Search

In this paper, we study the problem of maximizing the sum throughput of K users in a hybrid heterogeneous visible light communication (VLC)/radio frequency (RF) wireless communication system. This is done by optimizing the transmission time intervals of a time division multiple access (TDMA) scheme allocated to the users in two different downlink (DL) scenarios. In both scenarios, using the harvested energy, each user transmits its signal in an optimal allocated time interval over the uplink (UL) channel. In the first scenario, a lightwave powered communication network (LPCN)-based VLC system is used to radiate only optical energy to be harvested by the users over DL channel. Specifically, UL sum-rate maximization problem is formulated by optimizing UL time allocations. In the second scenario, a time switching-based simultaneous lightwave information and power transfer (TS-based SLIPT) is considered where the light-emitting diode (LED) transmitter sends both information and power simultaneously over DL channel. Specifically, we propose a multi-objective optimization problem (MOOP) to study the trade-off between the UL and DL sum-rates. The non-convex MOOP framework is then transformed into an equivalent form, which yields a set of Pareto optimal resource allocation policies. The effectiveness of the proposed approaches is illustrated through numerical results.

This paper optimizes the energy consumption of the downlink of a multiple-antenna base station (BS) transmitting to several single-antenna users. The BS utilizes simultaneous wireless information and power transfer (SWIPT) while receivers apply power-splitting (PS) with a nonlinear energy harvesting model leading to PS-SWIPT. We use an intelligent reflecting surface (IRS) and propose a joint design to optimize the active data and the BS’s energy beamformers, IRS’s passive beamformers, and the receivers’ PS ratios under perfect and imperfect CSI availability. In particular, the total BS transmit power is minimized while guaranteeing a minimum rate and harvested energy for each receiver. We apply the block coordinate descent (BCD) method to optimize active and passive beamformers iteratively. We enforce the rank-one constraint and solve the corresponding optimization problem via successive convex approximation (SCA) for accurate semidefinite relaxations. Furthermore, we propose a worst-case robust design for the imperfect CSI case and reformulate this problem with infinitely many constraints. With the BCD method, the problem is iteratively solved via semidefinite programming (SDP) and a second sub-problem with a linear objective and quadratic matrix inequalities, which is also solved via SCA. Numerical results show significant improvements (e.g., 30% decrease in transmit power) than those of no-IRS and IRS with random phase shifts.

Optical signal propagation through underwater channels is affected by three main degrading phenomena, namely, absorption, scattering, and fading. In this paper, we experimentally study the statistical distribution of intensity fluctuations in underwater wireless optical channels with random temperature and salinity variations, as well as the presence of air bubbles. In particular, we define different scenarios to produce random fluctuations on the water refractive index across the propagation path and, then, examine the accuracy of various statistical distributions in terms of their goodness of fit to the experimental data. We also obtain the channel coherence time to address the average period of fading temporal variations. The scenarios under consideration cover a wide range of scintillation index from weak to strong turbulence. Moreover, the effects of beam-expander-and-collimator (BEC) at the transmitter side and aperture averaging lens (AAL) at the receiver side are experimentally investigated. We show that the use of a transmitter BEC and/or a receiver AAL suits single-lobe distributions, such that the generalized Gamma and exponentiated Weibull distributions can excellently match the histograms of the acquired data. Our experimental results further reveal that the channel coherence time is on the order of 10−3 s and larger which implies to the slow fading turbulent channels.

A multiple antenna base station (BS) with an intelligent reflecting surface (IRS) platform, and several single antenna users are considered in the downlink mode. Simultaneous wireless information and power transfer (SWIPT) is utilized by the BS via transmit beamforming to convey information and power to all devices. Each device applies power splitting (PS) to dedicate separate parts of received power to information decoding and energy harvesting. We formulate a total transmit power minimization problem to jointly design the BS beamforming vectors, IRS phase shifts, and PS ratios at the receivers subject to minimum rate and harvested energy quality of service (QoS) constraints at all the receivers. First, we develop a block coordinate descent algorithm, also known as alternating optimization that can decrease the objective function in every iteration with guaranteed convergence. Afterwards, two low-complexity sub-optimal algorithms that rely on well-known maximum ratio transmission and zero-forcing beamforming techniques are introduced. These algorithms are beneficial when the number of BS antennas and/or number of users is large, or coherence times of channels are small. Simulations corroborate the expectation that by deploying a passive IRS, BS transmit power can be reduced by 10–20 dBw while maintaining similar guaranteed QoS. Furthermore, even the proposed sub-optimal algorithms outperform the globally optimal SWIPT solution without IRS for a modest number of IRS elements.

Internet of Things (IoT) paradigm with strong impact on future life will be interconnected through Cognitive Radio Networks (CRNs). CRNs with Ubiquitous trait are highly promising to achieve interference-free and on-demand services. CRs are able to sense the spectral environment, to detect unoccupied bands, and to use them for signal transmissions. This opportunity encourages malicious Users to surpass CRs by Primary User Emulation (PUE) attack and use vacant spectrums. This paper proposes an unsupervised algorithm to distinguish CRs from PUs regardless of static and mobile user. Employing K-means and graph theory are coincident in our algorithm to improve detection outcomes. The edge of graph corresponding to the relation between signals is used and the result of comparison the signal properties is exposed to different clusters. The Receiver Operating Characteristic (ROC) and Detection Error Tradeoff (DET) of our proposed algorithm prove our claim.

Green communications has been paid much attention recently, to provide energy efficient networks with a high level of quality of service (QoS). In this study, the authors propose a cross-layer scheduling scheme for the downlink of long-term evolution networks, considering the buffer state which may be empty in some times. Their target is to minimise energy consumption under the constraints of limited average transmitting power in the base station, and maximum tolerable delay as QoS of heterogeneous users. In the proposed scheme, the authors use an effective capacity link layer model to consider the data link layer characteristics, such as probability of non-empty buffer and probability of maximum delay, along with the physical layer parameters. Simulation results show more than 48% reduction of energy consumption in peak traffic loads.

In this paper, we consider the problem of cognitive radio (CR) user selection to maximize overall CR network (CRN) throughput when the available spectrum bandwidth is less than the demand by all CR users. We formulate optimal CR user selection problem. Then, based on approximation of the average received signal to interference plus noise ratio (SINR) and adaptive modulation and coding (AMC), we estimate the required bandwidth of CR users with different required quality of services (QoSs). Using the principle of optimality, we propose a novel cooperative spectrum sharing algorithm for a CRN. The proposed algorithm not only achieves exhaustive search performance but also its complexity is in the order of N × M versus 2N for exhaustive search, where N is the number of CR users, and M is the spectrum pool size. Extensive simulation results illustrate that the proposed algorithm significantly outperforms the existing algorithms that ignore optimal CR user selection. Also, these results illustrate a better fairness criterion than those of previous works.

In this paper, we propose a new scheme to improve the performance of an LDPC-coded OFDM based cognitive radio (CR) link by applying adaptive power weights. To minimize estimation errors of detected signals in all the CR subcarriers, power weights are allocated to the CR subcarriers at the secondary transmitter. Some constraints for the power weights are considered, such as keeping the interference power introduced by the CR to primary users below a given interference threshold and also keeping sum of transmission powers in all CR subcarriers within a total transmission power. The LDPC decoder applies these power weights in the Log Likelihood Ratios (LLRs) used in message passing scheme at the secondary receiver to achieve more reliable communications. So, the received signal in each CR subcarrier will be decoded with the knowledge of transmission power weights, which come from the cognitive feedback channel without additional cost. Simulation results demonstrate that our proposed scheme achieves a lower bit error rate and a higher transmission rate compared with those of the same scheme without applying power weights.

In this paper, a two step secure spectrum sensing algorithm is proposed for cognitive radio networks. In this algorithm, the sensing results of secondary users are pre-filtered and applying fuzzy logic, so, the overall sensing performance of the network is improved. To determine pre-filter parameters, statistical parameters of the sensing results are used to remove those sensing results which are far from the majority sensing results. However, to obtain a better performance in the spectrum sensing, we propose a fuzzy logic to nullify the effects of malicious users who transmit false sensing data to the fusion center. We further propose a Fuzzy Trust Level for each user as to weight the sensing result of the corresponding user before combining all sensing results in the fusion center. Simulation results demonstrate that our proposed algorithm yield significant improvement in the performance of the spectrum sensing and identifying malicious users.

Employing cooperative communication in multihop wireless sensor networks provides the network with significant energy efficiency. However, the lifetime of such a network is directly dependant upon the lifetime of each of its individual sections (or clusters). Ignoring the fact that those sections close to sink have to forward more data (their own data plus the data received from the previous sections) and hence die sooner with considering equal section sizes, leads to a sub-optimal lifetime. In this paper, we optimize the section sizes of a multihop cooperative WSN so that it maximizes the network lifetime. Simulation results demonstrate a significant lifetime enhancement for the proposed optimal sectoring.

In this paper bit error rate (BER) performance is analyzed for multiple input-multiple output (MIMO) communications systems using antenna selection in the transmitter, maximal ratio combining (MRC) and linear de-correlating detector (LDD) in the receiver in wide band code division multiple access (WCDMA) downlink channels with correlated Nakagami fading. The MRC maximizes signal to noise ratio of the received signal, then the LDD cancels out multiple access interference (MAI). Theoretical results are validated using computer simulations. Moreover, a pilot based estimation method is proposed to jointly estimate the channel gains and the rows of the LDD operator. Simulation results show that using this proposed method, diversity order is maintained in the receiver. Furthermore, our analysis shows the spectral efficiency degradation due to the pilot based strategy is negligible.

This paper considers blind chip rate estimation of DS-SS signals in multi-rate and multi-user DS-CDMA systems over channels having slow flat Rayleigh fading plus additive white Gaussian noise. Channel impulse response is estimated by a subspace method, and then the chip rate of each signal is estimated using zero crossing of estimated differential channel impulse response. For chip rate estimation of each user, an algorithm which uses weighted zero-crossing ratio is proposed. Maximum value of the weighted zero crossing ratio takes place in the Nyquist rate sampling frequency, which equals to the twice of the chip rate. Furthermore, bit time of each user is estimated using fluctuations of autocorrelation estimators. Since code length of each user can be obtained using bit time and chip time ratio. Fading channels reduce reliability factor of the proposed algo-rithm. To overcome this problem, a receiver with multiple antennas is proposed, and the reliability factor of the proposed algorithm is analyzed over both spatially correlated and independent fading channels.

In this paper, we investigate the joint spectrum sensing and resource allocation problem to maximize throughput capacity of an OFDM-based cognitive radio link with a cognitive relay. By applying a cognitive relay that uses decode and forward (D&F), we achieve more reliable communications, generating less interference (by needing less transmit power) and more diversity gain. In order to account for imperfections in spectrum sensing, the proposed schemes jointly modify energy detector thresholds and allocates transmit powers to all cognitive radio (CR) subcarriers, while simultaneously assigning subcarrier pairs for secondary users (SU) and the cognitive relay. This problem is cast as a constrained optimization problem with constraints on (1) interference introduced by the SU and the cognitive relay to the PUs; (2) miss-detection and false alarm probabilities and (3) subcarrier pairing for transmission on the SU transmitter and the cognitive relay and (4) minimum Quality of Service (QoS) for each CR subcarrier. We propose one optimal and two sub-optimal schemes all of which are compared to other schemes in the literature. Simulation results show that the proposed schemes achieve significantly higher throughput than other schemes in the literature for different relay situations., EAI Endorsed Transactions on Wireless Spectrum 14(1): e4 Published 13th Apr 2014

A wireless sensor network with a large number of tiny sensor nodes can be used as an effective tool for gathering data in various situations. One of the major issues in wireless sensor networks is developing an energy-efficient routing protocol which has a significant impact on the overall lifetime of the sensor network. In this paper, we propose a novel hierarchical with static clustering routing protocol called Energy-Efficient Protocol with Static Clustering (EEPSC). EEPSC, partitions the network into static clusters, eliminates the overhead of dynamic clustering and utilizes temporary-cluster-heads to distribute the energy load among high-power sensor nodes; thus extends network lifetime. We have conducted simulation-based evaluations to compare the performance of EEPSC against Low-Energy Adaptive Clustering Hierarchy (LEACH). Our experiment results show that EEPSC outperforms LEACH in terms of network lifetime and power consumption minimization.

Due to the limited energy resources, energy efficient operation of sensor node is a key issue in wireless sensor networks. Clustering is an effective method to prolong the lifetime of energy constrained wireless sensor network. However, clustering in wireless sensor network faces several challenges such as selection of an optimal group of sensor nodes as cluster, optimum selection of cluster head, energy balanced optimal strategy for rotating the role of cluster head in a cluster, maintaining intra and inter cluster connectivity and optimal data routing in the network. In this paper, we propose a protocol supporting an energy efficient clustering, cluster head selection/rotation and data routing method to prolong the lifetime of sensor network. Simulation results demonstrate that the proposed protocol prolongs network lifetime due to the use of efficient clustering, cluster head selection/rotation and data routing., {"references":["C.Y. Chong and S.P. Kumar, \"Sensor networks: Evolution, Opportunities, and Challenges,\" Proceedings of the IEEE, vol. 91, no. 8, pp. 1247-1256, August 2003.","Ameer Ahmed Abbasi and Mohamed Younis, \"A Survey on Clustering Algorithms for Wireless Sensor Networks,\" Elsevier Journal of","Computer Communications, vol. 30, pp. 2826-2841, October 2007. Olutayou Boyinbode, Hanh Le, Audrey Mbogho, Makoto Takizawa, and Ravi Poliah, \"A Survey on Clustering Algorithms for Wireless Sensor Networks,\" International Conference on Network-Based Information Systems, Gifu, Japan, pp. 358-364, September 2010.","Abbasi and M. Younis, \"A Survey on Clustering Algorithms for Wireless Sensor Networks,\" Elsevier Journal of Computer Communications, vol. 30, pp. 2826-2841, October 2007.","Xiaobing Wu, Guihai Chen and S.K. Das, \"Avoiding Energy Holes in Wireless Sensor Networks with Nonuniform Node Distribution,\" IEEE Transactions on Parallel and Distributed Systems, vol. 19, no. 5, pp. 710-720, May 2008.","M. Younis, M. Youssef and K. Arisha, \"Energy-Aware Management in cluster-Based Sensor Networks,\" International Journal on Computer Networks, vol.43, no. 5, pp. 649-668, December 2003.","D.J Baker and A. Ephremides, \"The Architectural Organization of a Mobile Radio Network via a Distributed Algorithm,\" IEEE Transactions on Communications, vo. 29, no.11, pp. 1694-1701, 1981.","D.J Baker and A. Ephremides, \"The Design and Simulation of a Mobile Radio Network with Distributed Control,\" IEEE Journal on Selected Areas in Communications, vol. 2, no.1, pp. 226-237, January 1984.","Ephremides, J.E. Wieselthier and D.J. Baker, \"A Design Concept for Reliable Mobile Radio Networks with Frequency Hopping Signaling,\" Proceedings of IEEE vol. 75, no.1, pp.56-73, January 1987.\n[10] K.Xu and M. Gerla, \"A Heterogeneous Routing Protocol Based on A New Stable Clustering Scheme,\" Proceeding of IEEE Military Communications Conference, Anaheim, CA, pp. 123-131, October 2002.\n[11] Nagpal and D. Coore, \"An Algorithm for Group Formation in an Amorphous Computers,\" Proceedings of 10th International Conference on Parallel and Distributed Systems (PDCS'98), Las Vegas, NV, October 1998.\n[12] M. Younis, K. Akkaya and A. Kunjithapatham, \"Optimization of Task Allocation in A Cluster—Based Sensor Network\", Proceedings of the 8th IEEE Symposium on Computers and Communications (ISCC'2003), Antalya, Turkey, June 2003.\n[13] W. Heinzelman, A. Chandrakasan and H. Balakrishnan, \"Energy-Efficient Communication Protocol for Wireless Micro Sensor Networks\" Proceedings of the 33rd Hawaii International Conference on System Sciences (HICSS '00), vol.2, pp. 234-243, January 2000.\n[14] 14. Wendi B. Heinzelman, Anantha P. Chandrakasan and Hari Balakrishnan, \"An Application-Specific Protocol Architecture for Wireless Microsensor Networks\", Transactions on Wireless Communications, Vol. 4, pp.660 —670, October 2002.\n[15] Manjeshwar and D. Agrawal, \"TEEN: A Routing Protocol for Enhanced Efficiency in Wireless Sensor Networks,\" Proceedings of 15th International Parallel and Distributed Processing Symposium (IPDPS'01), San Francisco, CA, pp. 2009-2015, April 2001.\n[16] Manjeshwar and D. Agrawal, \"APTEEN: A Hybrid Protocol for Efficient Routing and Comprehensive Information Retrieval in Wireless Sensor Networks,\" Proceedings of International Parallel and Distributed Processing Symposium (IPDPS'02), Fort Lauderdale, Florida, pp.195- 202, April 2002.\n[17] An Amir Sepasi Zahmati, Bahman Abolhassani, Ali Asghar Beheshti Shirazi, and Ali Shojaee Bakhtiari, \"Energy-Efficient Protocol with Static Clustering for Wireless Sensor Networks,\" World Academy of Science, Engineering and Technology vol. 28, pp. 69-72, 2007.\n[18] Lindsey and C. Raghavendra, \"PEGASIS: Power-Efficient Gathering in Sensor Information Systems,\" Proceedings of IEEE Aerospace Conference, Big Sky, Montana, USA, vol.3, pp.1125-1130, March 2002.\n[19] M. Demirbas, A. Arora and V. Mittal, \"FLOC: A Fast Local Clustering Service for Wireless Sensor Networks,\" Proceedings of Workshop on Dependability Issues in Wireless Ad Hoc Networks and Sensor Networks (DIWANS'04), Palazzo dei Congressi, Florence, Italy, June 2004.\n[20] M. Ye, C. Li, G. Chen and J. Wu, \"An Energy Efficient Clustering Scheme in Wireless Sensor Networks,\" Journal of Ad Hoc & Sensor Wireless Networks, Vol.1, pp. 1-21, 2006.\n[21] Younis and S. Fahmy, \"HEED: A Hybrid Energy-Efficient Distributed Clustering Approach for Ad Hoc Sensor Networks,\" IEEE Transactions on Mobile Computing, vol. 3, no.4, 2004.\n[22] Li, M. Ye, G. Chen and J. Wu, \"An Energy-Efficient Unequal Clustering Mechanism for Wireless Sensor Networks,\" Proceedings of IEEE International Conference on Mobile Ad hoc and Sensor Systems Conference(MASS05), Washington, D.C., pp. 604-611, November 2005.\n[23] S. Yi, J. Heo, Y. Cho, and J. Hong, \"PEACH: Power-Efficient and Adaptive Clustering Hierarchy Protocol for Wireless Sensor Networks,\" Elsevier Journal on Computer Communications, vol. 30, pp. 2842-2852, October 2007.\n[24] Xiaorong Zhu, Lianfeng Shen and Tak Shing Peter Yum, \"Hausdorff Clustering and Minimum Energy Routing for Wireless Sensor\nNetworks,\" IEEE Transaction on Vehicular Technology, vol. 58, no.2, pp. 990-997, February 2009.\n[25] Huei Wen Femg, Robby Tendean and Arief Kurniawan, \"Energy Efficient Routing Protocol for Wireless Networks with Static Clustering and Dynamic Structure,\" Springer Journal of wireless personal communication (published online), March 2001.\n[26] N. Bulusu, J. Heidemann and D. Estrin, \"GPS-Less Low-Cost Outdoor Localization for Very Small Devices,\" IEEE Personal Communications, vo. 7, no.5, pp. 28-34, 2000.\n[27] N. Patwari and A.O. Hero, \"Using Proximity and Quantized RSS for Sensor Localization in Wireless Networks,\" Proceedings of the 2nd ACM International Conference on Wireless Sensor Networks and Applications (WSNA 2003), NY, USA, 2003.\n[28] Lee, J.Y. and Scholtz R., \"Ranging in a Dense Multipath Environment Using an UWB Radio Link,\" IEEE Journal on Selected Areas in Communications, Vol. 20, no.9, pp. 1677-1683, 2002.\n[29] D. Goldenberg, A. Krishnamurthy, W. Maness, R. Yang, A. Young, A.S. Morse, A. Savvides and B.D.O. Anderson, \"Network localization in partially localizable networks,\" IEEE INFOCOM, vol. 1, pp. 313-326, 2005.\n[30] I. Rekleitis, D. Meger and G. Dudek, \"Simultaneous Planning, Localization, and Mapping in a Camera Sensor Network,\" Robotics and Autonomous Systems, vol. 54, no.11, pp.921-932, 2006.\n[31] Haibo Zhang and Hong Shen, \"Balancing Energy Consumption to Maximize Network Lifetime in Data-Gathering Sensor Networks,\" IEEE Transactions on Parallel and Distributed Systems, vol. 20, no.10, pp.1526-1539, October 2009.\n[32] Jiguo Yu, Yingying QI and Guanghui Wang, \"An Energy-Driven Unequal Clustering Protocol for Heterogeneous Wireless Sensor Networks,\" Journal of Control Theory Applications vol. 9, no. 1, pp. 133-139, February 2011.G. 0. Young, \"Synthetic structure of industrial plastics (Book style with paper title and editor),\" in Plastics, 2nd ed. vol. 3, J. Peters, Ed. New York: McGraw-Hill, 1964, pp. 15-64."]}