Optimal CSMA scheduling with dual access probability for wireless networks

Wireless Networks - Recently optimal Carrier Sense Multiple Access (CSMA) scheduling schemes have attracted much attention in wireless networks due to their low complexity and provably optimal...     

Adaptive Auction Framework for Spectrum Market in Cognitive Radio Networks

For the recent decade, cognitive radio networks have received much attention as an alternative to the traditional static spectrum allocation policy since the licensed spectrum channels are not being used efficiently. The most critical issue of the cognitive radio networks is how to distribute the idle spectrum channels to the secondary users opportunistically. The auction-based market is desirable for the trade of idle spectrum channels since the secondary users can purchase a channel in timely manner and the licensed primary users can earn the additional profit while not using the channels. Among the auction algorithms proposed for the spectrum market, we focus on the TASG framework, which consists of two nested auction algorithms, because it enables the group-buying of spectrum channels for the secondary users with limited budgets, and possesses many positive properties such as budget-balance, individual rationality and truthfulness. However, the TASG framework is not very attractive to the market participants since the seller earns the small revenue and the buyer has the low utility. In this paper, we propose a new auction framework for the spectrum markets, called aDaptive and Economically robust Auction-based Leasing (DEAL), that keeps all the benefits of TASG while improving the utility (or revenue) of the participants. To this end, we develop an enhanced inner-auction algorithm, called the Global Auction algorithm in our DEAL framework, and adapt the involved parameters dynamically based on the previous bids from the potential buyers. Simulation results demonstrate that our framework significantly outperforms the previous TASG.     

Realistic cell-oriented adaptive admission control for QoS support in wireless multimedia networks

An important quality-of-service (QoS) issue in wireless multimedia networks is how to control handoff drops. We propose admission-control algorithms that adaptively control the admission threshold in each cell in order to keep the handoff-dropping probability below a predefined level. The admission threshold is dynamically adjusted based on handoff-dropping events. We first present a simple admission-control scheme that brings out an important performance evaluation criterion - intercell fairness - and serves as a reference point. We then investigate the intercell unfairness problem and develop two enhanced schemes to overcome this problem. The performance of these protocols is benchmarked and compared with other competitive schemes. The results indicate that our schemes perform very well while, in addition, achieving significantly reduced complexity and signaling load.     

Utility-based downlink power allocation in multicell wireless packet networks

This paper introduces a utility-based radio resource management technique in multicell wireless packet networks. In terms of allocation of base station (BS) downlink transmit power and assignment of resource to users in each cell, we formulate a problem of maximizing system utility which is defined as the sum of cell utilities. The problem, however, is not solvable due to its non-convex property. Thus, we propose a heuristic algorithm based on an intuition obtained from analyzing a simple two-cell problem. Though the heuristic approach also incurs signaling overhead for power coordination between neighboring base stations, it is much less than that of the original approach. Simulation results show the performance of our proposed algorithm compared with two competitive schemes: optimal and maximum power allocation schemes. As expected, the optimal allocation scheme shows the best performance but can not be employed in a real network due to intractable complexity. Our heuristic algorithm performs reasonably well with very low complexity.     

Power-based admission control for multiclass calls in QoS-sensitive CDMA networks

In this letter, we propose a power-based call admission control (CAC) scheme to accommodate multiclass traffic by directly extending the number-based CAC scheme in multicode CDMA networks, and develop some related mathematical properties. Against the conventional findings, we demonstrate that complete partitioning (CP) of the received signal power at a basestation for each traffic class can be an approach as useful as complete sharing (CS) in accommodating an appropriate number of users. The main advantage of CP scheme over CS scheme is its simplicity in resource management     

Energy efficient transmission scheduling for infrastructure sensor nodes in location systems

This paper considers a location system where a number of deployed sensor nodes collaborate with objects that need to be localized. Unlike existing works, we focus on reducing the energy consumption of the sensor nodes, which are assumed to be static and run on limited battery power. To minimize the total wake-up time of the sensor nodes, we control the transmission schedule of each object. Because it is difficult to find an optimal solution to the considered optimization problem, we consider an approach to this problem that consists of two steps: (1) create an equivalent modified graph coloring subproblem, and (2) permute the coloring result to obtain a best possible solution. We adopt some existing graph coloring algorithms for step 1 and find two properties of optimal schedules that can be used to confine the search space for step 2. Additionally, we propose a heuristic algorithm that aims at significantly reducing the complexity for the case where the confined search space is still too large. The performance of our heuristic algorithm is evaluated through extensive simulations. It is shown that its performance is comparable to that of the simulated annealing algorithm, which gives a near-optimal solution.     

Analysis of total average queue length in multi-hop wireless networks

In upcoming communication environments, multi-hop networking is expected to be pervasive in wireless access and backbone networks because it promises large coverage and increased capacity. Many studies have been devoted to the throughput of multi-hop wireless networks, but the delay performance is not clearly investigated yet. In this Letter, we analyze a multi-hop wireless network to obtain a lower bound of the total average queue length since it is closely related to the delay. We first consider a network with single hop sessions only, and its total average queue length is characterized as a solution of a linear program. Then we present a novel idea of translating a network with multi-hop sessions into a network with single hop sessions only, thereby the linear programming approach can be applied again. Simulation results show that the lower bound is very close to the achievable value of our reference link scheduler. Hence, our bound is tight.     

Contention based scheduling for femtocell access points in a densely deployed network environment

The proliferation of new data intensive devices has caused an enormous burden on wireless systems. A femtocell network is a promising new technology developed to meet these demands. Since each femtocell network consists of uncoordinated subnetworks that work independently, the interference between subnetworks can result in a significant degradation of the overall network capacity. In this paper, we address the interference problem between uncoordinated femtocell access points (FAPs) and propose a distributed FAP scheduling scheme in a densely deployed femtocell network where FAPs interfere with each other. In contrast to previous works that have focused on dynamic power and frequency management, our approach focuses on time sharing through FAP contention. Depending on the outcome of contention, our method selects a winning FAP to be the sole user of the next time frame. The approach operates in a fully distributed manner with help from mobile nodes (MNs). To implement this scheme, we develop a new synchronous frame structure, which uses special common control channels. Through simulations, we observe that the proposed scheme doubles the network capacity compared to the legacy non-contending scheme, and could serve as the basis for future standards on femtocell networks.     

A backward-compatible multiple-round collision avoidance scheme for contention based medium access control

Random-access mechanisms play an important role in wireless networks, and have been extensively studied in recent years. Although many previous studies have proposed enhanced algorithms, each one has only considered either throughput or fairness. In this paper, we propose an efficient random-access mechanism called Multi-round Collision Avoidance (MrCA) that considers throughput and fairness together. The key idea in MrCA is to avoid collisions through multiple contentions, each with a smaller sized contention window. With this simple modification, we can significantly reduce the collision probability as well as the access delay, in addition to increasing fairness index. We find the collision probability and throughput analytically. Through simulation, we validate our analytical model and find appropriate parameters for achieving good performance. We also demonstrate that, compared to the IEEE 802.11 DCF, MrCA makes the collision probability extremely low, so that it increases throughput by 25% as well as short-term fairness by 50% with 50 contending nodes. When MrCA and 802.11 DCF schemes are combined with the auto rate fallback scheme, the performance gain of MrCA over 802.11 DCF increases because MrCA lowers the collision probability significantly, which makes channel error estimation more accurate. We also discuss the issues of implementation and backward compatibility.     

Collision Observation-Based Optimization of Low-Power and Lossy IoT Network Using Reinforcement Learning

The Internet of Things (IoT) has numerous applications in every domain, e.g., smart cities to provide intelligent services to sustainable cities. The next-generation of IoT networks is expected to be densely deployed in a resource-constrained and lossy environment. The densely deployed nodes producing radically heterogeneous traffic pattern causes congestion and collision in the network. At the medium access control (MAC) layer, mitigating channel collision is still one of the main challenges of future IoT networks. Similarly, the standardized network layer uses a ranking mechanism based on hop-counts and expected transmission counts (ETX), which often does not adapt to the dynamic and lossy environment and impact performance. The ranking mechanism also requires large control overheads to update rank information. The resource-constrained IoT devices operating in a low-power and lossy network (LLN) environment need an efficient solution to handle these problems. Reinforcement learning (RL) algorithms like Q-learning are recently utilized to solve learning problems in LLNs devices like sensors. Thus, in this paper, an RL-based optimization of dense LLN IoT devices with heavy heterogeneous traffic is devised. The proposed protocol learns the collision information from the MAC layer and makes an intelligent decision at the network layer. The proposed protocol also enhances the operation of the trickle timer algorithm. A Q-learning model is employed to adaptively learn the channel collision probability and network layer ranking states with accumulated reward function. Based on a simulation using Contiki 3.0 Cooja, the proposed intelligent scheme achieves a lower packet loss ratio, improves throughput, produces lower control overheads, and consumes less energy than other state-of-the-art mechanisms.