Beatriz Lorenzo

On the Analysis of Scheduling in Dynamic Duplex Multihop mmWave Cellular Systems

With the shortage of spectrum in conventional cellular frequencies, millimeter-wave (mmWave) bands are being widely considered for use in next-generation networks. Multihop relaying is likely to play a significant role in mmWave cellular systems for self backhauling, range extension and improved robustness from path diversity. However, designing scheduling policies for these systems is challenging due to the need to account for both adaptive directional transmissions and dynamic time-division duplexing schedules, which are key enabling features of mmWave systems. This paper considers the problem of joint scheduling and congestion control in a multihop mmWave network using a Network Utility Maximization (NUM) framework. Interference is modeled with an exact model and two auxiliar simplified models: actual interference (AI), with a graph-based calculation of the Signal to Interference plus Noise Ratio (SINR) depending on dynamic link activity and directivity, as well as upper and lower bounds computed from worst-case interference (WI) and interference free (IF) approximations. Throughput and utility optimal policies are derived for all interference models (AI, WI and IF) with both deterministic Maximum Weighted and randomized Pick and Compare scheduling algorithms, jointly with decentralized Dual Congestion Control. Results are evaluated with numerical simulations, using accurate mmWave channel and beamforming gain approximations based on measurement campaigns.

Optimal Routing and Traffic Scheduling for Multihop Cellular Networks Using Genetic Algorithm

When considering a multicell scenario with nonuniform traffic distribution in multihop wireless networks, the search for the optimum topology becomes an NP-hard problem. For such problems, exact algorithms based on exhaustive search are only useful for small toy models, so heuristic algorithms such as genetic algorithms (GA) must be used in practice. For this purpose, we present a novel sequential genetic algorithm (SGA) to optimize the relaying topology in multihop cellular networks aware of the intercell interference and the spatial traffic distribution dynamics. We encode the topologies as a set of chromosomes and special crossover and mutation operations are proposed to search for the optimum topology. The performance is measured by a fitness function that includes the throughput, power consumption and delay. Improvement in the fitness function is sequentially controlled as newer generations evolve and whenever the improvement is sufficiently increased the current topology is updated by the new one having higher fitness. Numerical results show that SGA provides both high performance improvements in the system and fast convergence (at least one order of magnitude faster than exhaustive search) in a dynamic network environment. We also demonstrate the robustness of our algorithm to the initial state of the network.

Context-aware nanoscale modeling of multicast multihop cellular networks

In this paper, we present a new approach to optimization of multicast in multihop cellular networks. We apply a hexagonal tessellation for inner partitioning of the cell into smaller subcells of radius r. Subcells may be several orders of magnitude smaller than, e.g., microcells, resulting in what we refer to as a nanoscale network model (NSNM), including a special nanoscale channel model (NSCM) for this application. For such tessellation, a spatial interleaving SI MAC protocol is introduced for context-aware interlink interference management. The directed flooding routing protocol (DFRP) and interflooding network coding (IFNC) are proposed for such a network model including intercell flooding coordination (ICFC) protocol to minimize the intercell interference. By adjusting the radius of the subcell r , we obtain different hopping ranges that directly affect the throughput, power consumption, and interference. With r as the optimization parameter, in this paper we jointly optimize scheduling, routing, and power control to obtain the optimum tradeoff between throughput, delay, and power consumption in multicast cellular networks. A set of numerical results demonstrates that the NSNM enables high-resolution optimization of the system and an effective use of the context awareness.

Sociality-Aided New Adaptive Infection Recovery Schemes for Multicast DTNs

Delay-tolerant networks (DTNs) consist of nodes moving around and occasionally coming into each others proximity. During the limited proximity time, nodes can exchange data; this can result in a very slow data dissemination process that is usually governed by a replication-based mechanism. However, due to the long propagation delay and the large overhead associated with the replication approach, DTN delivery performance is neither efficient nor effective. To reduce the overhead, which becomes a critical aspect particularly when addressing a multicast scenario, infection recovery mechanisms have been proposed to control and reduce the number of packet copies circulating through the network. This, however, has the cost of decreasing the chances of delivering packets to all destinations. In this paper, adaptivity in infection recovery is addressed. This represents a viable solution to make transmission more reliable, hence delaying the activation of the infection recovery procedure, depending on the number of nodes, destinations, and the time. Moreover, we also propose to exploit an additional feature in data multicasting, i.e., socially aided data dissemination, where the packet dissemination procedure is not trivially epidemic, but rather exploits the intrinsic sociality of users and their interests to reduce the delivery overhead and speed up the multicast process. More specifically, we consider a procedure where users are not regarded as individual members of the network but can be aggregated into groups sharing interests, and their sociality helps the data dissemination procedure. Results of our analysis show that new sociality-aided adaptive recovery schemes can speed up the delivery process.

Power-Efficient Resource Allocation in a Heterogeneous Network With Cellular and D2D Capabilities

This paper focuses on a heterogeneous scenario in which cellular and wireless local area technologies coexist and mobile devices are enabled with device-to-device (D2D) communication capabilities. In this context, this paper assumes a network architecture in which a given user equipment (UE) can receive mobile service either by connecting directly to a cellular base station (BS) or by connecting through another UE that acts as an access point (AP) and relays the traffic from a cellular BS. This paper investigates the optimization of the connectivity of different UEs with the target to minimize the total transmission power. An optimization framework is presented, and a distributed strategy based on Q-learning and softmax decision-making is proposed as a means to solve the considered problem with reduced complexity. The proposed strategy is evaluated under different conditions, and it is shown that the strategy achieves a performance very close to the optimum. Moreover, significant transmission power reductions of approximately 40% are obtained with respect to the classical approach, in which all UEs are connected to the cellular infrastructure. For multicell scenarios, in which the optimum solution cannot be easily known a priori, the proposed approach is compared against a centralized genetic algorithm. The proposed approach achieves similar performance in terms of total transmitted power while exhibiting much lower computational requirements.

Traffic adaptive relaying topology control

The optimization of the relaying topology in multihop cellular network should provide the answer to the question who is transmitting to whom, and when, in such a way to insure the best system performance. In the case of temporally and spatially varying traffic distribution the optimal topology will also vary in time and an efficient way for topology control is needed in order to maximize the system performance. In this paper we present an algorithm for efficient relaying topology control, which is aware of the intercell interference, requiring coordinated action between the cells and resulting in multicell jointly optimal relaying topology. Numerical results demonstrate that an adaptive relaying topology control provides the network utility improvements and presents the framework for quantifying these improvements for spatially and temporally varying traffic.

Exploiting Context-Awareness for Secure Spectrum Trading in Multi-Hop Cognitive Cellular Networks

In this paper, we consider context-awareness to enhance route reliability and robustness in multi-hop cognitive networks. A novel context-aware route discovery protocol is presented to enable secondary users to select the route according to their QoS requirements. The protocol facilitates adjacent relay selection under different criteria, such as shortest available path, route reliability and relay reputation. New routing and security-based metrics are defined to measure route robustness in spatial, frequency and temporal domains. Secure throughput, defined as the percentage of traffic not being intercepted in the network, is provided. The resources needed for trading are then obtained by jointly optimizing secure throughput and trading price. Simulation results show that when there is a traffic imbalance of factor 4 between the primary and secondary networks, 4 channels are needed to achieve 90% link reliability and 99% secure throughput in the secondary network. Besides, when relay reputation varies from 0.5 to 0.9, a 20% variation in the required resources is observed.

Modeling dynamics of complex wireless networks

In this paper, we present a unified model for the analysis of a number of wireless network characteristics leading to the existence of uncertain links. The probabilistic characterization of the link uncertainty enables the random graph representation of the network topology and use of existing tools of complex networks in the system analysis. In the analysis, the multi-hop concept is adopted to model future networks with dense user population and enable mobile to mobile (m2m) connections. The potential users acting as relays may belong to different operators and as such may or may not want to cooperate. Consequently, the existence of those links will be uncertain. Some subareas of the cell will be covered by other technologies such as femto cell or WLAN enabling the possibility for the cellular system to offload the traffic. The existence of those links depends on the relaying distance and coverage of the WLAN, as well as the cooperation agreement between the operators. Our model includes also the possibility to consider green networks where the link uncertainty results from the effort to prevent the radiations from the terminal towards the user. In addition, security requirements may not accept every available relay. For this reason, the existence of the link, even when physically present, will be also uncertain. In such a complex network, cognitive links are also available with limited certainty due to unpredictable activity of the primary user. The uncertain and time varying existence of the links and nodes results into a Dynamic Network Architecture (DNA). AU these characteristics of the network are incorporated in a unified model enabling a tractable analysis of the overall system performance.

Heterogeneous millimeter-wave/micro-wave architecture for 5G wireless access and backhauling

5G is expected to provide a unified platform where a number of different frequency bands and technologies are strategically integrated and combined. In this paper we investigate the potential of combining micro-wave (microWave) and millimeter-wave (mmWave) technologies in outdoor scenarios. We first envision the design and architecture of a novel 5G microWave/mmWave Heterogeneous Network (HetNet) where the mmWave backhaul is integrated. Next, we discuss a Service-Driven Dynamic Resource Radio Management system for the proposed architecture and propose a Multi-Layer Dynamic Transmission Scheme, which enables cooperation between different network slices, increasing the degrees of freedom of the overall system. Finally, we present a preliminary analytical and experimental study of the performance of the proposed 5G microWave/mmWave HetNet, highlighting the significant benefits that a mmWave network achieves with flexible, dynamic support from microWave technologies.

A Microeconomic Approach to Data Trading in User Provided Networks

In this paper, we consider a novel cellular network connection paradigm, known as user-provided network (UPN), where users share their connectivity with others. Motivated by the recently launched traded data plans, where Wireless Service Providers allow users to sell and buy leftover data capacities (caps) from each other, we have developed a dynamic pricing scheme for such a network. Optimal prices for the volume of data traded are derived from market equilibrium. The proposed pricing scheme provides a global data market reference price, trading price for local data markets, considering user connectivity requirements, and price recovery. An analysis of UPN based on microeconomics is presented to provide insights into how traffic dynamics affect data volume demand and supply and thus, trading prices. The stability of the market equilibrium is also analyzed. Numerical results show that our scheme incentivizes user participation in UPNs by dynamically adapting the price to data volume demand and supply, and also provides high returns to both buyers and sellers.