Thant Zin Oo

An Efficient Time Slot Acquisition on the Hybrid TDMA/CSMA Multichannel MAC in VANETs

The multichannel MACs increase the throughput and reduce the collision probability compared to the single-channel MACs. However, coordination of multiple nodes across multichannels is nontrivial. Recently, a multichannel MAC, called HER-MAC, uses both TDMA and CSMA schemes to improve reliability in broadcasting safety messages and efficiency in service channel utilization. Nevertheless, HER-MAC suffers a high collision probability for a large number of vehicle nodes. In this letter, we propose a hybrid TDMA/CSMA multichannel MAC protocol for VANETs that allows efficient broadcasting of messages and increases throughput on the control channel. Furthermore, our proposed MAC eliminates unnecessary control packet such as HELLO and SWITCH packets in HER-MAC. Analysis and simulation results show that the proposed MAC can provide faster time slot acquisition on the control channel than HER-MAC.

Mode Selection and Resource Allocation in Device-to-Device Communications: A Matching Game Approach

Device to device (D2D) communication is considered as an effective technology for enhancing the spectral efficiency and network throughput of existing cellular networks. However, enabling it in an underlay fashion poses a significant challenge pertaining to interference management. In this paper, mode selection and resource allocation for an underlay D2D network is studied while simultaneously providing interference management. The problem is formulated as a combinatorial optimization problem whose objective is to maximize the utility of all D2D pairs. To solve this problem, a learning framework is proposed based on a problem-specific Markov chain. From the local balance equation of the designed Markov chain, the transition probabilities are derived for distributed implementation. Then, a novel two phase algorithm is developed to perform mode selection and resource allocation in the respective phases. This algorithm is then shown to converge to a near optimal solution. Moreover, to reduce the computation in the learning framework, two resource allocation algorithms based on matching theory are proposed to output a specific and deterministic solution. The first algorithm employs the one-to-one matching game approach whereas in the second algorithm, the one-to many matching game with externalities and dynamic quota is employed. Simulation results show that the proposed framework converges to a near optimal solution under all scenarios with probability one. Moreover, our results show that the proposed matching game with externalities achieves a performance gain of up to 35 percent in terms of the average utility compared to a classical matching scheme with no externalities.

Efficient forwarding and popularity based caching for Content Centric Network

In Content Centric Network, caching and forwarding schemes can affect the performance of the whole network. The original caching and forwarding schemes are simple, but these schemes have some drawbacks. Firstly, the original caching scheme stores the same content on several neighboring routers along the request path. This redundant caching does not use the limited storage space available to the routers efficiently. Secondly, the original forwarding scheme in which the data requests are flooded to neighboring routers, also degrades the performance of the network. In this paper, we aim to solve the issues of using cache space of each router efficiently and forwarding the data requests effectively. In this proposal, we divided an Autonomous System (AS) in several groups of routers to cache the popular contents. Routers in a group cooperatively store the data and forward the Interest in order to increase the network performance. To improve the cache hit, the group of routers only store the popular contents and reduce the duplicate content without effecting the redundancy. We used Consistent Hashing to reduce overlapping contents and forward the request efficiently. The content popularity prediction algorithm assists the routers to store the popular contents that pass through them. Finally, we evaluated the performance of our proposed scheme by using a chunk level simulator.

An Efficient and Fast Broadcast Frame Adjustment Algorithm in VANET

Designing MAC protocol for vehicle ad hoc networks (VANETs) is challenging because of quick topology changes, high vehicle mobility, and different quality of service requirements. One promising approach is to employ both TDMA and CSMA hybrid access schemes in the control channel interval. These protocols can adjust the length of TDMA frame (also called broadcast frame) to adapt itself to different vehicle conditions and provide efficient non-safety message transmission. To improve the efficiency of the hybrid MAC mechanism in VANET, we propose an efficient and fast broadcast frame adjustment algorithm, called EFAB based on the three-hop neighbor information. By adjusting the broadcast frame length quickly, MAC protocol using EFAB can support efficient broadcast services on the control channel. Simulation results show that the hybrid MAC protocol using EFAB can provide faster broadcast frame adjustment and higher packet delivery ratio of safety packets on the control channel than using the existing algorithms.

A reliable multi-hop safety message broadcast in Vehicular Ad hoc Networks

Vehicular Ad hoc NETworks (VANETs) should provide the reliable safety message broadcasts and the efficient non-safety message transmissions to vehicles. The IEEE 1609.4 MAC, which supports multi-channel operations in VANETs, is not reliable enough for the safety message broadcast and not efficient in the Service CHannel (SCH) resources utilization. In this paper, we propose a MAC protocol which supports a Reliable Multi-hop Safety message Broadcast (RMSB-MAC) in VANETs. Each Multi-hop Forwarder (MF) collects the safety messages from the neighbor vehicle nodes, and then the MF uses its reserved time slot to broadcast them to all vehicle nodes in its transmission range as well as to forward them to the next MF. Moreover, by allowing vehicle nodes to exchange non-safety messages during the Control CHannel Interval (CCHI), the RMSB-MAC utilizes the SCH resources more efficiently.

SDN based optimal user association and resource allocation in heterogeneous cognitive networks

The increase in the number of connected smart mobile devices has fueled the exponential growth in mobile data. The next-generation networks must meet the demand for higher capacity. Heterogeneous cognitive networks with multiple base station tiers are a promising approach to achieving the higher data rate target. The user association problem is a major issue in the heterogeneous cognitive networks because of the disparity in transmission powers of the base stations involved. Our objective is to achieve the optimal user association under interference constraints. We formulate the problem into an optimization problem and employ matching theory to propose an algorithm to obtain the optimal user association. The proposed matching algorithm for the optimal user association plays the role of SDN application. We then perform simulations and compare our proposed algorithm with existing ones. The simulations results depict that our proposed algorithm outperforms others.

Offloading in HetNet: A Coordination of Interference Mitigation, User Association, and Resource Allocation

The use of heterogeneous small cell-based networks to offload the traffic of existing cellular systems has recently attracted significant attention. One main challenge is solving the joint problems of interference mitigation, user association, and resource allocation. These problems are formulated as an optimization which is then analyzed using two different approaches: Markov approximation and log-linear learning. However, finding the optimal solutions of both approaches requires complete information of the whole network which is not scalable with the network size. Thus, an approach based on a Markov approximation with a novel Markov chain design and transition probabilities is proposed. This approach enables the Markov chain to converge to the bounded near optimal distribution without complete information. In the game-theoretic approach, the payoff-based log-linear learning is used, and it converges in probability to a mixed-strategy

Joint pricing and power allocation for uplink macrocell and femtocell cooperation

\epsilon

Traffic offloading via Markov approximation in heterogeneous cellular networks

-Nash equilibrium. Based on the principles of these two approaches, a highly randomized self-organizing algorithm is proposed to reduce the gap between optimal and converged distributions. Simulation results show that all of the proposed algorithms effectively offload more than 90 percent of the traffic from the macrocell base station to small cell base stations. Moreover, the results also show that the algorithms converge quickly irrespective of the number of possible configurations.

Alternating renewal framework for estimation in spectrum sensing policy and proactive spectrum handoff

In this paper, we study cooperation among mobile users for uplink in two-tiers heterogeneous wireless networks. In our cooperative model, a macrocell user equipment can relay its data via a femtocell user equipment when it cannot connect to its macro base station or any femtocell base stations directly. In this scenario, the macrocell user equipment tries to find the best relay user in a set of candidate relay femtocell user equipments to maximize its utility function. Additionally, the candidate relay femtocell user equipments give a pricing-based strategy per each power unit to the macrocell user equipment along with power level at relay femtocell user equipments which would be used for relaying data in order to maximize both the relay femto and macrocell user equipments utility function. In static network environment, this problem is formulated as a Stackelberg game. Moreover, in stochastic network environment we find stochastic optimization in a long-term for both the utility functions by modeling the problem as a restless bandit problem. Simulation results illustrate the efficiency of our proposal.