Stefan Valentin

Simulating wireless and mobile networks in OMNeT++ the MiXiM vision

Wireless communication has attracted considerable interest in the research community, and many wireless networks are evaluated using discrete event simulators like OMNeT++. Although OMNeT++ provides a powerful and clear simulation framework, it lacks of direct support and a concise modeling chain for wireless communication. Both is provided by MiXiM. MiXiM joins and extends several existing simulation frameworks developed for wireless and mobile simulations in OMNeT++. It provides detailed models of the wireless channel (fading, etc.), wireless connectivity, mobility models, models for obstacles and many communication protocols especially at the Medium Access Control (MAC) level. Further, it provides a user-friendly graphical representation of wireless and mobile networks in OMNeT++, supporting debugging and defining even complex wireless scenarios. Though still in development, MiXiM already is a powerful tool for performance analysis of wireless networks. Its extensive functionality and clear concept may motivate researches to contribute to this open-source project [4].

When cellular meets WiFi in wireless small cell networks

The deployment of small cell base stations, SCBSs, overlaid on existing macrocellular systems is seen as a key solution for offloading traffic, optimizing coverage, and boosting the capacity of future cellular wireless systems. The next generation of SCBSs is envisioned to be multimode (i.e., capable of transmitting simultaneously on both licensed and unlicensed bands). This constitutes a cost-effective integration of both WiFi and cellular radio access technologies that can efficiently cope with peak wireless data traffic and heterogeneous quality of service requirements. To leverage the advantage of such multimode SCBSs, we discuss the novel proposed paradigm of cross-system learning by means of which SCBSs self-organize and autonomously steer their traffic flows across different RATs. Cross-system learning allows the SCBSs to leverage the advantage of both the WiFi and cellular worlds. For example, the SCBSs can offload delay-tolerant data traffic to WiFi, while simultaneously learning the probability distribution function of their transmission strategy over the licensed cellular band. This article first introduces the basic building blocks of cross-system learning and then provides preliminary performance evaluation in a Long-Term Evolution simulator overlaid with WiFi hotspots. Remarkably, it is shown that the proposed cross-system learning approach significantly outperforms a number of benchmark traffic steering policies.

Context-Aware Small Cell Networks: How Social Metrics Improve Wireless Resource Allocation

In this paper, a novel approach for optimizing resource allocation in wireless small cell networks (SCNs) with device-to-device (D2D) communication is proposed. The proposed approach allows jointly exploiting the wireless and social context of wireless users for optimizing the overall allocation of resources and improving the traffic offload in SCNs. This context-aware resource allocation problem is formulated as a matching game, in which user equipments (UEs) and resource blocks (RBs) rank one another, based on utility functions that capture both wireless and social metrics. Due to social interrelations, this game is shown to belong to a class of matching games with peer effects. To solve this game, a novel self-organizing algorithm is proposed, using which UEs and RBs can interact to decide on their desired allocation. The proposed algorithm is then proven to converge to a two-sided stable matching between UEs and RBs. The properties of the resulting stable outcome are then studied and assessed. Simulation results using real social data show that clustering of socially connected users allows offloading a substantially larger amount of traffic than the conventional context-unaware approach. These results show that exploiting social context has high practical relevance in saving resources on wireless links and in the backhaul.

Energy-Efficient Adaptive Video Transmission: Exploiting Rate Predictions in Wireless Networks

The unprecedented growth of mobile video traffic is adding significant pressure to the energy drain at both the network and the end user. Energy-efficient video transmission techniques are thus imperative to cope with the challenge of satisfying user demand at sustainable costs. In this paper, we investigate how predicted user rates can be exploited for energy-efficient video streaming with the popular Hypertext Transfer Protocol (HTTP)-based adaptive streaming (AS) protocols [e.g., dynamic adaptive streaming over HTTP (DASH)]. To this end, we develop an energy-efficient predictive green streaming (PGS) optimization framework that leverages predictions of wireless data rates to achieve the following objectives: 1) Minimize the required transmission airtime without causing streaming interruptions; 2) minimize total downlink base station (BS) power consumption for cases where BSs can be switched off in deep sleep; and 3) enable a tradeoff between AS quality and energy consumption. Our framework is first formulated as mixed-integer linear programming (MILP) where decisions on multiuser rate allocation, video segment quality, and BS transmit power are jointly optimized. Then, to provide an online solution, we present a polynomial-time heuristic algorithm that decouples the PGS problem into multiple stages. We provide a performance analysis of the proposed methods by simulations, and numerical results demonstrate that the PGS framework yields significant energy savings.

Cooperative Wireless Networking Beyond Store-and-Forward

In future wireless networks devices may cooperate to form logical links. Each of these links may consist of several independent physical channels which are shared by the cooperating partners. Even without multiple antennas this cooperation provides diversity in time and space. This so-called cooperation diversity increases the robustness of the link vs. fading and interference. After surveying approaches in cooperation diversity we focus on optimizing its performance by combining several cooperation schemes and by integrating cooperation into space-time coding. For multiple scenarios, we further discuss the factors and benefits introduced by user cooperation and how cooperation-aware resource allocation can be employed to further increase the performance of cooperative networks. When it comes to implementation, the question arises how cooperation can be integrated efficiently into existing wireless networks. A case study for 802.11-based WLANs reveals the issues that need to be solved in order to deploy cooperative techniques. We provide an overview of the state of the art in implementing cooperation approaches, analyze how appropriate these approaches solve the issues, and, where appropriate, point out their deficiencies. We conclude with a road map for future research necessary to tackle these deficiencies for the practical implementation of cooperation in next generation mesh, WLAN, WMAN, and cellular standards.

Context-aware resource allocation for cellular wireless networks

Current cellular networks are often overloaded by Smartphone traffic, while the users’ Quality of Service (QoS) demands are not met. To cope with this problem, we demonstrate a new radio resource management approach. With Context-Aware Resource Allocation, the base station’s scheduler (i) observes Context Information (CI) from the user’s environment and (ii) utilizes this knowledge for an efficient throughput-delay tradeoff. After introducing our framework for accessing CI from the handheld’s applications and operating system, we use time-utility functions to develop a practical scheduling algorithm. Studying this heuristic under various traffic assumptions shows that our context-aware scheduler can support three times the load of proportional fair scheduling, at equal capacity and utility. Thus, even a small degree of CI increases the wireless links’ efficiency without sacrificing the users’ QoS.

Optimal Predictive Resource Allocation: Exploiting Mobility Patterns and Radio Maps

Resource Allocation (RA) in cellular networks is a challenging problem due to the demanding user requirements and limited network resources. Moreover, mobility results in channel gains that vary significantly with time. However, since location and received signal strength are correlated, user mobility patterns can be exploited to predict the data rates they will experience in the future. In this paper, we show that with such predictions, long-term RA plans that span multiple cells can be made. We formulate an optimal Predictive Resource Allocation (PRA) framework for a network of cells as a linear programming problem for three different objectives. Presented numerical results provide a benchmark of the PRA performance in realistic and random user mobility scenarios. Significant network and user satisfaction gains are observed compared to RA schemes that do not utilize any predictions.

Integrating Multiuser Dynamic OFDMA into IEEE 802.11 WLANs - LLC/MAC Extensions and System Performance

Multiuser dynamic OFDMA for the downlink has been extensively studied, specifically, in terms of fast close-to- optimal subcarrier allocation heuristics and the efficient representation of signaling information. Although these functions provide the fundament of dynamic OFDMA, a complete multiuser OFDMA system requires more functionality. Focusing on WLAN systems, we discuss such additional functionality required for enabling the IEEE 802.11 link and Medium Access Control (MAC) sublayer to leverage OFDMA advantages. We identify necessary extensions, study the resulting overhead, and introduce a lightweight design for a complete dynamic OFDMA IEEE 802.11a system. Studying its performance shows that with our lightweight integration dynamic OFDMA can improve IEEE 820.11a UDP throughput by up to 154% and UDP latency by up to 63% even if the full overhead is taken into account.

Design and Evaluation of a Routing-Informed Cooperative MAC Protocol for Ad Hoc Networks

Cooperative relaying has been shown to provide diversity gains which can significantly improve the packet error rate (PER) in wireless transmissions. In ad hoc wireless routing where packets may travel over a number of hops before reaching the destination, hop-wise cooperative relaying may severely reduce network capacity. This approach was mainly addressed in literature so far. In this paper, we efficiently apply cooperative relaying along a complete path and over multiple hops at the same time. We use information from the routing layer to improve the medium access control (MAC) layers performance. Simulations and testbed implementation show appealing gains through diversity resulting in up to 66% better PER performance and up to 148% goodput increase compared to conventional approaches.

Kernel-Based Adaptive Online Reconstruction of Coverage Maps With Side Information

In this paper, we address the problem of reconstructing coverage maps from path-loss measurements in cellular networks. We propose and evaluate two kernel-based adaptive online algorithms as an alternative to typical offline methods. The proposed algorithms are application-tailored extensions of powerful iterative methods such as the adaptive projected subgradient method (APSM) and a state-of-the-art adaptive multikernel method. Assuming that the moving trajectories of users are available, it is shown how side information can be incorporated in the algorithms to improve their convergence performance and the quality of the estimation. The complexity is significantly reduced by imposing sparsity awareness in the sense that the algorithms exploit the compressibility of the measurement data to reduce the amount of data that is saved and processed. Finally, we present extensive simulations based on realistic data to show that our algorithms provide fast and robust estimates of coverage maps in real-world scenarios. Envisioned applications include path-loss prediction along trajectories of mobile users as a building block for anticipatory buffering or traffic offloading.