In B-IDNC (buffered instantly decodable network coding), each receiver can cache the non-instantly decodable network coded packets (NIDPs) which contain two wanted packets of the network layer for subsequent network d...
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In B-IDNC (buffered instantly decodable network coding), each receiver can cache the non-instantly decodable network coded packets (NIDPs) which contain two wanted packets of the network layer for subsequent network decoding, so that more packets can be recovered at the network layer than the traditional instantly decodable network coding (IDNC). By employing B-IDNC, we consider a radio access network wherein a base station (BS) is required to broadcast a block of packets to a set of receivers. After completing network decoding at the network layer, each receiver can deliver its recovered packets from the network layer to the application layer in order. We consider minimizing the average packet access time of the application layer for B-IDNC. For the optimization problem is intractable, we approximate it to reduce the sum minimum access delay of the application layer across all receivers. The approximate problem is shown to be equivalent to a maximum weight encoding clique problem over the B-IDNC graph. We propose a simple heuristic algorithm based on greedy maximum weight vertex search to solve the approximate problem. Simulation results verify the effectiveness of our proposed algorithm as compared with the existing techniques.
Satellite network is a resource-limited system. How to fully explore the transmission capacity of a satellite network by integrating advanced communication technologies is a hot research topic. This article focuses on...
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Satellite network is a resource-limited system. How to fully explore the transmission capacity of a satellite network by integrating advanced communication technologies is a hot research topic. This article focuses on network coding based single-source to multi-destination transmission problem in satellite networks and attempts to design a joint link adaption and resource allocation scheme to maximize the entire network's transmission capacity. We first apply time expanded graph (TEG) to depict the satellite network's dynamic and formulate the network coding involved joint data flow allocation, inter-satellite link (ISL) establishment, and power allocation issue into a non-convex mixed integer programming problem. To efficiently solve the problem, we then use Lagrange method to decouple it into two sub-problems: network coding based data flow routing problem and joint ISL establishment and power allocation problem. The former is proved to be a convex problem which is solved by solving a minimum-cost routing problem and designing a trisection method, while the latter is solved by Lagrange dual method. Considering the relaxation solution is not necessarily optimal or even feasible, we finally propose an alternating optimization method to further improve the network capacity on the basis of the obtained relaxation solution. Simulation results validate the effectiveness of the proposed algorithm and indicate the significance of designing network coding based transmission schemes for satellite networks.
We resolve three long-standing open problems, namely the (algorithmic) decidability of network coding, the decidability of conditional information inequalities, and the decidability of conditional independence implica...
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We resolve three long-standing open problems, namely the (algorithmic) decidability of network coding, the decidability of conditional information inequalities, and the decidability of conditional independence implication among random variables, by showing that these problems are undecidable. The proof utilizes a construction inspired by Herrmann's arguments on embedded multivalued database dependencies, a network studied by Dougherty, Freiling and Zeger, together with a novel construction to represent group automorphisms on top of the network.
Deep reinforcement learning is a successful learning model in many application domains. A Cognitive radio network (CRN) is a promising solution for spectrum scarcity and sharing issues, while the intrusion of network ...
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Deep reinforcement learning is a successful learning model in many application domains. A Cognitive radio network (CRN) is a promising solution for spectrum scarcity and sharing issues, while the intrusion of network coding schemes will enhance the utilization efficiency of the spectrum. In the CRN network model, the network coding scheme is incorporated to maximize the throughput and effectiveness of the data transmission. This study includes three stages: spectrum sharing model, routing model, and network coding scheme. Dueling enhanced Q reinforcement learning model is proposed for efficient spectrum sharing, and this can be executed with state, action and reward functions. The second phase is routing, which is insisted with adaptive transient search differential evaluation routing protocol to obtain an optimal path for data transmission. The third phase is network coding, and it is very crucial. A homomorphic network coding scheme is proposed to secure and transmit efficient data by maximizing throughput and minimizing delay. The evaluation of the spectrum sharing model, network coding scheme and routing protocol are analyzed in terms of throughput, transmission delay, routing overhead, packet delivery ratio and end-to-end delay and compared with existing available techniques. In the network coding scheme, if the channel loss probability and channel conflict probability are 0.5, the proposed model attains a throughput of 2.3 x 10(5) Bits/s and 2.8 x 10(5) Bits/s, respectively. In the spectrum sharing scenario, the proposed model obtains a PU system throughput of 14 Mbps and an SU system throughput of 45 Mbps, respectively.
This paper concerns with efficient communication over Gaussian and fading multiple-access channels (MACs). Existing orthogonal multiple-access (OMA) and power-domain nonorthogonal-OMA (NOMA) cannot achieve all rate-tu...
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This paper concerns with efficient communication over Gaussian and fading multiple-access channels (MACs). Existing orthogonal multiple-access (OMA) and power-domain nonorthogonal-OMA (NOMA) cannot achieve all rate-tuples in the MAC capacity region. Meanwhile, code-domain NOMA schemes usually require big-loop receiver-iterations for multiuser decoding, which is subject to high implementation cost and latency. This paper studies a linear physical-layer network coding multiple access (LPNC-MA) scheme that is capable of achieving any rate-tuples in the MAC capacity region without receiver iterations. For deterministic Gaussian MACs with M users, we propose to utilize q-ary irregular repeat accumulate (IRA) codes over finite integer fields/rings and q-ary pulse amplitude modulation (q-PAM) as the underlying coded-modulation. The receiver sequentially computes M network coded (NC) messages of the M users. All users' messages are then recovered by solving the computed M NC messages via the inverse of the NC coefficient matrix. A joint nested code construction and extrinsic information transfer (EXIT) chart based code optimization method is developed, yielding near-capacity performance (within 0.7 and 1.1 dB the capacity limits for two and three users respectively). For fading MAC, we study the symmetric rate of LPNC-MA, and propose a pragmatic method for identifying the mutual information (MI) maximizing network coding coefficient matrix. Numerical results demonstrate that the frame error rate (FER) of the optimized LPNC-MA is within a fraction of dB the outage probability of fading MAC capacity. LPNC-MA remarkably outperforms NOMA-SIC and IDMA while avoiding the big-loop receiver iteration.
In optical-processing-enabled network, transitional lightpaths crossing the same node could be optically encoded to each other to achieve greater spectral efficiency. In this context, we present a new research problem...
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In optical-processing-enabled network, transitional lightpaths crossing the same node could be optically encoded to each other to achieve greater spectral efficiency. In this context, we present a new research problem, entitled, routing, wavelength, network coding assignment and protection configuration (RWNCA-PC) arisen in exploiting photonic network coding (NC) for dedicated path protection in wavelength division multiplexing (WDM) networks with an extra degree of freedom in the selection of protection triggering mechanism, that is, network-side and client-side, tailoring to each connection. In order to maximize the NC benefits, we thus provide a weighted multi-objective optimization model for solving RWNCA-PC problem so as to minimize the wavelength count as the strictly prioritized goal and the redundant resources measured by the number of client-side connections as the secondary objective. Numerical results on the realistic COST239 network reveal that a saving of up to 25% wavelength resources could be achieved thanks to the optimal use of NC compared to the non-coding designs and among coding-aware designs, the use of mixed protection configurations would be spectrally more efficient than the design with only network-side protection scheme. Our proposal yields the highest spectrum efficiency compared to all reference designs and moreover, features an average saving of more than 40% transponder count compared with its single objective counterpart.
network coding (NC), when combined with multipath routing, enables a linear programming (LP) formulation for a multi-source multicast with intra-session network coding (MISNC) problem. However, it is still hard to sol...
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network coding (NC), when combined with multipath routing, enables a linear programming (LP) formulation for a multi-source multicast with intra-session network coding (MISNC) problem. However, it is still hard to solve using conventional methods due to the enormous scale of variables or constraints. In this paper, we try to solve this problem in terms of throughput maximization from an algorithmic perspective. We propose a novel formulation based on the extreme flow decomposition technique, which facilitates the design and analysis of approximation and online algorithms. For the offline scenario, we develop a fully polynomial time approximation scheme (FPTAS) which can find a (1 +w) -approximation solution for any specified w > 0. For the online scenario, we develop an online primal-dual algorithm which proves O(1)-competitive and violates link capacities by a factor of O (log m), where m is the link number. The proposed algorithms share an elegant primal-dual form and thereby have inherent advantages of simplicity, efficiency, and scalability. To better understand the proposed approach, we devise delicate numerical examples on an extended butterfly network to validate the effects of algorithmic parameters and make an interesting comparison between the offline and online cases. We also perform large-scale simulations on real networks to validate the effectiveness of the proposed FPTAS and online algorithm. (c) 2023 Elsevier Inc. All rights reserved.
Resource scheduling mechanism of LEO satellite networks is the key to determining communication efficiency. Facing the LEO satellite networks with the dynamic topology changes, varying service requirements, and interm...
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Resource scheduling mechanism of LEO satellite networks is the key to determining communication efficiency. Facing the LEO satellite networks with the dynamic topology changes, varying service requirements, and intermittent inter-satellite links (ISLs), the state-of-the-art cannot achieve high resource efficiency under both heavy and burst traffic loads, and the applicability of parameters design is insufficient under intermittent ISLs. Considering this, we propose a dynamic even distribution mechanism combined with network coding DENC. This novel mechanism obtains the service requirements and allocates resources dynamically through the even distribution algorithm to balance network maintenance overhead and resource waste and improves the success probability of transmission based on network coding to balance retransmission and redundancy. In this paper, we establish performance analysis models to optimize the parameters such as maintenance frequency and coding coefficient. Besides, we construct a system-level simulation platform. Mathematical and simulation results indicate that the resource efficiency of EMNC can be improved by more than 48% compared with SAHN-MAC, ICSMA, CSMA-TDMA, and HTM when all nodes have service needs, and the ISL outage rate is 20%. As the outage probability of ISL increases and the proportion of nodes with service requirements decreases, the performance advantage of EMNC becomes more apparent.
This paper addresses the design of global-scale free-space optics/quantum key distribution (FSO/QKD) networks comprising geostationary (GEO) and low Earth orbit (LEO) satellites. In particular, we present a novel impl...
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Permutation linear network coding (LNC) is a generalization of circular-shift LNC. It has a much richer supply of linear coding operations which can be efficiently implemented. It is known that circular-shift LNC is i...
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Permutation linear network coding (LNC) is a generalization of circular-shift LNC. It has a much richer supply of linear coding operations which can be efficiently implemented. It is known that circular-shift LNC is insufficient to achieve the exact capacity of certain multicast networks. It would be natural to ask whether permutation LNC can achieve the exact capacity of every multicast network. In this letter, we prove that a multicast network has an L-dimensional permutation linear solution over a ring R if and only if it has a scalar linear solution over R. This result implies that the capacity of a multicast network not scalar linearly solvable over R cannot be exactly achieved by permutation LNC either. On the other hand, we unveil, by an explicit instance, the advantage of permutation LNC over circular-shift LNC in terms of the shorter block length to yield a linear solution at rate smaller than 1.
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