Cellular relay networks adopting orthogonal frequency division multiple (OFDM) technology has been widely accepted for next generation wireless communication due to its advantage in enlarging coverage scale as well as improving data rate.In order to improve the performance of user equipments (UEs) near the cell edge,especially to avoid the interference from inter-cell and intra cell,an enhanced soft frequency reuse scheme is adopted in this paper to assure inter-cell interference coordination (ICIC).Compared with traditional frequency allocation work,the proposed scheme is interference-aware and load-adaptive,which dynamically assigns available frequency among UEs under certain schedule method in variable traffic load condition and mitigates interference using information provided by interference indicator.It can improve signal-to-interference plus noise ratio (SINR) of the UE in each sub channel thus enable the system achieve better throughput and blocking probability performance.Simulation results prove that the proposed scheme may achieve desirable performance on throughput,blocking probability and spectral utilization in the sector under different traffic load compared with other schemes.
A distributed best-relay node selection scheme is investigated for cooperative communications with adaptive modulation and coding(MAC) strategy over underlay-paradigm based cognitive radio(CR) networks.The scheme aims to maximize the average spectral efficiency and meanwhile to guarantee that the primary link is provided with a minimum-rate for a certain percentage of time.Simulation results demonstrate that the proposed scheme can significantly improve the spectral efficiency compared with other existing schemes.
In Cognitive Radio(CR) networks,cooperative communication has been recently regarded as a key technology for improving the spectral utilization efficiency and ensuring the Quality of Service(QoS) for Primary Users(PUs).In this paper,we propose a distributed joint relay selection and power allocation scheme for cooperative secondary transmission,taking both Instantaneous Channel State Information(I-CSI) and residual energy into consideration,where secondary source and destination may have different available spectrum.Specifically,we formulate the cognitive relay network as a restless bandit system,where the channel and energy state transition is characterized by the finite-state Markov chain.The proposed policy has indexability property that dramatically reduces the computation and implementation complexity.Analytical and simulation results demonstrate that our proposed scheme can efficiently enhance overall system reward,while guaranteeing a good tradeoff between achievable date rate and average network lifetime.
In this paper, a novel flow control mechanism in cognitive packet network (CPN) based on the improved back propagation (BP) neural network is proposed, considering the flow distribution status predicted by BP neural network when packets are routed. The objective is to increase the capacity of CPN and improve the quality of service (QoS) by achieving flow balance. Besides, considering the slow convergence speed of traditional BP algorithm and the quick change of the flow status in cognitive packet network, an improved BP algorithm with dynamic learning rate is designed in order to achieve a higher convergence speed. The mechanism, which we propose, regards the predicated traffic data as an important factor when packets are routed to implement flow control. By achieving balance, the quality of network can be improved obviously. The simulation results show that the proposed mechanism provides better average time delay and packets loss ratio.
Cognitive wireless local area network with fibre-connected distributed antennas(CWLAN-FDA) is a promising and efficient architecture that combines radio over fiber,cognitive radio and distributed antenna technologies to provide high speed/high capacity wireless access at a reasonable cost.In this paper,a Q-learning approach is applied to implement dynamic channel assignment(DCA) in CWLAN-FDA.The cognitive access points(CAPs) select and assign the best channels among the industrial,scientific,and medical(ISM) band for data packet transmission,given that the objective is to minimize external interference and acquire better network-wide performance.The Q-learning method avoids solving complex optimization problem while being able to explore the states of a CWLAN-FDA system during normal operations.Simulation results reveal that the proposed strategy is effective in reducing outage probability and improving network throughput.
A cross-layer optimized query routing mismatch alleviation(QRMA)architecture is proposed to mitigate the problem of query routing mismatch(QRM)phenomenon between the structured peer to peer(P2P)overlay and the routing layer in mobile Ad-hoc networks(MANETs),which is an important issue that results in the inefficiency of lookup process in the system.Explicated with the representative Chord protocol,the proposal exploits the information of topologic neighbors in the routing layer of MANETs to find if there is any optimized alternative next hop in P2P overlay during conventional lookup progress.Once an alternative next hop is detected,it will take the shortcut to accelerate the query procedure and therefore alleviate the QRM problem in scalable MANETs without any assistance of affiliation equipments such as GPS device.The probability of finding out such an alternative node is formulated and the factors that could increase the chance are discussed.The simulation results show that the proposed architecture can effectively alleviate the QRM problem and significantly improve the system performance compared with the conventional mechanism.
Due to the heterogeneity and versatility of emerging services and applications in wireless networks,it has been a great challenge to improve the system performance with both the awareness of service characteristics and the balance of the traffic between adjacent networks.This paper is committed to solve this problem by introducing a Service-aware Proactive Vertical Handoff(SPVH) algorithm in Heterogeneous Wireless Networks(HWN).A Bandwidth Requirement Prediction Model(BRPM) is illustrated at first,which is adaptive to the system condition variants to forecast traffic requests.Moreover,by adopting a service-aware objective utility function,each user can optimize the vertical handover decisions with awareness of the related supporting networks and service characteristics.Since the decision process is executed with consideration of BRPM predictions,the SPVH algorithm can avoid congestion in HWN through a proactive method.The experiment results show that the proposed SPVH can solidly enhance the system performance in terms of service access ratio,average access delay,system throughput,usage ratio of spectrum resource,and eventually achieve higher network utility.
In this paper,a novel WLAN system,Cognitive WLAN over Fiber (CWLANoF),is introduced in the first place.Moreover,when CWLANoF has more channels than STAs,a new channel allocation scheme is proposed using the Hungarian algorithm,which is demonstrated to be the optimal one.Furthermore,when CWLANoF has fewer channels than STAs,it is possible for more than one STA to share the same channel simultaneously based on the new features of CWLANoF.And the power control scheme is proposed for this kind of sharing,considering efficiency and fairness.Finally,extensive simulation results illustrate the significant performance improvement of the proposed channel allocation scheme and power control scheme.
Cooperative relaying is emerging as an effective technology to fulfill requirements on high data rate coverage in next-generation cellular networks,like long term evolution-advanced (LTE-Advanced).In this paper,we propose a distributed joint relay node (RN) selection and power allocation scheme over multihop relaying cellular networks toward LTE-Advanced,taking both the wireless channel state and RNs' residual energy into consideration.We formulate the multihop relaying cellular network as a restless bandit system.The first-order finite-state Markov chain is used to characterize the time-varying channel and residual energy state transitions.With this stochastic optimization formulation,the optimal policy has indexability property that dramatically reduces the computational complexity.Simulation results demonstrate that the proposed scheme can efficiently enhance the expected system reward,compared with other existing algorithms.
