Cross-layer Optimization for Wireless Networks with Deterministic Channel Models

Cross-layer optimization (physical, data link and network layers) in wireless networks has been limited by its combinatorial complexity (Toumpis and Goldsmith, 2003). Existing literature has traditionally been able to provide numerical analysis only for networks with less than 8 nodes (Madan et al., 2005), limiting our ability to understand the benefits of cross-layer optimization for more realistic-sized networks. In Barreto (2004) and Johansson and Xiao (2006) the authors showed that column generation could be used to reduce the complexity of the problem, with Johansson and Xiao (2006) providing in-depth analysis for 10-node networks and limited results for 20-node networks - all with minimum SNR and transmission rate restrictions. In this paper we show how decomposition methods can be used for the joint optimization of the physical (rate and power control), data link (MAC/scheduling), and networking (routing) layers, increasing the size of networks that can be analyzed. We present exact numerical solutions for ad-hoc networks with up to 30 nodes and mesh networks with up to 40 nodes, a significant scalability improvement. We continue by showing how bounded solutions for larger networks can be obtained with decreased computational complexity and present to heuristic implementations that increase the analysis to arbitrary-sized networks (Barreto and Chiu, 2006). We show how decomposition methods effectively decouple routing from the MAC/physical layers and allow us to solve the problems independently while reaching a global optimal solution.

Cyber-Physical System (CPS) is envisioned to tightly integrate the cyber-world of computation, communication, and control with the physical world. CPS is typically designed as a networked system of interacting sensors, actuators, and embedded computing devices to monitor and control the physical world. Thus, one of the essential building blocks of such a system is a highly efficient networking infrastructure. In this paper, we aims to develop an efficient wireless networking technology which can be utilized in CPS. More specifically, we develop a cross-layer optimization model based on the Network Utility Maximization (NUM) framework and its distributed solution for wireless multihop multicast networks exploiting multi-user diversity. It is known that the capacity of a wireless network can be increased by exploiting different channel conditions at different users, i.e., multi-user diversity; however, it is yet to be determined how much performance gain can be achieved by exploiting multi-user diversity in wireless multihop multicast networks. To address this problem, we extend the NUM framework and derive a new optimization problem including the benefits of multi-user diversity for multicasting scenarios in wireless multihop networks under a probabilistic media access control (MAC). In our problem, multi-user diversity is achieved via opportunistic scheduling. Then, we propose a distributed approximation algorithm for the problem. Our numerical results confirm that the benefit of multi-user diversity is prominent in a wireless multihop network with multicast flows.

Since the initial publication of Kelly et al.'s seminal paper on resource allocation in wired networks, a large number of studies based on the network utility maximization (NUM) framework have been conducted, for example, on resource allocations in wired networks as well as wireless networks. Congestion control, used to adapt the traffic rates injected into a network, can be controlled separately at the transport layer or mutually with other layers, using a cross-layer design. The focus of this paper is a review of the research following the NUM framework and addressing the problem of congestion control from several dimensions, specifically, elastic traffic or inelastic traffic, same-timescale or multi-timescale, and single-path or multipath transmissions. First, cross-layer designs for wireless networks, basics of optimization theory, and an example of cross-layer congestion control are summarized. Then, studies on NUM-based congestion control are sequentially presented from several points of view. In addition, we survey the recent literature on cross-layer resource allocation in next generation networks, software-defined networking, and cloud radio access networks, which can enhance the performances and address issues found in current networks. Finally, we outline what we consider to be the current challenges and open issues in the area of NUM-based congestion control for wireless networks.

Interference alignment is a key technique for communication scenarios with multiple interfering links. In several such scenarios, interference alignment was used to characterize the degrees of freedom of the channel. However, these degree-of-freedom capacity approximations are often too weak to make accurate predictions about the behavior of channel capacity at finite signal-to-noise ratios (SNRs). The aim of this paper is to significantly strengthen these results by showing that interference alignment can be used to characterize capacity to within a constant gap. We focus on real, time-invariant, frequency-flat X-channels. The only known solutions achieving the degrees of freedom of this channel are either based on real interference alignment or on layer-selection schemes. Neither of these solutions seems sufficient for a constant-gap capacity approximation. In this paper, we propose a new communication scheme and show that it achieves the capacity of the Gaussian X-channel to within a constant gap. To aid in this process, we develop a novel deterministic channel model. This deterministic model depends on the 1/2 log (SNR) most-significant bits of the channel coefficients rather than only the single most-significant bit used in conventional deterministic models. The proposed deterministic model admits a wider range of achievable schemes that can be translated to the Gaussian channel. For this deterministic model, we find an approximately optimal communication scheme. We then translate this scheme for the deterministic channel to the original Gaussian X-channel and show that it achieves capacity to within a constant gap. This is the first constant-gap result for a general, fully-connected network requiring interference alignment.

The growing popularity for video based applications has put an enormous strain on the network and causes the network to be more prone to congestion. The study of congestion control and bandwidth allocation problems is often formulated into Network Utility Maximization (NUM) framework, and the existing solutions for NUM generally focus on single-layered applications. However, today's quality of video is divided into several layers, where each layer provides different level of enhancement of quality. In this paper, we study how multi-layered video based applications impact network performance and pricing through NUM formulation, in particularly traffic from video streaming. In our investigation, we design and implement a new multi-layered user utility model that leverages on studies of human visual perception. Then, using this new utility model to examine network activities, we demonstrate that solving NUM with multi-layered utility is intractable, and that rate allocation and network pricing may oscillate due to user behavior specific to multi-layered applications. To address this, we propose a new approach for admission control to ensure quality of service and experience.

In this letter we address the problem of users' desired trade-off between optimal throughput and delay in wireless multihop networks under the framework of Network Utility Maximization (NUM). Via dynamic utility design and proper adaptation of the constraint set of the NUM problem, the impact of users' delay sensitivity on the resulting optimal throughput is considered. A cross-layer policy is proposed-stemming from Lyapunov drift techniques-that performs routing, scheduling and congestion control optimizing the above trade-off. Via modeling, analysis and simulations the performance of the proposed problem formulation and policy is evaluated.

An interference alignment example is constructed for the deterministic channel model of the <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> -user interference channel. The deterministic channel example is then translated into the Gaussian setting, creating the first known example of a fully connected Gaussian <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> -user interference network with single antenna nodes, real, nonzero and constant channel coefficients, and no propagation delays where the degrees of freedom outerbound is achieved. An analogy is drawn between the propagation delay based interference alignment examples and the deterministic channel model which also allows similar constructions for the two-user <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</i> channel as well.

This paper describes user location and tracking on indoor scenarios based on the pattern recognition of IEEE 802.11 signal strength measurements by means of a fuzzy inference system. Signal fluctuation makes difficult and imprecise the trilateration of the user location, so we propose the use of fuzzy techniques to deal with such imprecision. To implement the location system, deterministic wireless channel characterisation is obtained by means of in–house implemented 3D ray launching code. Several scenarios are analysed, since their size strongly impacts on algorithm convergence. The limitations and solution, based on subdivision of the scenario in sub–zones, are analysed and applied to the case of a new emergency hospital complex in Navarra. Simulation and measurement results are presented showing good agreement. The results can aid in the determination of an optimised WiFi–based location system providing efficient positioning and communication services in complex indoor scenarios.

A spatio-temporal (ST) channel measurement carried out at 8.45 GHz is reported, which is purposed to determine the ST propagation characteristics in urban micro- and pico-cell environments. A wideband channel sounder and a directional parabolic antenna are employed to measure the ST channel parameters. A deconvolution technique is utilized and proved to be effective to obtain unambiguous spatial channel profiles. A random-phase-assisted hybrid 2D-3D ray-tracing model is used to predict the ST channel parameters and the statistical distribution of angular profiles. The verification between ray-tracing and measurement demonstrated that ray-tracing approaches are appropriate for deterministic ST channel modeling in urban micro- and pico-cell environments.

Cross-layer design can significantly improve the performance of mobile ad-hoc networks (MANETs), as indicated by the flurry of recent results in the literature. Much of this work stems from the Kelly network utility maximization (NUM) framework, where convexity is crucial for developing algorithms that reach the global optimum. Unfortunately many problems are nonconvex in nature, so convex approximations are abundant. In this paper, we consider the joint optimization of source data-rates and link transmitter powers in a MANET, specifically dealing with the statistical variations of the wireless channel. In this paradigm we show that the commonly applied high-SIR convex approximation is unrealistic, so we seek to find solutions of the unmodified NUM problem. Our first result shows that the canonical formulation (previously thought to be nonconvex) is indeed a convex problem for logarithmic TCP-Vegas utilities; we then derive an algorithm reaching the global optimum. Our main result caters for the general case of strictly concave utilities, where we derive an algorithm that provably converges to the global solution of the underlying nonconvex NUM problem.

We consider a group of mobile users, within proximity of each other, who are interested in watching the same online video. The common practice today is that each user downloads the video independently on her mobile device using her own cellular connection, which wastes access bandwidth and may also lead to poor video quality. We propose a novel cooperative system where each mobile device uses simultaneously two network interfaces: (i) cellular to connect to the video server and download parts of the video and (ii) WiFi to connect locally to all other devices in the group to exchange those parts. Devices cooperate to efficiently utilize all network resources and to adapt to varying wireless network conditions. In the local WiFi network, we exploit overhearing, which we further combine with network coding. The end result is savings in cellular bandwidth and improved user experience. We follow a complete approach, from theory to practice. First, we formulate the problem using a network utility maximization (NUM) framework, decompose the problem, and provide a distributed solution. Then, based on the structure of the NUM solution, we design a system called MicroCast, and we implement a prototype as an Android application. We provide both simulation results of the NUM solution and experimental evaluation. We demonstrate that the proposed approach brings significant performance benefits (namely, faster download on the order of the group size) without battery penalty.

In this paper, we consider lossy mobile ad hoc networks where the data rate of a given flow becomes lower and lower along its routing path. One of the main challenges in lossy mobile ad hoc networks is how to achieve the conflicting goal of increased network utility and reduced power consumption, while without following the instantaneous state of a fading channel. To address this problem, we propose a cross-layer rate-effective network utility maximization (RENUM) framework by taking into account the lossy nature of wireless links and the constraints of rate outage probability and average delay. In the proposed framework, the utility is associated with the effective rate received at the destination node of each flow instead of the injection rate at the source of the flow. We then present a distributed joint transmission rate, link power and average delay control algorithm, in which explicit broadcast message passing is required for power allocation algorithm. Motivated by the desire of power control devoid of message passing, we give a near-optimal power-allocation scheme that makes use of autonomous SINR measurements at each link and enjoys a fast convergence rate. The proposed algorithm is shown through numerical simulations to outperform other network utility maximization algorithms without rate outage probability/average delay constraints, leading to a higher effective rate, lower power consumption and delay. Furthermore, we conduct extensive network-wide simulations in NS-2 simulator to evaluate the performance of the algorithm in terms of throughput, delay, packet delivery ratio and fairness.

Crowdsourced image processing has the potential to vastly impact response timeliness in various emergency situations. Because images can provide extremely important information regarding an event of interest, sending the right images to an analyzer as soon as possible is of crucial importance. In this paper, we consider the problem of optimally assigning resources, both local (CPUs in phones) and remote (network-based GPUs) to mobile devices for processing images, ultimately sending those of interest to a centralized entity while also accounting for the energy consumption. To that end, we use the Network Utility Maximization (NUM) framework, coupled with a hit-ratio estimator and energy costs, to enable a distributed implementation of the system. Our results are validated using both synthetic simulations and real-life traces.

In some modern communication systems, as it is the case of WiMAX, it has been decided to implement Demand Assignment Multiple Access (DAMA) solutions. End-users request transmission opportunities before accessing the system, which provides an efficient way to share system resources. In this paper, we briefly review the PHY and MAC layers of an OFDMA-based WiMAX system, and we propose to use a Network Utility Maximization (NUM) framework to formulate the DAMA strategy foreseen in the uplink of IEEE 802.16. Utility functions are chosen to achieve fair solutions attaining different degrees of fairness and to further support the QoS requirements of the services in the system. Moreover, since the standard allocates resources in a terminal basis but each terminal may support several services, we develop a new decomposition technique, the coupled-decompositions method, that obtains the optimal service flow allocation with a small number of iterations (the improvement is significant when compared to other known solutions). Furthermore, since the PHY layer in mobile WiMAX has the means to adapt the transport capacities of the links between the Base Station (BS) and the Subscriber Stations (SSs), the proposed PHY-MAC cross-layer design uses this extra degree of freedom in order to enhance the network utility.

In this paper, we introduce a model for Multiple-Input Multiple-Output (MIMO) Space Division Multiple Access (SDMA) into the analysis of a multi-hop millimeter wave network under the classic Network Utility Maximization (NUM) framework with Maximum Back Pressure scheduling (MBP). We show that the proof of convergence of MBP remains valid when we allow the scheduler to select multiple links to the same receiver in the same frame. Conventional MBP with a single link per receiver is traditionally implemented using the Maximum Weighted Matching (MWM) algorithm over the network graph. Under our modification, the problem becomes a Maximum Weighted Partition of the graph. Message Passing (MP) algorithms are efficient and have been successfully applied to graph partitioning problems in the past, so we use one to approximate the optimal MBP scheduling. Through simulation over a randomized mmWave picocell, we compare the MWM reference without SDMA, the efficient MP approximation, and the exact optimal MBP scheduler with SDMA (obtained by brute force). Simulations show that by leveraging SDMA in multi-hop mmWave network scheduling, a 50% capacity increase is obtained on average.