Mehmet Koseoglu

Lower Bounds on the LTE-A Average Random Access Delay Under Massive M2M Arrivals

Rapid growth of machine-to-machine (M2M) communications necessitates the reevaluation of the Long Term Evolution-Advanced (LTE-A) performance, since the current standard is not optimized for intensive M2M traffic. A serious issue is that massive M2M arrivals can overload the LTE-A random access channel, resulting in a significant access delay. There have been a number of proposals to control this overload; however, there are no studies on the mathematical characterization of delay bounds to the best of our knowledge. Here, we derive lower bounds for the LTE-A average random access delay for both a regular traffic pattern (uniformly distributed arrivals) and for a traffic pattern, indicating a serious congestion (beta-distributed arrivals). The proposed delay bounds, which predict the minimum delay with less than 6% error, present the fundamental limits of delay that can be achieved by a practical load-balancing algorithm. This paper is also one of the first attempts toward the mathematical analysis of beta-distributed arrivals. We also analyze the effect of estimation accuracy, frequency of random access opportunities, and the number of preambles on the access delay. We show that it is possible to reduce the access delay by several orders of magnitude using an appropriate configuration of these system parameters.Rapid growth of machine-to-machine (M2M) communications necessitates the reevaluation of the Long Term Evolution-Advanced (LTE-A) performance, since the current standard is not optimized for intensive M2M traffic. A serious issue is that massive M2M arrivals can overload the LTE-A random access channel, resulting in a significant access delay. There have been a number of proposals to control this overload; however, there are no studies on the mathematical characterization of delay bounds to the best of our knowledge. Here, we derive lower bounds for the LTE-A average random access delay for both a regular traffic pattern (uniformly distributed arrivals) and for a traffic pattern, indicating a serious congestion (beta-distributed arrivals). The proposed delay bounds, which predict the minimum delay with less than 6% error, present the fundamental limits of delay that can be achieved by a practical load-balancing algorithm. This paper is also one of the first attempts toward the mathematical analysis of beta-distributed arrivals. We also analyze the effect of estimation accuracy, frequency of random access opportunities, and the number of preambles on the access delay. We show that it is possible to reduce the access delay by several orders of magnitude using an appropriate configuration of these system parameters.

Joint resource and network scheduling with adaptive offset determination for optical burst switched grids

Optical burst switching (OBS) is a promising technology for optical grids with short-lived and interactive data communication requirements. On the other hand, burst losses are in the nature of the OBS protocol and these losses severely affect the grid job completion times. This paper first proposes a joint grid resource and network provisioning method to avoid congestion in the network in order to minimize grid job completion times. Simulations show that joint provisioning significantly reduces completion times in comparison to other methods that perform network provisioning after grid scheduling. An adaptive extra offset based quality of service (QoS) mechanism is also proposed in order to reduce grid burst losses in case of network congestion. Results show that this adaptive mechanism significantly reduces grid completion times by exploiting the trade-off between decreasing loss probability and increasing delay introduced by the extra offset time.

Probabilistic broadcast for dense AUV networks

As the technology behind autonomous underwater vehicles (AUV) becomes more feasible, it is possible to use fleets of AUVs for coordinated missions such as area mapping, surveillance, etc. The AUVs are typically sent by a central node such as a submarine and move autonomously. Although there is no need for continuous monitoring of AUV nodes, the central node may occasionally need to send a broadcast message to all nodes in the fleet to announce a change in mission; e.g. ordering nodes to return back to the base. To address this need, reliable and low-latency broadcast algorithms for underwater networks are needed. Here we investigate the performance of probabilistic broadcast for AUV networks where the AUVs are not aware of locations of their neighbors. Our results indicate that probabilistic broadcast significantly improves average and maximum delay along with energy consumption in comparison to simple flooding especially for dense networks.

Cross-Layer Energy Minimization for Underwater ALOHA Networks

Underwater networks suffer from energy efficiency challenges due to difficulties in recharging underwater nodes. In addition, underwater acoustic networks show unique transmission characteristics such as frequency-dependent attenuation, which causes the transmission power to significantly depend on the bandwidth and the distance. We here investigate the cross-layer energy minimization problem in underwater ALOHA networks considering the unique transmission properties of the underwater medium. We first analyze the separate optimization of the physical (PHY) and multiple access control (MAC) layers to minimize energy consumption. We analytically obtain the energy-optimum channel access rate for the ALOHA MAC layer, which minimizes the energy consumption per successfully transmitted bit. We then formulate a cross-layer optimization problem, which jointly optimizes PHY and MAC layers to minimize energy consumption. We show that such cross-layer optimization reduces the energy consumption per bit as much as 66% in comparison with separate optimization of both layers. Cross-layer optimization achieves this energy efficiency by assigning higher MAC-layer resources to the nodes that have a longer distance to the base station, i.e., which experience a less efficient PHY layer. Moreover, cross-layer optimization significantly increases the amount data transferred until first node failure since it results in a more homogeneous energy consumption distribution among the nodes.

Effect of network density and size on the short-term fairness performance of CSMA systems

As the penetration of wireless networks increase, number of neighboring networks contending for the limited unlicensed spectrum band increases. This interference between neighboring networks leads to large systems of locally interacting networks. We investigate whether the short-term fairness of this system of networks degrades with the system size and density if transmitters employ random spectrum access with carrier sensing (CSMA). Our results suggest that (a) short-term fair capacity, which is the throughput region that can be achieved within the acceptable limits of short-term fairness, reduces as the number of contending neighboring networks, i.e., degree of the conflict graph, increases for random regular conflict graphs where each vertex has the same number of neighbors, (b) short-term fair capacity weakly depends on the network size for a random regular conflict graph but a stronger dependence is observed for a grid deployment. We demonstrate the implications of this study on a city-wide Wi-Fi network deployment scenario by relating the short-term fairness to the density of deployment. We also present related results from the statistical physics literature on long-range correlations in large systems and point out the relation between these results and short-term fairness of CSMA systems.

Connectivity analysis of an AUV network with OFDM based communications

Autonomous underwater vehicle (AUV) networks play a crucial role in tactical, commercial, and scientific applications, where reliable and robust communication protocols are needed due to the challenging characteristics of the channel. With this motivation, connectivity of AUV networks in different regions with varying transducer characteristics are analyzed through simulations based on real-life orthogonal frequency division multiplexing (OFDM) based communication experiments over noisy and Doppler-distorted channels. Doppler compensation is performed according to the autocorrelation using the cyclic prefix. Using binary and quadrature phase shift keying (BPSK and QPSK) modulation schemes in conjunction with low density parity check (LDPC) coding, error rate levels are investigated through shallow water pond and at-sea experiments. It is shown that, the utilized transmission scheme is capable of correcting all bit errors among nearly one million bits transmitted up to a distance of 1 km, yielding a payload rate of 15.6 kbps with 4096 subcarriers and QPSK modulation. The simulations provide key parameters that must be taken into account in the design of scalable and connected AUV networks.

Pricing-Based Load Control of M2M Traffic for the LTE-A Random Access Channel

Existing cellular infrastructures have to be revisited for emerging machine-to-machine (M2M) traffic as semi-synchronized M2M arrivals may create a significant congestion resulting in a high access delay. In such a case, there is a strong need for service differentiation, since the delay requirements of IoT applications may vary greatly from delay-tolerant metering applications to security applications with stringent requirements. This problem has been receiving significant interest from the research community in the context of the LTE-A random access channel. Most studies, however, consider load control schemes with few fixed service classes, which can provide limited service differentiation. We propose an alternative scheme where the load is controlled by the price announced by the base station. The proposed method controls the load effectively and provides negligible delay for messages with the highest priority. It also enables low-cost wireless access to delay-tolerant messages by generating most of the revenue from high-priority messages. We derive pricing-based load control schemes for throughput and revenue maximization, and present the relationship between delay, revenue, and cost in both schemes. Our results suggest that dynamic pricing is a promising solution for major problems associated with cellular M2M traffic.

Spatio-Temporal Analysis of Throughput for Single-Hop CSMA Networks

Throughput model for non-persistent CSMA networks which was proposed by Kleinrock and Tobagi has been widely used, although it provides a loose lower bound when nodes are distributed in a large area because the analysis assumes that the propagation delay between each pair of users equals to the largest propagation delay in the network. We present a throughput analysis which considers the spatial distribution of nodes. We obtain a simple throughput expression which predicts throughput with an 8% maximum error whereas the earlier model results in a 44% error when the maximum propagation delay equals to the packet transmission time.

Throughput Modeling of Single Hop CSMA Networks with Non-Negligible Propagation Delay

We analyze the performance of the CSMA protocol under propagation delays that are comparable with packet transmission times. We propose a semi-Markov model for the 2-node CSMA channel. For the 2-node case, the capacity reduces to 40% of the zero-delay capacity when the one-way propagation delay is 10% of the packet transmission time. We then extend this model and obtain the optimum symmetric probing rate that achieves the maximum network throughput as a function of the average propagation delay, d̅, and the number of nodes sharing the channel, N. The proposed model predicts that the total capacity decreases with d̅-1 as N goes to infinity when all nodes probe the channel at the optimum rate. The optimum probing rate for each node decreases with 1/N and the total optimum probing rate decreases faster than d̅-1 as N goes to infinity. We investigate how the short-term unfairness problem in CSMA worsens as the propagation delay increases and propose a back-off mechanism to mitigate this issue. The theoretical results presented in this paper can be used as a benchmark for the performance improvements provided by algorithms that have already been developed.

Energy-Optimum Throughput and Carrier Sensing Rate in CSMA-Based Wireless Networks

We propose a model for the energy consumption of a node as a function of its throughput in a wireless CSMA network. We first model a single-hop network, and then a multi-hop network. We show that operating the CSMA network at a high throughput is energy inefficient since unsuccessful carrier sensing attempts increase the energy consumption per transmitted bit. Operating the network at a low throughput also causes energy inefficiency because of increased sleeping duration. Achieving a balance between these two opposite operating regimes, we derive the energy-optimum carrier-sensing rate and the energy-optimum throughput which maximize the number of transmitted bits for a given energy budget. For the single-hop case, we show that the energy-optimum total throughput increases as the number of nodes sharing the channel increases. For the multi-hop case, we show that energy-optimum throughput decreases as the degree of the conflict graph corresponding to the network increases. For both cases, the energy-optimum throughput reduces as the power required for carrier-sensing increases. The energy-optimum throughput is also shown to be substantially lower than the maximum throughput and the gap increases as the degree of the conflict graph increases for multi-hop networks.