High-fidelity and time-driven simulation of large wireless networks with parallel processing

It has been a challenge to develop efficient algorithms for relay selection and relay power optimization in arbitrarily large multi-hop wireless networks. In this paper, considering Gaussian networks with half-duplex decode-and-forward relays, we develop a practical network-wide signal processing procedure with which the relay's data rate is not degraded by mutual interference. Multi-hop relaying is thus immune to mutual interference. Then, we develop an algorithm to find approximately the optimal hop count and the optimal relays for source-destination transmission rate maximization. With a quadratic complexity O(N2), where N is the network size, this algorithm is efficient for arbitrarily large wireless networks. More interestingly, this algorithm is similar to the well-known Dijkstra's algorithm of wired networks.

This paper investigates the potential gain of cooperation in large wireless networks with multiple sources and relays, where the nodes form an homogeneous Poisson point process. The source nodes may choose their nearest neighbor from the set of inactive nodes as their relay. Although cooperation can potentially lead to significant improvements on the asymptotic error probability of a communication pair, relaying causes additional interference in the network, increasing the average noise. We address the basic question: how should source nodes optimally balance cooperation vs. interference to guarantee reliability in all communication pairs. Based on the decode-and-forward (DF) scheme at the relays, we derive closed-form approximations to the upper bounds on the error probability, averaging over all node positions. Surprisingly, in the small outage probability regime, there is an almost binary behavior that dictates — depending on network parameters — the activation or not of all relay nodes.

This paper investigates the speed limit of information propagation in large wireless networks, which provides fundamental understanding of the fastest information transportation and delivery that a wireless network is able to accommodate. We show that there is a unified speed upper bound for broadcast and unicast communications in large wireless networks. When network connectivity and successful packet delivery are considered, this speed upper bound is a function of node density. As this bound is unreachable with finite node density, we also quantify the gap between the actually achieved speed and the desired upper bound, which converges to zero exponentially as the node density increases to infinity.

In this paper we address the evaluation of speech quality through a wireless network as perceived by the user. User perceived evaluation (in addition to the usual network metrics including delay, throughput, packet loss statistics, etc.) is critical in the design of wireless multimedia networks where speech and video play a key role and are affected by several factors, such as network performance and encoding scheme. We use two approaches for our evaluation: small scale testbed implementation and hybrid simulation. The testbeds are implemented either by UDP (user datagram protocol) connection or UCLA talk connection. The hybrid simulation is event driven and is based on the Maisie parallel simulation language. It uses speech traces for inputs and plays back the speech outputs off-line. There are several advantages in using the simulation tool, mainly from the point of view of scalability and new protocol feature testing. At the same time, the real life testbed provides verification of the simulator assumptions, albeit on a small scale.

The evaluation and assessment of the design of large wireless networks is an important problem in numerous applications. Direct simulation is a traditional approach for studying such networks but is severely limited in its utility as the size of the network increases. This necessitates other means for studying large networks, one of which is the modeling of large networks with continuum models. In this paper, we introduce nonlinear partial differential equations whose solutions approximate the expected behavior of large networks governed by probabilistic communication rules. The relative speed at which solutions can be obtained from the continuum models allows for the investigation of routing protocols and communication limitations due to interference—a feat that is not feasible via simulation methods. Specifically, we investigate the effects of a directed diffusion routing protocol and explore communication limitations in an interference-sensitive network. Network design studies using the approximating continuum models are then presented.

This paper investigates the application of multi-agent reinforcement learning (MARL) algorithm to solve the joint spectrum and power allocation problem (JSPAP) in wireless network. The objective of JSPAP is to optimize the subband selection and transmit power levels for links, with the aim of maximizing the sum-rate utility function. To address the JSPAP with discrete subband selection and continuous power allocation, most existing algorithms rely on a centralized optimizer and the instantaneous global channel state information, which can be challenging to implement in large wireless networks with time-varying subbands. To conquer such limitation, a two-stage MARL algorithm is proposed, which comprises a top layer network for selecting subbands across all links and a bottom layer network for determining the transmit power levels for all transmitters. By utilizing the value decomposition technique in the top layer network, the links can cooperatively select transmission subbands, effectively resolving non-stationarity issues in wireless network. Additionally, in the bottom layer network of the proposed two-stage MARL algorithm, each transmitter selects the transmit power level based solely on the local information, thereby effectively reducing computational burden. Empirical experiments demonstrate the effectiveness of the proposed two-stage MARL algorithm by comparison with the state-of-the-art RL algorithms and fractional programming algorithms.

We consider the problem of joint routing, scheduling and power control in multi-hop wireless networks. We use a linear relation between link capacity and signal to interference noise ratio in our formulation. In a previous work, using a duality approach, the optimal link scheduling and power control that minimizes the total average transmission power is found. We formulate this problem as a linear programming problem with exponential number of constraints. To cope with the exponential number of constraints, we propose an iterative algorithm based on the cutting plane method. The separation oracle for the cutting plane algorithm turns out to be an element-wise concave optimization problem that can be effectively solved using branch and bound algorithm. We extend the same method to find the optimal routing scheduling and power control. Simulation results show that this methodology is more efficient and scalable compare to the previously proposed algorithm.

In this paper, we consider CSMA policies for scheduling packet transmissions in multihop wireless networks with one-hop traffic. The main contribution of the paper is to propose a novel CSMA policy, called Unlocking CSMA (U-CSMA), that enables to obtain both high throughput and low packet delays in large wireless networks. More precisely, we show that for torus interference graph topologies with one-hop traffic, U-CSMA is throughput optimal and achieves order-optimal delay. For one-hop traffic, the delay performance is defined to be order-optimal if the delay stays bounded as the network-size increases. Simulations that we conducted suggest that (a) U-CSMA is throughput-optimal and achieves order-optimal delay for general geometric interference graphs and (b) that U-CSMA can be combined with congestion control algorithms to maximize the network-wide utility and obtain order-optimal delay. To the best of our knowledge, this is the first time that a simple distributed scheduling policy has been proposed that is both throughput/utility optimal and achieves order-optimal delay.

Distributing data aggregation through multiple access channel (MAC) has been challenging in large wireless networks. In order to tackle the challenge, a computing over the MAC (CP-MAC) scheme has been proposed as a promising communication-computation integrated way for function-centric networks. In this paper, we analyze the performance of the CP-MAC scheme, compared with the traditional communication-computation separated way, i.e., a communicating over the MAC (CM-MAC) scheme. Function-centric wireless networks are considered, where the fusion center (FC) does not need the individual data of each node but only the target function. We begin with the ideal uniform-MAC scenarios, where the CP-MAC scheme is always better than the CM-MAC scheme. Then, practical non-uniform MAC scenarios are studied for both homogeneous networks with Rayleigh fading and heterogeneous networks with a different path loss. Closed-form expressions of the achievable function rate are provided using the asymptotic theory of ordered statistics. It is found that the CP-MAC scheme is not always superior to the CM-MAC scheme. Simulation results are provided to verify and illustrate our derived results.

Physical layer secrecy in wireless networks in the presence of eavesdroppers of unknown location is considered. In contrast to prior schemes, which have expended energy in the form of cooperative jamming to enable secrecy, we develop schemes where multiple transmitters send their signals in a cooperative fashion to confuse the eavesdroppers. Hence, power is not expended on "artificial noise"; rather, the signal of a given transmitter is protected by the aggregate interference produced by the other transmitters. We introduce a two-hop strategy for the case of equal path-loss between all pairs of nodes, and then consider its embedding within a multi-hop approach for the general case of an extended network. In each case, we derive an achievable number of eavesdroppers that can be present in the region while secure communication between all sources and intended destinations is ensured.

Future generation of wireless networks will solicit the cooperation of heterogeneous Radio Access Networks (RANs) in order to provide multimedia traffic to different classes of users with varying Quality of Service (QoS) requirements over regions and time zones. The use of such RANs as subsystems of a larger wireless network, i.e, a multi-access network, claims for an Access Selection (AS) scheme to coordinate the load sharing so as to meet the optimum combined network performance. Depending on the individual subsystem capacities, the traffic load conditions and the subsystem channel quality, an intelligent AS algorithm can provide capacities beyond the sum of the subsystems capacities. This paper addresses the issue of the AS in the context of multi-access networks. We intend to provide an analytical formulation which indicates the suitable/unsuitable scenarios for employment of an intelligent AS strategy. The usability of the proposed qualitative studies are demonstrated by means of simulations.

Objectives: Public key cryptography helps in achieving information security in wireless networks. Public key will be issued by CA (Certificate Authority). But having CA in wireless network makes it vulnerable to various attacks. Hence distributed cache was proposed where each node in network caches public key of some nodes existing in network in its cache and request to obtain keys not cached will be made via trusted nodes. Due to limited memory only few keys can be cached. There exists an optimal threshold value for caching keys so that equal number of both local nodes and remote nodes public keys can be cached. Methods: Random replacement policy was used for updating cache contents, where if a node’s local/ remote key cached ratio exceeds optimal ratio then a key will be randomly chosen from the cache and will be removed. Here we propose implementation of bloom filter in cache to cache public key of nodes existing in network. Findings: By using bloom filter of large size, more number of public keys can be cached thereby providing space and time advantage and reduces query cost with increased usage of memory. MAC (Media Access Control) id and PSN (Processor Serial Number) are used for uniquely identifying each node in network and are stored in encrypted format. When mismatch is found in encrypted MAC id and PSN, then that node is determined to be malicious node and its id will be notified to all nodes in network thus excluding the node from making any communication to other nodes in the network. Applications: Used for checking an element’s presence in the set thus reducing I/O lookup when large data set is used. Used as an alternative to cache as it uses limited memory more efficiently than cache does.

Mobile Backbone Wireless Networks (MBWN) [10] are wireless networks in which the base stations are mobile. Our strategy is the following: mobile nodes are dynamically grouped into clusters of bounded radius. In the very large wireless networks we deal with we deal with, several hundreds of clusters may be generated. Clustering makes use of a two dimensional Euclidean version of the Antipole Tree data structure [5]. This very effective structure was originally designed for finite sets of points in an arbitrary metric space to support efficient range searching. It requires only a linear number of pair-wise distance calculations among nodes. Mobile Base Stations occupy an approximate centroid of the clusters and are moved according to a fast practical bipartite matching algorithm which tries to minimize both total and maximum distance. We show that the best known computational geometry algorithms [1] become infeasible for our application when a high number of mobile base stations is required. On the other hand our proposed 8% average error solution requires O (k log k) running time instead of the approximatively O (k2) exact algorithm [1]. Communication among nodes is realized by a Clusterhead Gateway Switching Routing (CGSR) protocol [15] where the mobile base stations are organized in a suitable network. Other efficient clustering algorithms [11, 17] may be used instead of the Antipole Tree. However the nice hierarchical structure of the Antipole Tree makes it applicable to other types of mobile wireless (Ad-Hoc) and wired networks but this will be subject of future work.

In the development of large wireless networks, scaling law studies can provide fundamental insights. For example, is it possible to build an arbitrarily large wireless network without a wired infrastructure while maintaining a constant communication rate for each user? This is equivalent to asking if a linear scaling law is achievable for wireless networks. Whether too ambitious a goal or not, this question has attracted intensive research but still remains open. Among many proposals, the hierarchical scheme is impressive in exploiting the MIMO gain with a bootstrapping strategy. In this paper, a careful analysis of the hierarchical scheme exposes the potential influence of the pre-constant in deriving scaling laws. It is found that a modified hierarchical scheme can achieve a throughput up to an arbitrary factor higher than the original one, although it is still short of linear scaling. This study demonstrates the essential importance of the throughput formula itself, rather than the scaling laws consequently derived.

We consider the capacity problem in large wireless multihop networks by separating the problem into macroscopic and microscopic level subproblems. At the macroscopic level, the task is to determine the routes that are geometric curves. At the microscopic level, we need to forward the traffic according to its directional distribution that results from the macroscopic level routes. Previous studies have treated the macroscopic level problem simply as that of load balancing, implying the use of time sharing to serve the traffic flowing in different directions. We study how a scheduling that truly interleaves the traffic flows in different directions affects the macroscopic level problem. We restrict the macroscopic routes to certain simple path sets. This together with earlier results on the microscopic level forwarding capacity allows us to obtain new results on the macroscopic level capacity that demonstrate the gains from multidirectional forwarding.