Performance Analysis of Cooperative Virtual MIMO Systems for Wireless Sensor Networks

Rafique, Zimran; Seet, Boon‐Chong; Al-Anbuky, Adnan

doi:10.3390/s130607033

Cited by 29 publications

(20 citation statements)

References 32 publications

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“…Composed of inexpensive and low-power-consumption sensors that have the ability to compute and communicate, wireless sensor networks (WSNs) have various applications in military defense, environmental testing, space exploration, home intelligence, traffic surveillance, and other fields [1]. However, it is hard to supply energy for battery-powered sensors in bad environments.…”

Section: Introductionmentioning

confidence: 99%

A Novel Joint Power and Feedback Bit Allocation Interference Alignment Scheme for Wireless Sensor Networks

Wang

et al. 2017

Sensors

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It is necessary to improve the energy efficiency of batteries in wireless sensor networks (WSNs). The multiple-input multiple-output (MIMO) technique has become an important means to ameliorate WSNs, and interference management is the core of improving energy efficiency. A promising approach is interference alignment (IA), which effectively reduces the interference and improves the throughput of a system in the MIMO interference channels. However, the IA scheme requires perfect channel state information (CSI) at all transceivers in practice, which results in considerable feedback overhead. Thus, limited IA feedback has attracted much attention. In this paper, we analyze the throughput loss of the K-user MIMO interference channels when each transmitter delivers multiple streams in one slot, and derives the upper-bound of the system interference leakage and throughput loss. Then, to reduce the interference leakage and throughput loss for the MIMO interference alignment with limited feedback, a joint power and feedback bit allocation optimization scheme is proposed. The simulation results show that, compared with the conventional schemes, the presented optimal scheme achieves less residual interference and better performance in the system throughput.

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Section: Introductionmentioning

confidence: 99%

A Novel Joint Power and Feedback Bit Allocation Interference Alignment Scheme for Wireless Sensor Networks

Wang

et al. 2017

Sensors

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“…Therefore, [13,14,15,16,17,18] investigated the MIMO transmission for the WSNs. The work in [13] studied the cooperative MIMO (C-MIMO) technology and adopted the low density parity check (LDPC) code as the error-correcting code, while [14,15] analyzed the virtual MIMO (V-MIMO) for WSNs. The former configured the minimum energy consuming route and satisfied the end-to-end data rate and bit error rate (BER) requirements, and the latter analyzed the impact of the modulation techniques for the cooperative V-MIMO of WSNs.…”

Section: Introductionmentioning

confidence: 99%

“…However, the research in [13,14,15,16,17,18] has mainly focused on the terrestrial network applications, such as the lack of terrestrial networks coverage or where terrestrial networks were challenged by the operating environment, e.g., forest, wilderness and military environments. Since the multibeam satellite communication networks have the advantages of global coverage, unrestricted geography, low cost, quick response [19], and so on, satellite-based sensor networks have been investigated for various application scenarios [20,21,22].…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Integrated Wireless Sensor and Multibeam Satellite Networks Under Terrestrial Interference

Yin

Gong

et al. 2016

Sensors

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This paper investigates the performance of integrated wireless sensor and multibeam satellite networks (IWSMSNs) under terrestrial interference. The IWSMSNs constitute sensor nodes (SNs), satellite sinks (SSs), multibeam satellite and remote monitoring hosts (RMHs). The multibeam satellite covers multiple beams and multiple SSs in each beam. The SSs can be directly used as SNs to transmit sensing data to RMHs via the satellite, and they can also be used to collect the sensing data from other SNs to transmit to the RMHs. We propose the hybrid one-dimensional (1D) and 2D beam models including the equivalent intra-beam interference factor β from terrestrial communication networks (TCNs) and the equivalent inter-beam interference factor α from adjacent beams. The terrestrial interference is possibly due to the signals from the TCNs or the signals of sinks being transmitted to other satellite networks. The closed-form approximations of capacity per beam are derived for the return link of IWSMSNs under terrestrial interference by using the Haar approximations where the IWSMSNs experience the Rician fading channel. The optimal joint decoding capacity can be considered as the upper bound where all of the SSs’ signals can be jointly decoded by a super-receiver on board the multibeam satellite or a gateway station that knows all of the code books. While the linear minimum mean square error (MMSE) capacity is where all of the signals of SSs are decoded singularly by a multibeam satellite or a gateway station. The simulations show that the optimal capacities are obviously higher than the MMSE capacities under the same conditions, while the capacities are lowered by Rician fading and converge as the Rician factor increases. α and β jointly affect the performance of hybrid 1D and 2D beam models, and the number of SSs also contributes different effects on the optimal capacity and MMSE capacity of the IWSMSNs.

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“…Rafique combined virtual MIMO and multi-carrier modulation and analyzed the effect of different virtual MIMO communication modulation schemes. The research verified that the BPSK-WOFDM was a very useful and energy-efficient modulation scheme for high-speed virtual MIMO transmission [6]. Marwan proposed a novel virtual MIMO communication strategy, called CMIMO (cooperative MIMO) [7], which involved clustering the WSN into several clusters, each managed by up to two cluster heads (CHs); a master CH (MCH) and a slave CH (SCH).…”

Section: Introductionmentioning

confidence: 99%

A Virtual MIMO Communication Strategy Based on Cooperative Groups for Wireless Sensor Networks

Li¹,

Zheng

Li³

et al. 2015

IJFGCN

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As we all know, virtual MIMO technology is an efficient way for energy saving in wireless sensor networks (WSNs) (CG) in this paper, called Group Collaboration MIMO (GCMIMO). GCMIMO clusters the WSN into several clusters. Each cluster is managed by two cluster heads, master cluster head (MCH) and vice cluster head (VCH). MCH divides the source nodes into several cooperative groups, every two nodes as a group. A CG can constitute a virtual dual-antenna transmitter for sending source data of both nodes directly. VCH and MCH are always seen as a CG, called master cooperative group (MCG), which can receive and forward the data from

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Performance Analysis of Cooperative Virtual MIMO Systems for Wireless Sensor Networks

Cited by 29 publications

References 32 publications

A Novel Joint Power and Feedback Bit Allocation Interference Alignment Scheme for Wireless Sensor Networks

A Novel Joint Power and Feedback Bit Allocation Interference Alignment Scheme for Wireless Sensor Networks

Performance Analysis of Integrated Wireless Sensor and Multibeam Satellite Networks Under Terrestrial Interference

A Virtual MIMO Communication Strategy Based on Cooperative Groups for Wireless Sensor Networks

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