In software-defined satellite information networks (SD-SINs), each requested service can be characterized by a predetermined sequence of virtual network functions (VNFs), referred to as a service function chain (SFC). However, VNFs shared by multiple requested services are prone to failures, causing service interruptions. Furthermore, the rapid movement of satellites results in an intermittent yet predictable network topology. Moreover, efficient use of multi-dimensional heterogeneous resources can enhance reliability and network performance. Therefore, in this paper, we investigate reliability-enhanced network slicing by jointly exploiting communication, storage, and computation resources in time-varying SD-SINs. Specifically, we use the time-expanded graph (TEG) to model time-varying SD-SINs with multi-dimensional heterogeneous resources. Based on TEG, we propose a joint reliability-enhanced VNF deployment and flow routing strategy, formulated as an integer nonlinear programming (INLP) problem, to maximize the number of completed services with reliability requirements. To effectively solve the INLP problem, we propose two novel algorithms: the integer linear programming reformulation (ILPR) algorithm, which achieves optimal solutions but with high complexity, and the LP relaxation-based VNF deployment and routing (LPR-VDR) algorithm, which provides near-optimal solutions with significantly lower complexity. Simulation results demonstrate that the LPR-VDR algorithm performs very closely to the ILPR algorithm.

A Orthogonal Time Frequency Space (OTFS) system has demonstrated its potential advantages in high-mobility communication scenarios. A low complexity detection algorithm is always a critical challenge for the OTFS receiver. In this paper, we propose a novel detection chain based low-complexity successive interference cancellation (SIC) symbol detection algorithm for OTFS system. Specifically, we first set a protection interval in OTFS data frames, which can provide some inter-symbol interference (ISI)-free areas in received signals. Based on these ISI-free areas, we can construct the detection chain to determine the detection order of the SIC algorithm based on the association relationship of received signals. Furthermore, in order to mitigate the error propagation of the SIC algorithm, we propose a detection direction selection scheme, which can provide the direction selection gain and significantly improve the performance. Moreover, we analyze the complexity of the proposed SIC algorithm. Compared with many existing detection algorithms, the proposed SIC algorithm can significantly reduce the detection complexity. Simulation results show that in the channel scenario with the line of sight (LOS) path, the proposed algorithm outperforms that of message passing (MP) and maximal ratio combining (MRC) algorithms at high SNRs. Furthermore, in the channel scenario without the LOS path, the proposed algorithm is close to MP and MRC algorithms.

In this paper, we investigate the fundamental tradeoff between computation and communication for the full-duplex (FD) wireless MapReduce distributed computing network. Specifically, a coded interference alignment and neutralization (CIAN) scheme is proposed to significantly reduce the achievable normalized delivery time (NDT) for any given computation load, which jointly exploits both the coding and interference management technologies. In particular, a novel coding strategy is designed to create the coded message desired by multiple nodes, thereby providing the coded multicasting gain. Furthermore, the Shuffle phase is molded as a special cooperative X-multicast network. For this network, a novel IAN scheme is proposed to improve the achievable sum degree of freedom (SDoF), thereby providing the IAN gain. In the proposed CIAN scheme, the fundamental tradeoff between the coded multicasting gain and IAN gain is characterized, and the achievable NDT is minimized by carefully optimizing these two gains. Furthermore, a tight information-theoretic lower bound on the NDT is derived, demonstrating the optimality of the CIAN scheme in some cases. In other cases, the achievable NDT of the CIAN scheme and the lower bound are within a multiplicative gap of 2. Theoretical analysis and numerical results indicate the superior performance of the CIAN scheme compared to existing schemes, particularly by providing additional coded multicasting gain and improved IAN gain.

With the development of wireless communication technology, the structure of wireless communication networks is becoming increasingly complex, and there are various factors that can affect the performance of a wireless communication network. Therefore, we first construct a knowledge graph of wireless communication network. Then, combined with mutual information and the constructed knowledge graph, the factors that have a greater impact on the efficiency of key performance indicators are effectively screened. Finally, a Multi-layer Perceptron (MLP) is used to fit the screening results to verify the effectiveness of the proposed scheme. Based on the data set collected in the real communication scenario, after fitting the first 20 selected factors, the accuracy of the scheme proposed in this paper in the test set is 9.56% higher than that of the Bayesian network and 18.8% higher than that of the PageRank algorithm. On the fitting results of the first 30 selection factors, our method is 1.11% and 1.84% higher than the two methods respectively. Experimental results show that the method in this paper outperforms the Bayesian network as well as PageRank algorithm.

Deep neural networks (DNN) based Channel state information (CSI) fingerprint indoor localization schemes have been widely investigated. However, existing DNN based schemes usually require to collect massive amount of training data samples, which is labor intensive and time consuming. In order to reduce the labor effort and time consumption, in this paper, we propose a Siamese network based CSI fingerprint indoor localization scheme with WiFi (SiFi), which only requires small number of training data samples to achieve a higher localization accuracy. Specifically, in our experiments, with only limited number of training data samples, for localization error less than 0.4m, the proposed SiFi scheme has the probability 82% to fall into this range, while the CNN based scheme only has the probability 67%. The experiment results demonstrate the proposed SiFi scheme has distinct advantages when only small number of training data samples are available.

In this paper, for a general full-duplex wireless MapReduce distributed computing network, we investigate the minimization of the communication overhead for a given computation overhead. The wireless MapReduce framework consists of three phases: Map phase, Shuffle phase and Reduce phase. Specifically, we model the Shuffle phase into a cooperative X network based on a more general file assignment strategy. Furthermore, for this cooperative X network, we derive an information-theoretic upper bound on the sum degree of freedom (SDoF). Moreover, we propose a joint interference alignment and neutralization (IAN) scheme to characterize the achievable SDoF. Especially, in some cases, the achievable SDoF coincides with the upper bound on the SDoF, hence, the IAN scheme provides the optimal SDoF. Finally, based on the SDoF, we present an information-theoretic lower bound on the normalized delivery time (NDT) and achievable NDT of the wireless distributed computing network, which are less than or equal to those of the existing networks. The lower bound on the NDT shows that 1) there is a tradeoff between the computation load and the NDT; 2) the achievable NDT is optimal in some cases, hence, the proposed IAN scheme can reduce the communication overhead effectively.

Sample alignment technique is a key component of vertical federated learning. One of the important solutions for sample alignment is known as private set intersection (PSI). It requires multiple participants to collaboratively compute the intersection from their samples while preserving data security and privacy. However, with a growing number of participants and samples, the communication and computation overhead of the multiparty PSI protocol becomes heavy, which severely impacts the performance of vertical federated learning. To improve the efficiency of sample alignment, this paper proposes a distributed multiparty PSI protocol based on fast Fourier transform (FFT) polynomial interpolation and oblivious pseudo-random function (OPRF). The scheme reduces communication complexity by FFT polynomial interpolation. Meanwhile, it employs OPRF to resist collusion attacks. We evaluate the performance of the scheme in two scenarios: without collusion and arbitrary collusion. The experimental results indicate that the scheme is a practical and efficient solution for sample alignment in vertical federated learning.

Given the network and the time-triggered flow requests of a Time Sensitive Network (TSN), configuring the gate control lists (GCL) of IEEE 802.1Qbv for the ports of each node can be formed as a Job Shop Scheduling Problem, which is NP-hard. At present, most of the existing heuristic solutions for such problems consider scenarios where all given traffic flows can be scheduled. In order to solve the undetermined flow scheduling problem in scenarios no matter whether the flows can be scheduled or not, we propose to maximize the remaining time in conjunction with optimizing the network utilization instead of only minimizing the flowspan. Though the new problem is still NP-hard, it is a unified framework capable of covering general scenarios. On the basis of the new framework, we propose a novel Mixed initial population Genetic Algorithm (MGA) to solve the problem. Extensive simulation evaluation shows that MGA performs better and faster in different network scenarios while other methods prevails only in specific scenarios. This feature makes the method attractive in realistic TSN scheduling applications for in most cases it is hard for users to properly classifying the problem.

Space-air-ground integrated network (SAGIN) aided multi-tier computing network can be modelled as a dynamic and predictable network. For the SAGIN aided multi-tier computing network, the traditional time expanded graph (TEG) can only jointly model communication and storage capability, as well as one computing function for one mission flow within one same node. However, for multiple computing functions for one mission flow in one same node, TEG is not applicable. In this paper, for SAGIN aided multi-tier computing networks, we propose an multi-functional time expanded graph (MF-TEG) to jointly model the communication, storage, and computation capability of nodes where multiple computing functions for one mission flow in one same node can be characterized. Specifically, based on TEG, for each node having computation functions, we adopt the virtual network graph (VNG) to virtually decompose it into three virtual components: sub-virtual node, virtual computing nodes, and virtual transmission links, where the virtual computing node provides the computing function. We characterize the amount of data flow on each link and also present four kinds of fundamental constraints for the data flow in the MF-TEG for joint communication, storage, and computing function: computation capacity constraints, communication capacity constraints, storage capacity constraints, and flow conservation constraints. We provide one example of using MF-TEG to model the SAGIN aided multi-tier computing network with a service function chain (SFC), where satellite nodes could provide communication, storage, and multiple computing functions for one mission flow in one same node, where TEG is not valid. Furthermore, simulation results show that for SAGIN aided multi-tier computing network, the proposed MF-TEG model significantly outperforms the snapshot graph-aided VNG (SSG-aided VNG) model. The reason for that is only communication and computation capability is considered by the SSG-aided VNG model, while storage capability is not exploited.

Space information network (SIN) can provide global coverage in 6G network. Furthermore, SIN with network function virtualization (NFV) can achieve flexible deployment of network functions and improve the utilization of resources. In SIN with NFV, network functions can be virtualized into virtual network functions (VNFs). However, in SIN with NFV, the mission flow must satisfy the service function chain (SFC) constraint, i.e., the mission flow must be processed by all VNFs in the predefined order. Furthermore, each VNF can be deployed on multiple physical nodes. Moreover, different kinds of services may have the diverse quality of service (QoS) requirements. In this paper, we investigate the joint VNFs deployment and flow routing strategy (VNF-R) to maximize the number of completed missions with the guaranteed end-to-end latency under SFC constraints in time-varying SINs. Specifically, the problem can be formulated as a mixed integer linear programming (MILP) problem, which is proved to be NP-hard. In order to effectively solve the problem, we propose a novel low-complexity near-optimal penalty successive upper bound minimization rounding LP relaxation iterative rounding (PSUM-R-LRIR) algorithm. The simulation results show that the PSUM-R-LRIR algorithm can achieve near-optimal performance, and our proposed VNF-R scheme significantly outperforms the fixed VNF deployment scheme.

For the space information network (SIN) with network function virtualization (NFV), a large number of active nodes deployed with virtual network functions (VNFs) impose heavy coordination overhead. In this paper, we investigate the trade-off between the network maximum flow and coordination overhead under the service function chain (SFC) constraints. Specifically, we propose the group sparse joint VNFs deployment and flow routing strategy (GS-VNF-R) to strike the trade-off between the network maximum flow and coordination overhead. Although the GS-VNF-R scheme can be formulated as a convex problem, for a large-scale SIN, solving the GS-VNF-R problem by traditional convex optimizations imposes a heavy computation burden. In order to reduce the time complexity, we propose a novel optimal low-complexity block-successive upper-bound minimization method of multipliers based group sparse (BSUM-M-GS) algorithm, which can converge to the global optimal with much less complexity. Simulation results show that for some scenarios, 60% of active nodes can be saved by using the proposed GS-VNF-R scheme without any performance loss compared to the full cooperation scheme, which results in significant cooperation overhead reduction. Moreover, simulation results demonstrate that our proposed BSUM-M-GS algorithm can significantly reduce the complexity to the extent of 7 orders of magnitude for some scenarios.

In this paper, we investigate the maximum flow routing strategy with the service function chain (SFC) constraints in the space information networks (SINs), where a SFC consists of a specific ordered sequence of service functions, and the mission flow must go through these functions in a predefined order. The time-varying SIN is modeled by the time-expanded graph (TEG). We formulate the maximum flow routing strategy problem with the SFC constraints as a linear programming (LP) problem. Furthermore, for a large-scale SIN, as the complexity of solving the LP problem is still very high, we propose a novel low-complexity SFC-constrained graph theory based (SFC-GT) algorithm. Specifically, we formulate this problem as one special single commodity maximum flow problem, where this flow must satisfy the SFC constraints. We first define the SFC-constrained residual network and the SFC-constrained augmenting path. Afterwards, we iteratively search the SFC-constrained augmenting path and update the SFC-constrained residual network. Simulation results demonstrate our proposed SFC-GT algorithm can achieve near-optimal performance with much less complexity.

In this paper, we consider the joint base station (BS) association, power control, and beamforming problem for an uplink SISO/SIMO cellular network under the max-min fairness criterion. We first prove a strange discrepancy: a normalized fixed point (NFP) iterative algorithm has geometric convergence to global optima, but it only has pseudo-polynomial time complexity and thus whether the problem is NP-hard or not is an open question. In this paper, we resolve this discrepancy by proving that this problem is indeed polynomial-time solvable. Our proof is based on converting this mixed integer programming (MIP) problem to a series of auxiliary convex problems. Our results fill in a gap in the understanding of the computational complexity of BS association problem. Another implication of our result is that the uplink SIMO problem is easy, but either changing uplink to downlink or changing SIMO to MIMO will make the problem NP-hard. Empirically, the polynomial time algorithm converges much slower than the NFP algorithm, leaving open the question of whether a polynomial time algorithm that converges fast in practice exists for this problem.

In this paper, we analyze the feasibility of interference alignment (IA) schemes with T finite channel extensions for multi-input-multi-output (MIMO) interference broadcast channel (IBC) with common messages, where each base station transmits one common single data stream to all users within its cell. We investigate necessary conditions of feasibility of the IA schemes for this network in three different scenarios, i.e., with no channel extensions (T = 1), with T (T ≥ 2) channel extensions for a positive measure of channel coefficients, and with T (T ≥ 2) dependent channel extensions. These necessary conditions impose constraints on system configurations, e.g., the number of users, the number of antennas, the number of cells, and the number of channel extensions, which must be satisfied when the IA schemes are feasible, and also implicitly provide constraints on the upper bound of degrees of freedom (DoF) with finite channel extensions.

In this paper, we investigate the interference alignment (IA) for a class of partially connected multi-input multi-output heterogeneous networks, which consist of L fully connected pico cells, J partially connected pico cells, and one macro cell. In particular, we investigate the feasibility of IA conditions with no channel extensions, T-independent channel extensions, and T-dependent channel extensions, respectively, for the single beam case. We present three theorems to characterize some necessary conditions, which L, J, T, and the channel diversity order L̅ must jointly satisfy when IA conditions are feasible. These necessary conditions also implicitly impose the constraints on the upper bounds of achievable degrees of freedom with different channel extensions.

In this paper, a joint power and resource block (RB) allocation (JPRBA) algorithm with low complexity is proposed, which addresses the intra-and-inter-cell interference management problem for a multicell device-to-device (D2D) communication underlaying LTE-Advanced network. We first introduce a power control and resource allocation vector (PORAVdm) to each D2D transmitter, and the set of all PORAVdmhas two functions: one is to select appropriate reused RBs for each D2D link, whereas the other is to determine the optimal power for D2D transmitters on each selected RB. To obtain the appropriate PORAVdms, we exploit the group sparse structure to formulate a sum rate maximization problem (referred to as the group least absolute shrinkage and selection operator programming). Then we derive the stationary solution by solving its equivalent sparse weighted mean square error minimization problem. Finally, simulation results show that the proposed JPRBA algorithm can efficiently improve the total throughput.

The optimal weighting matrix design at the relay node for the V-BLAST based amplify-and-forward (AF) MIMO relay transmission scheme has been intensively investigated in literature. However, there are few papers considering the optimal design of the weighting matrix at the relay node for orthogonal space time block codes (OSTBCs) based AF MIMO relay transmission scheme. In this paper, the maximum-SNR weighting matrix at the relay node is designed for the OSTBC based AF MIMO relay scheme, so that for the first time we can quantify the SNR gain by optimizing the weighting matrix at the relay node for the OSTBC based AF MIMO relay scheme. Specifically, the canonical form of the optimal linear weighting matrix is derived by exploiting matrix inequality theory and majorization theory. Furthermore, we propose a simulation based Gamma approximation method for diversity order analysis. The simulation results show that the proposed optimal weighting matrix significantly outperforms the traditional scalar-gain weighting matrix for the OSTBC based AF MIMO relay scheme.

In this work, we study the problem of interference in a two-tiered uplink network which contains a macro-cell and multiple small-cells. We null the interference from small-cells to macro-cell by using Vandermonde-subspace frequency division multiplexing (VFDM). VFDM can exploit frequency selectivity and null space generated by the cyclic prefixes (CP) used by the macro-cell communication. We use an interference alignment (IA) scheme with an extension of a grouping method to align the interference between multiple small-cells into a lower dimensional-subspace, by using this grouping method we reduce the number of receive antennas required to null interference. Finally, we derive the achievable degrees of freedom (DoF).

In cognitive radio networks, the available spectrum holes are usually discontinuous. Hence, it is hard to exploit these spectrum holes because the bandwidth of an individual one may not be able to satisfy the wide bandwidth requirement imposed by secondary users (SUs). Spectrum aggregation (SA) enables SUs to integrate several spectrum holes into one channel with wide bandwidth which may support high bandwidth requirement. In this paper, we investigate the problem of SA based spectrum allocation. Specifically, we cast this problem into Multiple Knapsack Problems (MKP). Based on this scheme, we propose a spectrum aggregation algorithm to maximize the available bandwidth that SUs can access, as well as two spectrum allocation algorithms including an optimal algorithm and a suboptimal one. The proposed algorithms are evaluated in terms of total available bandwidth and spectrum utilization efficiency. Numerical results show that the proposed algorithms can significantly outperform the existing algorithms.

In this contribution, we derive the Discrete-input Continuous-output Memoryless Channel (DCMC) capacity of the non-regenerative Multiple-Input Multiple-Output (MIMO) relay channel, when the source-to-destination link is inferior and hence considered absent. We design near-capacity Forward Error Correction (FEC) codes for approaching this capacity limit. It is shown that our design is capable of approaching the DCMC capacity within 0.4dB, when communicating over uncorrelated Raleigh fading channels, where the source node, relay node and destination node are equipped with two antennas each.

A novel block diagonalisation (BD)-based Multiuser multiple input multiple output (MU-MIMO)-aided relaying scheme is proposed for downlink transmissions, which does not require any channel state information at the base station (BS) and decomposes a MU-MIMO-aided relaying system into several parallel single-user MIMO-assisted relaying schemes. Furthermore, based on the proposed algorithm the optimal linear processing matrix designed for the maximum achievable capacity is derived, which significantly outperforms the so-called naive weighting matrix.

A full-duplex uplink/downlink (UL/DL) system having a UL transmitter/receiver (transceiver) pair and a DL transceiver is considered. Both the UL and DL are improved. The DL is enhanced by a novel vector perturbation (VP)-aided singular value decomposition (SVD)-based precoding scheme, which is capable of exploiting the different-quality SVD eigenbeams and substantially reduces the overall average transmit power requirement of the traditional zero-forcing (ZF) precoder. By contrast, the UL is enhanced by a lattice reduction (LR)-aided receiver scheme designed for SVD-based multiuser multiple-input multiple-output (MU MIMO) UL transmission, when different modulation schemes are employed for different-quality eigenbeams. This new UL scheme avoids the noise-enhancement problem of the classic ZF UL receiver. The authors demonstrate that the proposed VP-aided DL and LR-assisted UL constitute a powerful full-duplex system, which achieves an ∼15 dB signal to noise ratio (SNR) gain for both SVD-based MU-MIMO DL and UL transmissions over the traditional ZF-aided schemes at a bit error rate (BER) of 10−3.

Vector perturbation (VP) is a prominent precoding technique attracted a lot of attention in recent years. Until now, however, various extended VP techniques proposed to apply in multiuser precoding are almost restricted to one antenna configuration of each user. The restriction does not meet the development of next generation wireless systems. So, the well-known block diagonal (BD) algorithm and VP is naturally combined and proposed, named BD-VP for short, to solve this problem. However, the BD-VP completely suppressing multi user interference (MUI) at the expense of noise enhancement results in performance degradation. To overcome the shortcoming of BD-VP, we propose generalized channel inversion VP (GCI-VP) algorithms. Analysis and simulation results show that the proposed ZF GCI-VP is equivalent to the BD-VP, while the algorithm MMSE GCI-VP I and MMSE GCI VP II greatly outperform the BD-VP.

In point to point MIMO systems, uniform channel decomposition (UCD) has been proven to be optimal in BER performance and strictly capacity lossless when perfect channel state information (CSI) are assumed to be available at both the transmitter and receiver side. However, in practice, CSI is always contaminated by channel estimation error. In this paper, we proposed a novel Robust UCD scheme which is capable of improving the BER and capacity performance in the context of imperfect CSI compared with the conventional UCD scheme. We also derived the capacity lower bound of the MIMO channel using the Robust UCD scheme with channel estimation error.