Network Throughput Improvement in Cognitive Networks by Joint Optimization of Spectrum Allocation and Cross-layer Routing


This paper introduces innovative technology development that will improve performance of next-generation cognitive wireless networking among space, air, and ground assets. The paper describes methods to develop a system where cognitive users transmit wideband spread-spectrum signals that are designed to adaptively avoid the interference dynamics of the available spectrum at the receiver. These technological advances can achieve improvement in network throughput, delay, and reliability. Theoretical performance expectations are given. The theoretical approach is translated to the construction of specific software techniques and their implementation within the cross-layer wireless communications architecture. The hardware/software testbed that simulates a dynamic Ad Hoc Software Defined Radio (SDR) network is described, and a series of tests to measure the value of the optimization techniques is given. Preliminary test results and the total expected performance improvements are shown. 1.0 INTRODUCTION Cognitive radio networks have emerged as a promising technology to improve the utilization efficiency of the existing radio spectrum. However, in a radio network consisting of a number of primary and secondary users, primary users hold licenses for specific spectrum bands, and can only occupy their assigned portion of the spectrum. Secondary users do not have any licensed spectrum and opportunistically send their data by utilizing idle portions of the primary spectrum. In unlicensed spectrum bands there are potentially many uncoordinated devices. And in a multi-hop network the spectrum environment varies in time and space depending on the activities of primary users, interference, and fading, so the optimal spectrum-spreading channelization may therefore be different at each hop in a multi-hop path. Also as new secondary links are formed and others vanish, routing of data flows from one secondary node to another may frequently change. Therefore, controlling the interaction between routing and spectrum allocation is of fundamental importance. The focus of the research is on developing software enhancements in multi-hop routing and spread spectrum channelization that allow for secondary and primary users to co-exist on a non-interfering basis. The authors are Network Throughput Improvement in Cognitive Networks by Joint Optimization of Spectrum Allocation and Cross-layer Routing 7 2 STO-MP-IST-123 NATO UNCLASSIFIED RELEASABLE TO PFP NATO UNCLASSIFIED RELEASABLE TO PFP implementing optimization technique software that runs in Physical (PHY), Medium Access Control (MAC) and Network layers (i.e., “cross-layer”). Figure 7-1 shows the major components of the new development software alongside the customary seven layers of the Open Source Interconnection (OSI) Model. Figure 7-1: Optimization software implemented in planes that complement the lower three OSI layers. The software techniques are instantiated in each network node; a decentralized joint routing and code-division channelization solution that maximizes throughput by jointly optimizing the following parameters: opportunistic routing, spectrum allocation, and transmit power control. The joint optimization algorithms will be extended to include dynamic code sequence optimization. The result of this joint optimization is improved throughput and reliability. With nodes constrained to maintain constant Signal-to-Interference-plus-Noise-Ratio (SINR), network performance improvement is measured in terms of Quality of Service (QoS) metrics including throughput (Mbits/sec), and packet loss when undergoing network traffic reshaping. 2.0 TESTBED ORGANIZATION The network control technology is being implemented and demonstrated in a programmable and reconfigurable testbed consisting of a grid of up to six network nodes based on the Universal Software Radio Peripheral (USRP) 2 hardware and GNU Radio software. Network Throughput Improvement in Cognitive Networks by Joint Optimization of Spectrum Allocation and Cross-layer Routing STO-MP-IST-123 7 3 NATO UNCLASSIFIED RELEASABLE TO PFP NATO UNCLASSIFIED RELEASABLE TO PFP Software development in the ANDRO SDR lab is aligned with companion and complementary research at testbed facilities of academic partners at the University of Buffalo and the government sponsor at the US Air Force Research Laboratory in Rome NY. Each of the three program partners has very similar SDR lab environments; thus, collaborative software development and innovations can be easily shared. 3.0 ALGORITHM DEVELOPMENT AND EXPERIMENTAL PROCEDURE 3.1 Research Goals and Results The research effort succeeded in demonstrating the feasibility of the approach to enable cognitive radio frequency (RF) users to implicitly cooperate with existing narrowband or wideband users of the spectrum without effectively limiting each individual device’s throughput, operating distance, or both. Methods were introduced to achieve cooperative spread-spectrum access that can be measured in terms of enhanced throughput, reliability, and reduced delay. A new theoretical framework was developed based on nonlinear optimization to rigorously derive efficient distributed algorithms for joint adaptive cognitive routing and spreadspectrum allocation using waveforms compatible with existing military defense applications. A new spread-spectrum management paradigm has been proposed in which digital waveforms are designed to occupy the entire available spectrum, and to adaptively track the interference profile at the receiver to maximize the link capacity while avoiding interference to primary users. Specifically, power and spreading code are jointly selected to maximize the pre-detection secondary SINR while providing QoS guarantees to on-going primary and secondary transmissions, and while the routing algorithm dynamically selects relays based on the network traffic dynamics and on the achievable data rates on different secondary links. Throughput and delay performance were characterized by conducting extensive simulation experiments. The simulations demonstrated the appeal of the proposed framework by indicating significant performance gains compared to baseline solutions. 3.2 Routing and Spectrum Allocation (ROSA) Algorithm Development The ROSA algorithm [1] [2] is the enabler for the SDR adaptation in response to network conditions such as events in the RF channel preventing packets reaching a destination node or slowing down such a transfer, or corrupting the data. ROSA instructs the SDR to adapt its PHY, MAC, and routing behavior according to time-­‐varying traffic demands, network topology, and interference profile. Based on the feedback from the relay nodes and the destination node, ROSA generates real-time decisions and reconfigurations finding several possible routes and the best current route. Data packet delivery management (transmission, reception and temporary storage) is achieved using a novel application of the backflow-pressure algorithm based on hydro-flow principles. ROSA pulses each network node for a utility value that describes the node’s usage. If a relay node has many backlogged packets, then this would provide a low utility value for that particular link, and thus trigger the decision algorithm to avoid routing through that path if an alternative is available. Specifically, the routing protocol Finite State Machine (FSM) pulls routing information contained in the routing table and initiates the decision algorithms to form new network layer headers, in coordination with the MAC-­‐layer FSM. Therefore, the routing FSM prepares packets to be sent to the MAC layer or identifies received packets from the MAC layer. ROSA decision algorithms decide the next hop, spectrum band to be occupied, waveform and size of the contention window. After execution of the decision algorithm, the updated, optimized parameters are written to Network Throughput Improvement in Cognitive Networks by Joint Optimization of Spectrum Allocation and Cross-layer Routing 7 4 STO-MP-IST-123 NATO UNCLASSIFIED RELEASABLE TO PFP NATO UNCLASSIFIED RELEASABLE TO PFP and made accessible from the register plane. Dynamic routing updates endure an insignificant time lag due to time spent on the decision and updating the routing tables so that the route reconfigurations are still optimized in real time. Advanced testing and development of SDR networking protocols necessitates a relatively stable system architecture that defines interactions between components such as the OSI network layers and device Input/Output (I/O). An abstracted system architecture can be used to test a variety of protocols and programs on a common platform. Cross-layer routing algorithms such as ROSA could also benefit from such a modular architecture. To build upon the preliminary simulation results, a new cross-layer SDR architecture was developed called RcUBe (Realtime Reconfigurable Radio). RcUBe abstracts the SDR node into four distinct planes: decision, control, register and data as illustrated in Figure 7-2. The decision plane implements routing algorithm logic; the control plane implements access-control between the layers; and the data plane implements the network, link, and physical layers of the protocol stack. All three of these layers communicate through the register plane which instantiates a shared memory model. Figure 7-2: RcUBe architecture. The RcUBe architecture was successfully demonstrated on a six-node USRP test bed at the University of Buffalo’s electrical engineering laboratory. It was implemented in Python and also demonstrated key concepts of the ROSA routing algorithm as a test case. After the initial RcUBe prototype was demonstrated, an expanded architecture was investigated in order to help transition the technology to other radios beyond GNU Radio and USRP platforms. ANDRO’s Cross Layer (AXL) architecture, shown in Figure 7-3, is fundamentally identical to Network Throughput Improvement in Cognitive Networks by Joint Optimization of Spectrum Allocation and Cross-layer Routing STO-MP-IST-123 7 5 NATO UNCLASSIFIED RELEASABLE TO PFP NATO UNCLASSIFIED RELEASABLE TO PFP RcUBe at a high-level, but it is being developed for transition into more robust testing environments. The diagram below shows how the AXL separates each of the network, link, and physical layers as abstract modules in the system. This simply is a lower level view of the interface provided by RcUBe, and more closely resembles the actual implementation. It also shows how each network layer is independently and cooperatively accessed by the optimization software. Figure 7-3: AXL architecture representation. The authors are further developing an implementation of the ROSA algorithm in the AXL architecture that includes sensing using GNU Radio primitives. Under AXL, the ROSA algorithm will be able to exchange relatively up-to-date SINR estimates with each node in the network in order to jointly optimize routing operations and spectrum allocation. AXL is currently being prototyped in the Python programming language using a multithreaded architecture. Python was chosen to quickly start the testing process and to evaluate the initial design. The ROSA algorithm was developed with the assumption of a second transceiver that could be used to communicate over a Common Control Channel (CCC). Further studies are being done to implement a CCC using a time-slotted algorithm and a single transceiver. This research has assumed the presence of a second transceiver, and simulated the link using an Ethernet Local Area Network (LAN). Separate work on a wireless control channel using USRPs is being done in parallel and there are plans to integrate the work in the future. A detailed test plan has been developed to coordinate testing of live demonstrations showcasing AXL and ROSA’s functionality, starting with tests at the unit level all the way to high-level systems testing. To use AXL as an ongoing test platform it must be continuously tested, including integration and regression testing. A fourcase test sequence is underway as summarized in Table 7-1. Initial testing was limited to just a pair of USRP nodes, each with a connected laptop running the AXL software. At first, MAC layer functionality must be verified including RTS/CTS/DTS (Request-to-Send, Clear-to-Send, Data Transmission reServation) protocols. Test cases monitored the performance of Link-layer handshakes and simultaneously measured throughput to make sure physical layer performance was not adversely effected. Network Throughput Improvement in Cognitive Networks by Joint Optimization of Spectrum Allocation and Cross-layer Routing 7 6 STO-MP-IST-123 NATO UNCLASSIFIED RELEASABLE TO PFP NATO UNCLASSIFIED RELEASABLE TO PFP Table 7-1: Test Cases toward verification of routing optimization methods. Description 2 node: Transmit / Receive 1A Successfully transmit data packets from transmitter to receiver using RTS/CTS/DTS handshake implemented in AXL’s control layer using a protocol derived from 802.11. 1B Include spectrum allocation with ROSA. Transmitter is able to select the best channel for the data link before the transmission and avoid unusable frequency bands by detecting the interference Also include collaborative virtual sensing information exchanged via control packets on the CCC 2A Successfully transmit data packets from source to destination node using the relay node as a pre-defined hop point. The channel will also be pre-selected. 2B Spectrum allocation and collaborative sensing. The relay node will still be a pre-defined hop point, but spectrum allocation will now be used to compare with previous tests. 3A “Best route” decision making. ROSA algorithm selects among alternatives routes to maximize throughput. 3B Subject one relay node to interference. Also increase network packet traffic and measure throughput and latency. 3C Multiple separate sessions with common transmitter-receiver pair, measure throughput and latency. 4A Successfully route data packets by using the ROSA algorithm to jointly optimize the best route among three distinct relay nodes while also selecting the optimal spectrum band. Test a variety of different network conditions. 4B Include network initialization packets, test the network under situations where nodes are entering and leaving the network. Test Case 3 node: T/R + Relay node 4 node: T/R + 2 relay nodes 5 node: T/R + 3 alternate nodes 1

7 Figures and Tables

Cite this paper

@inproceedings{Drozd2014NetworkTI, title={Network Throughput Improvement in Cognitive Networks by Joint Optimization of Spectrum Allocation and Cross-layer Routing}, author={Andrew L. Drozd and Tom Arcuri and Jithin Jagannath and Dimitris A. Pados and Tommaso Melodia and Emrecan Demirors and George Sklivanitis}, year={2014} }