Cooperative Positioning (CP) relies on a network of connected agents equipped with sensing and communication technologies to improve the positioning performance of standalone solutions. In this paper, we develop a com...
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Cooperative Positioning (CP) relies on a network of connected agents equipped with sensing and communication technologies to improve the positioning performance of standalone solutions. In this paper, we develop a completely data-driven model combining Long Short-Term Memory (LSTM) and messagepassing Neural Network (MPNN) for CP, where agents estimate their states from inter-agent and ego-agent measurements. The proposed LSTM-MPNN model is derived by exploiting the analogy with the probability-based message passing algorithm (MPA), from which the graph-based structure of the problem and messagepassing scheme are inherited. In our solution, the LSTM block predicts the motion of the agents, while the MPNN elaborates the node and edge embeddings for an effective inference of the agents' state. We present numerical evidence that our approach can enhance position estimation, while being at the same time an order of magnitude less complex than typical particle-based implementations of MPA for non-linear problems. In particular, the presented LSTM-MPNN model can reduce the error on agents' positioning to one third compared to MPA-based CP, it holds a higher convergence speed and better exploits cooperation among agents.
Orthogonal multiple access (OMA) such as orthogonal frequency division multiple access (OFDMA) has been the predominant access technology adopted by the current generation of mobile communication. Representing an evol...
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Orthogonal multiple access (OMA) such as orthogonal frequency division multiple access (OFDMA) has been the predominant access technology adopted by the current generation of mobile communication. Representing an evolutionary step in radio access networks, non-orthogonal multiple access (NOMA) is considered to be a promising technology to meet the goals of increased capacity, enhanced flexibility, and reduced latency. Pattern division multiple access (PDMA) is a NOMA scheme using resource-element mapping as the multiple-access signature in the symbol level. This article introduces PDMA in a millimeter-wave (mmWave) radio access system with systematic experimental demonstration. One key advantage of PDMA is that it can facilitate advanced parallel interference cancellation (PIC) in the decoding process. A message passing algorithm (MPA) is developed and implemented in this article to enhance the decoding performance of PDMA. A novel PDMA system integrated with MPA is experimentally demonstrated in a multi-user mmWave radio access system that uses radio-over-fiber (RoF) mobile fronthaul, achieving reduced sensitivity penalty and improved modulation spectral efficiency compared to conventional power-domain NOMA (PD-NOMA) with successive interference cancellation (SIC). Experimental results show that PDMA is capable of supporting disparate channel conditions, adapting to various patterns, and providing improved system flexibility.
For large-scale wireless sensor networks (WSNs) with thousands of sensors, cooperative self-localisation is a key task and has caused extensive concerns. In this study, the authors propose a message passing algorithm ...
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For large-scale wireless sensor networks (WSNs) with thousands of sensors, cooperative self-localisation is a key task and has caused extensive concerns. In this study, the authors propose a message passing algorithm for cooperative self-localisation of mobile WSNs by using belief propagation (BP) and variational messagepassing (VMP) on factor graphs. The sensors locate themselves through two steps: a prediction operation accounting for the sensors' mobility and a correction operation accounting for ranging measurements between neighbouring sensors. All the messages for computing and transmitting are restricted to be Gaussian to reduce communication overhead and computational complexity. According to the linear state-transition model and the non-linear ranging model, BP and VMP methods are employed to perform prediction and correction, respectively. Simulation results show that when the standard deviations of the prior distributions is small, the positioning accuracy of the proposed algorithm is comparable with that of sum-product algorithm over a wireless network (SPAWN) with much low communication overhead and computational complexity.
Consider the computations at a node in a message passing algorithm. Assume that the node has incoming and outgoing messages x = (x(1), x(2), ..., xn) and y = (y(1), y(2), ..., y(n)), respectively. In this paper, we in...
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Consider the computations at a node in a message passing algorithm. Assume that the node has incoming and outgoing messages x = (x(1), x(2), ..., xn) and y = (y(1), y(2), ..., y(n)), respectively. In this paper, we investigate a class of structures that can be adopted by the node for computing y from x, where each y(j), j = 1, 2,..., n is computed via a binary tree with leaves x excluding x(j). We make three main contributions regarding this class of structures. First, we prove that the minimum complexity of such a structure is 3n - 6, and if a structure has such complexity, its minimum latency is delta + inverted right perpendicularlog(n - 2(delta))inverted left perpendicular with delta = right perpendicularlog(n/2)left perpendicular, where the logarithm always takes base two. Second, we prove that the minimum latency of such a structure is inverted right perpendicularlog(n - 1)inverted left perpendicular, and if a structure has such latency, its minimum complexity is n log(n - 1) when n - 1 is a power of two. Third, given (n, tau) with tau >= inverted right perpendicularlog(n - 1)inverted left perpendicular, we propose a construction for a structure which we conjecture to have the minimum complexity among structures with latencies at most tau. Our construction method runs in O(n(3) log(2) (n)) time, and the obtained structure has complexity at most (generally much smaller than) ninverted right perpendicularlog(n)inverted left perpendicular - 2.
Wireless signals are frequently disturbed by complex noise sources, presenting a challenge to traditional direction of arrival (DOA) estimation methods that rely on the assumption of Gaussian noise. To address this is...
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Wireless signals are frequently disturbed by complex noise sources, presenting a challenge to traditional direction of arrival (DOA) estimation methods that rely on the assumption of Gaussian noise. To address this issue, this letter proposes an innovative Bayesian DOA estimation approach. This method utilizes Gaussian mixture model (GMM) and Dirichlet process prior to model the density function of complex noise in practical scenarios. Additionally, an efficient combined message passing algorithm is formulated on the factor graph through the use of generalized approximate messagepassing (GAMP) and mean field (MF) techniques. Simulation results validate the effectiveness of this algorithm.
A messagepassing (MP) algorithm for resource allocation (RA) in an Orthogonal Frequency Division Multiple Access (OFDMA) based spectrum sharing system is developed in this paper. We derive optimal power and subcarrie...
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ISBN:
(纸本)9781479930838
A messagepassing (MP) algorithm for resource allocation (RA) in an Orthogonal Frequency Division Multiple Access (OFDMA) based spectrum sharing system is developed in this paper. We derive optimal power and subcarrier allocations. We further shed light on advantages of the MP algorithm particularly against the dual solution and highlight its suitability in reducing the computational burdens for practical configurations. It is observed that MP algorithm converges only after two iterations compared to 400 required iterations in the dual case. This considerably reduces the corresponding computational time, which in turn results in much lower consumed processing energy. The saved processing energy and time can then be exploited for enhancing the scheduled data rate especially when the number of users is high.
In order to glean benefits from orthogonal frequency division multiplexing combined with index modulation (OFDM-IM) in the presence of strong Phase-Noise (PHN), in this paper, low-complexity joint PHN estimation and d...
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In order to glean benefits from orthogonal frequency division multiplexing combined with index modulation (OFDM-IM) in the presence of strong Phase-Noise (PHN), in this paper, low-complexity joint PHN estimation and decoding methods are developed in the framework of messagepassing on a factor graph. Both the Wiener process and the truncated discrete cosine transform (DCT) expansion model are considered for approximating the PHN variation. Then based on these a factor graph is constructed for explicitly representing the joint estimation and detection problem. Taking full account of the sparse and structured a priori information arriving from the soft-in soft-out (SISO) decoder of a turbo receiver, a modified generalized approximate messagepassing (GAMP) algorithm is invoked for decoupling the frequency-domain symbols. In the decoupling step, mean field (MF) approximation is employed for solving the unknown nonlinear transform matrix problem imposed by PHN. Furthermore, merged belief propagation and MF (BP-MF) methods amalgamated both with sequential and parallel messagepassing schedules are introduced and compared to the proposed GAMP based algorithms in terms of their bit error ratio (BER) vs. complexity. Our simulation results demonstrate the efficiency of the proposed algorithms in the presence of both perfect and imperfect channel state information.
Mega constellation, as an extremely large-scale radio access network, faces severe multi-user interference when accommodating ubiquitous access. Distributed multi-user detection (MUD) can utilize the multi-satellite s...
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Mega constellation, as an extremely large-scale radio access network, faces severe multi-user interference when accommodating ubiquitous access. Distributed multi-user detection (MUD) can utilize the multi-satellite spatial diversities and processing capabilities to alleviate inter-user interference. However, the spaceborne nature makes it seriously chained by inter-satellite link (ISL) constraints including the limited number and the constrained bandwidth of ISL ports. Therefore, this paper proposes an efficient messagepassing (MP) based distributed MUD framework under stringent ISL constraints. First, the overheads on ISL ports and bandwidth introduced by fully-connected distributed MUD are quantitatively characterized using distributed factor graph (FG) model. On this basis, we propose two ISL-compatible design principles for distributed MUD, i.e., orchestrating message flow (MF) hierarchically among satellites to save ports, and propagating messages selectively to save bandwidth. Specifically, a novel multi-branch tree-like MF orchestration is proposed to forward and aggregate the locally generated detection messages in a partially-connected manner. The relationship between MF structure and overall performance is revealed via EXIT chart and a fairness-aware orchestration algorithm is developed. Further, we introduce a novel squeeze node into the distributed FG, compressing messages and facilitating selective MP under bandwidth constraint. Three criteria are correspondingly proposed to identify the most effective messages for distributed MUD. Our proposed MUD framework is evaluated under practical settings, which demonstrates a reduction of 50% ISL bandwidth costs with less than 1 dB loss in terms of BER compared to the fully-connected MUD, and achieves up to 5 dB gain in BER over the state-of-the-art distributed reception methods.
Estimating the percolating cluster fraction is central to many percolation models. For real networks, the total size of clusters with loops can be considered a plausible metric for this fraction. In this paper, we dev...
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Estimating the percolating cluster fraction is central to many percolation models. For real networks, the total size of clusters with loops can be considered a plausible metric for this fraction. In this paper, we develop a semi-analytic algorithm to estimate clusters with loops for both site and bond percolation via modifying the message passing algorithm. We compared the estimates of the original message passing algorithm and our modified version with simulation results on four real networks. Our findings suggest that our modified algorithm can achieve accuracy for any real network, provided that a sufficient number of possible states following site or bond occupation are selected and analyzed to calculate the final estimate.
This letter proposes a novel hybrid automatic repeat request with chase combining assisted sparse code multiple access (HARQ-CC-SCMA) scheme. Depending on whether the same superimposed packet is retransmitted, synchro...
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This letter proposes a novel hybrid automatic repeat request with chase combining assisted sparse code multiple access (HARQ-CC-SCMA) scheme. Depending on whether the same superimposed packet is retransmitted, synchronous and asynchronous modes are considered for retransmissions. Moreover, a factor graph aggregation (FGA) method is used for multi-user detection. Specifically, a large-scale factor graph is constructed by combining all the received superimposed signals and message passing algorithm (MPA) is applied to calculate log-likelihood ratio (LLR). Monte Carlo simulations are preformed to show that FGA surpasses bit-level combining (BLC) and HARQ with incremental redundancy (HARQ-IR) in synchronous mode. Moreover, FGA performs better than BLC at high signal-to-noise ratio (SNR) region in asynchronous mode. However, FGA in asynchronous mode is worse than BLC at low SNR, because significant error propagation is induced by the presence of failed messages after the maximum allowable HARQ rounds.
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