabei ist das "Multiple input multiple output" (MIMO)-Verfahren für die drahtlose Kommunikation eine der vielversprechendsten Techniken. Jedoch ist aufgrund bestimmter physikalischer Beschränkungen, wie zum Beispiel die Baugröße, die Verwendung von mehreren Antennen für viele Endgeräte nicht möglich. Dennoch können solche Ein-Antennen-Geräte durch den Einsatz kooperativer MIMO-Verfahren von den Vorteilen des MIMO-Prinzips profitieren. Dabei schließen sich naheliegende Knoten zusammen um ein sogenanntes virtuelles Antennen-Array zu bilden. Weiterhin können Knoten mit beschränktem Kommunikationsbereich durch mehrere Hops mit weiter entfernten Knoten kommunizieren. Allerdings stellt der Aufbau eines solchen Ad-hoc-Netzwerks mit kooperativen MIMO-Fähigkeiten aufgrund der dezentralen Natur und das Fehlen einer zentral-steuernden Einheit, wie einer Basisstation, eine große Herausforderung dar. Diese Arbeit befasst sich mit den Problemstellungen dieser Netzwerke und bietet verschiedene Lösungsansätze.Im ersten Teil dieser Arbeit werden analytisch in sich geschlossene Ausdrücke für ein kooperatives Relaying-System bezüglicher verschiedener Metriken, wie das Signal-Rausch-Verhältnis, die Symbolfehlerrate, die Bitfehlerrate und die Kapazität, hergeleitet. Dabei werden die "Amplify-and forward" und "Decode-and-forward" Relaying-Protokolle, sowie unterschiedliche Mehrantennen-Konfigurationen, wie "Single input single output" (SISO), "Single input multiple output" (SIMO) und MIMO betrachtet. Diese Ausdrücke zeigen die Bedeutung der Reduzierung der Hop-Anzahl in Mehr-Hop-Systemen, um eine höhere Leistung zu erzielen. Zudem werden die Auswirkungen verschiedener Antennen-Konfigurationen und Sendeleistungen auf die Anzahl der Hops analysiert.  Weiterhin wird der Einfluss von Synchronisationsfehlern auf das kooperative MIMO-Verfahren herausgestellt und daraus eine untere Grenze für das Signal-zu-Interferenz-und-Rausch-Verhältnis, sowie ein Ausdruck für die Bitfehlerrate bei hohem Signal-Rausch-Verhältnis entwickelt. Diese Zusammenhänge sollen Netzwerk-Designern helfen die Qualität des Services auch in den Worst-Case-Szenarien sicherzustellen. Im zweiten Teil der Arbeit werden einige innovative Algorithmen vorgestellt, die die Einrichtung und die Funktionsweise von Cluster-basierten Ad-hoc-Netzwerken, die kooperative Relays verwenden, erleichtern und verbessern. Darunter befinden sich ein Clustering-Algorithmus, der den Batteriestatus der Knoten berücksichtigt, um eine längere Lebensdauer des Netzwerks zu gewährleisten und ein Routing-Mechanismus, der auf den Einsatz in kooperativen MIMO Mehr-Hop-Systemen zugeschnitten ist. Die Vorteile beider Algorithmen werden durch Simulationen veranschaulicht. Eine Methode, die Daten in Ad-hoc-Netzwerken mit verteilten Hash-Tabellen behandelt wird ebenfalls vorgestellt. Darüber hinaus wird auch ein Sicherheitsmechanismus für die physikalische Schicht in Multi-Hop-Systemen und kooperativen MIMO-Systemen präsentiert. Eine Analyse zeigt, dass das kooperative MIMO-Verfahren deutliche Vorteile gegenüber dem konventionellen MIMO-Verfahren hinsichtlich der informationstheoretischen Grenzen der Sicherheit auf der physikalischen Schicht aufweist

Collaborative Networking: The Integration of Collaborative Communication into WSN-routing

According to the Collaborative Communication (CC) techniques, a group of sensor nodes modify their carrier phases, so that their signals are received by the destination synchronously to gain higher level of reliability and flexibility. In this research, CC is fused into networking approaches to extend its scalability as well

Performance Analysis and Learning Algorithms in Advanced Wireless Networks

Over the past decade, wireless data traffic has experienced an exponential growth, especially with multimedia traffic becoming the dominant traffic, and such growth is expected to continue in the near future. This unprecedented growth has led to an increasing demand for high-rate wireless communications.Key solutions for addressing such demand include extreme network densification with more small-cells, the utilization of high frequency bands, such as the millimeter wave (mmWave) bands and terahertz (THz) bands, where more bandwidth is available, and unmanned aerial vehicle (UAV)-enabled cellular networks. With this motivation, different types of advanced wireless networks are considered in this thesis. In particular, mmWave cellular networks, networks with hybrid THz, mmWave and microwave transmissions, and UAV-enabled networks are studied, and performance metrics such as the signal-to-interference-plus-noise ratio (SINR) coverage, energy coverage, and area spectral efficiency are analyzed. In addition, UAV path planning in cellular networks are investigated, and deep reinforcement learning (DRL) based algorithms are proposed to find collision-free UAV trajectory to accomplish different missions. In the first part of this thesis, mmWave cellular networks are considered. First, K-tier heterogeneous mmWave cellular networks with user-centric small-cell deployments are studied. Particularly, a heterogeneous network model with user equipments (UEs) being distributed according to Poisson cluster processes (PCPs) is considered. Distinguishing features of mmWave communications including directional beamforming and a detailed path loss model are taken into account. General expressions for the association probabilities of different tier base stations (BSs) are determined. Using tools from stochastic geometry, the Laplace transform of the interference is characterized and general expressions for the SINR coverage probability and area spectral efficiency are derived. Second, a distributed multi-agent learning-based algorithm for beamforming in mmWave multiple input multiple output (MIMO) networks is proposed to maximize the sum-rate of all UEs. Following the analysis of mmWave cellular networks, a three-tier heterogeneous network is considered, where access points (APs), small-cell BSs (SBSs) and macrocell BSs (MBSs) transmit in THz, mmWave, microwave frequency bands, respectively. By using tools from stochastic geometry, the complementary cumulative distribution function (CCDF) of the received signal power, the Laplace transform of the aggregate interference, and the SINR coverage probability are determined. Next, system-level performance of UAV-enabled cellular networks is studied. More specifically, in the first part, UAV-assisted mmWave cellular networks are addressed, in which the UE locations are modeled using PCPs. In the downlink phase, simultaneous wireless information and power transfer (SWIPT) technique is considered. The association probability, energy coverages and a successful transmission probability to jointly determine the energy and SINR coverages are derived. In the uplink phase, a scenario that each UAV receives information from its own cluster member UEs is taken into account. The Laplace transform of the interference components and the uplink SINR coverage are characterized. In the second part, cellular-connected UAV networks is investigated, in which the UAVs are aerial UEs served by the ground base stations (GBSs). 3D antenna radiation combing the vertical and horizontal patterns is taken into account. In the final part of this thesis, deep reinforcement learning based algorithms are proposed for UAV path planning in cellular networks. Particularly, in the first part, multi-UAV non-cooperative scenarios is considered, where multiple UAVs need to fly from initial locations to destinations, while satisfying collision avoidance, wireless connectivity and kinematic constraints. The goal is to find trajectories for the cellular-connected UAVs to minimize their mission completion time. The multi-UAV trajectory optimization problem is formulated as a sequential decision making problem, and a decentralized DRL approach is proposed to solve the problem. Moreover, multiple UAV trajectory design in cellular networks with a dynamic jammer is studied, and a learning-based algorithm is proposed. Subsequently, a UAV trajectory optimization problem is considered to maximize the collected data from multiple Internet of things (IoT) nodes under realistic constraints. The problem is translated into a Markov decision process (MDP) and dueling double deep Q-network (D3QN) is proposed to learn the decision making policy