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Register a new userStudy of Cloud-Aided Multi-Way Multiple-Antenna Relaying with Best-User Link Selection and Joint ML Detection
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Added by
Rodrigo de Lamare
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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In this work, we present a cloud-aided uplink framework for multi-way multiple-antenna relay systems which facilitates joint linear Maximum Likelihood (ML) symbol detection in the cloud and where users are selected to simultaneously transmit to each other aided by relays. We also investigate relay selection techniques for the proposed cloud-aided uplink framework that uses cloud-based buffers and physical-layer network coding. In particular, we develop a novel multi-way relay selection protocol based on the selection of the best link, denoted as Multi-Way Cloud-Aided Best-User-Link (MWC-Best-User-Link). We then devise the maximum minimum distance relay selection criterion along with the algorithm that is incorporated into the proposed MWC-Best-User-Link protocol. Simulations show that MWC-Best-User-Link outperforms previous works in terms of average delay, sum-rate and bit error rate.
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We study a cooperative network with a buffer-aided multi-antenna source, multiple half-duplex (HD) buffer-aided relays and a single destination. Such a setup could represent a cellular downlink scenario, in which the source can be a more powerful wireless device with a buffer and multiple antennas, while a set of intermediate less powerful devices are used as relays to reach the destination. The main target is to recover the multiplexing loss of the network by having the source and a relay to simultaneously transmit their information to another relay and the destination, respectively. Successive transmissions in such a cooperative network, however, cause inter-relay interference (IRI). First, by assuming global channel state information (CSI), we show that the detrimental effect of IRI can be alleviated by precoding at the source, mitigating or even fully cancelling the interference. A cooperative relaying policy is proposed that employs a joint precoding design and relay-pair selection. Note that both fixed rate and adaptive rate transmissions can be considered. For the case when channel state information is only available at the receiver side (CSIR), we propose a relay selection policy that employs a phase alignment technique to reduce the IRI. The performance of the two proposed relay pair selection policies are evaluated and compared with other state-of-the-art relaying schemes in terms of outage and throughput. The results show that the use of a powerful source can provide considerable performance improvements.
In this work, we present a switched relaying framework for multiple-input multiple-output (MIMO) relay systems where a source node may transmit directly to a destination node or aided by relays. We also investigate relay selection techniques for the proposed switched relaying framework, whose relays are equipped with buffers. In particular, we develop a novel relay selection protocol based on switching and the selection of the best link, denoted as Switched Max-Link. We then propose the Maximum Minimum Distance (MMD) relay selection criterion for MIMO systems, which is based on the optimal Maximum Likelihood (ML) principle and can provide significant performance gains over other criteria, along with algorithms that are incorporated into the proposed Switched Max-Link protocol. An analysis of the proposed Switched Max-Link protocol and the MMD relay selection criterion in terms of computational cost, pairwise error probability, sum-rate and average delay is carried out. Simulations show that Switched Max-Link using the MMD criterion outperforms previous works in terms of sum-rate, pairwise error probability, average delay and bit error rate.
We consider the opportunistic multiuser diversity in the multiuser two-way amplify-and-forward (AF) relay channel. The relay, equipped with multiple antennas and a simple zero-forcing beam-forming scheme, selects a set of two way relaying user pairs to enhance the degree of freedom (DoF) and consequently the sum throughput of the system. The proposed channel aligned pair scheduling (CAPS) algorithm reduces the inter-pair interference and keeps the signal to interference plus noise power ratio (SINR) of user pairs relatively interference free in average sense when the number of user pairs become very large. For ideal situations, where the number of user pairs grows faster than the system signal to noise ratio (SNR), the DoF of $M$ per channel use can be achieved when $M$ is the relay antenna size. With a limited number of pairs, the system is overloaded and the sum rates saturate at high signal to noise ratio (SNR) though modifications of CAPS can improve the performance to a certain amount. The performance of CAPS can be further enhanced by semi-orthogonal channel aligned pair scheduling (SCAPS) algorithm, which not only aligns the pair channels but also forms semi-orthogonal inter-pair channels. Simulation results show that we provide a set of approaches based on (S)CAPS and modified (S)CAPS, which provides system performance benefit depending on the SNR and the number of user pairs in the network.
This paper presents an analytical investigation on the outage performance of dual-hop multiple antenna amplify-and-forward relaying systems in the presence of interference. For both the fixed-gain and variable-gain relaying schemes, exact analytical expressions for the outage probability of the systems are derived. Moreover, simple outage probability approximations at the high signal to noise ratio regime are provided, and the diversity order achieved by the systems are characterized. Our results suggest that variable-gain relaying systems always outperform the corresponding fixed-gain relaying systems. In addition, the fixed-gain relaying schemes only achieve diversity order of one, while the achievable diversity order of the variable-gain relaying scheme depends on the location of the multiple antennas.
Large number of antennas and radio frequency (RF) chains at the base stations (BSs) lead to high energy consumption in massive MIMO systems. Thus, how to improve the energy efficiency (EE) with a computationally efficient approach is a significant challenge in the design of massive MIMO systems. With this motivation, a learning-based stochastic gradient descent algorithm is proposed in this paper to obtain the optimal joint uplink and downlink EE with joint antenna selection and user scheduling in single-cell massive MIMO systems. Using Jensens inequality and the characteristics of wireless channels, a lower bound on the system throughput is obtained. Subsequently, incorporating the power consumption model, the corresponding lower bound on the EE of the system is identified. Finally, learning-based stochastic gradient descent method is used to solve the joint antenna selection and user scheduling problem, which is a combinatorial optimization problem. Rare event simulation is embedded in the learning-based stochastic gradient descent method to generate samples with very small probabilities. In the analysis, both perfect and imperfect channel side information (CSI) at the BS are considered. Minimum mean-square error (MMSE) channel estimation is employed in the study of the imperfect CSI case. In addition, the effect of a constraint on the number of available RF chains in massive MIMO system is investigated considering both perfect and imperfect CSI at the BS.
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