Low capacity and reliability are the challenges in the development of ionosphere communication channel systems. To overcome this problem, one promising and state-of-the-art method is applying a multi-carrier modulation technique. Currently, the use of multi-carrier modulation technique is using a single transmission frequency with a bandwidth is no more than 24 kHz in real-world implementation. However, based on the range of the minimum and maximum ionospheric plasma frequency values, which could be in the MHz range, the use of these values as the main bandwidth in multi-carrier modulation techniques can optimize the use of available channel capacity. In this paper, we propose a multi-carrier modulation technique in combination with a model variation of Lowest Usable Frequency (LUF) and Maximum Usable Frequency (MUF) values as the main bandwidth to optimize the use of available channel capacity while also maintaining its reliability by following the variation of the ionosphere plasma frequency. To analyze its capacity and reliability, we performed a numeric simulation using a LUF-MUF model based on Long Short Term-Memory (LSTM) and Advanced Stand Alone Prediction System (ASAPS) in Near Vertical Incidence Skywave (NVIS) propagation mode with the assumption of perfect synchronization between transmitter and receiver with no Doppler and no time offsets. The results show the achievement of the ergodic channel capacity varies for every hour of the day, with values in the range of 10 Mbps and 100 Mbps with 0 to 20 dB SNR. Meanwhile, the reliability of the system is in the range of 8% to 100% for every hour of one day based on two different Mode Reliability calculation scenarios. The results also show that channel capacity and system reliability optimization are determined by the accuracy of the LUF-MUF model.

This paper investigates a service deployment model for network function virtualization which handles per-flow priority to minimize the deployment cost. Service providers need to implement network services each of which consists of one or more virtual network functions (VNFs) with satisfying requirements of service delays. In our previous work, we studied the service deployment model with per-host priority; flows belonging to the same service, for the same VNF, and handled on the same host have the same priority. We formulated the model as an optimization problem, and developed a heuristic algorithm named FlexSize to solve it in practical time. In this paper, we address per-flow priority, in which flows of the same service, VNF, and host have different priorities. In addition, we expand FlexSize to handle per-flow priority. We evaluate per-flow and per-host priorities, and the numerical results show that per-flow priority reduces deployment cost compared with per-host priority.

This paper proposes a robust optimization model for probabilistic protection under uncertain capacity demands to minimize the total required capacity against multiple simultaneous failures of physical machines. The proposed model determines both primary and backup virtual machine allocations simultaneously under the probabilistic protection guarantee. To express the uncertainty of capacity demands, we introduce an uncertainty set that considers the upper bound of the total demand and the upper and lower bounds of each demand. The robust optimization technique is applied to the optimization model to deal with two uncertainties: failure event and capacity demand. With this technique, the model is formulated as a mixed integer linear programming (MILP) problem. To solve larger sized problems, a simulated annealing (SA) heuristic is introduced. In SA, we obtain the capacity demands by solving maximum flow problems. Numerical results show that our proposed model reduces the total required capacity compared with the conventional model by determining both primary and backup virtual machine allocations simultaneously. We also compare the results of MILP, SA, and a baseline greedy algorithm. For a larger sized problem, we obtain approximate solutions in a practical time by using SA and the greedy algorithm.

A sub-Terahertz band is envisioned to play a great role in 6G to achieve extreme high data-rate communication. In addition to very wide band transmission, we need spatial multiplexing using a hybrid MIMO system. A recently presented paper, however, reveals that the number of observed multipath components in a sub-Terahertz band is very few in indoor environments. A channel with few multipath components is called sparse. The number of layers (streams), i.e. multiplexing gain in a MIMO system does not exceed the number of multipaths. The sparsity may restrict the spatial multiplexing gain of sub-Terahertz systems, and the poor multiplexing gain may limit the data rate of communication systems. This paper describes fundamental considerations on sub-Terahertz MIMO spatial multiplexing in indoor environments. We examined how we should steer analog beams to multipath components to achieve higher channel capacity. Furthermore, for different beam allocation schemes, we investigated eigenvalue distributions of a channel Gram matrix, power allocation to each layer, and correlations between analog beams. Through simulation results, we have revealed that the analog beams should be steered to all the multipath components to lower correlations and to achieve higher channel capacity.

This paper investigates the joint problem of user association and spectrum allocation in satellite-terrestrial integrated networks (STINs), where a low earth orbit (LEO) satellite access network cooperating with terrestrial networks constitutes a heterogeneous network, which is beneficial in terms of both providing seamless coverage as well as improving the backhaul capacity for the dense network scenario. However, the orbital movement of satellites results in the dynamic change of accessible satellites and the backhaul capacities. Moreover, spectrum sharing may be faced with severe co-channel interferences (CCIs) caused by overlapping coverage of multiple access points (APs). This paper aims to maximize the total sum rate considering the influences of the dynamic feature of STIN, backhaul capacity limitation and interference management. The optimization problem is then decomposed into two subproblems: resource allocation for terrestrial communications and satellite communications, which are both solved by matching algorithms. Finally, simulation results show the effectiveness of our proposed scheme in terms of STIN's sum rate and spectrum efficiency.

This paper presents robust optimization models for minimizing the required backup capacity while providing probabilistic protection against multiple simultaneous failures of physical machines under uncertain virtual machine capacities in a cloud provider. If random failures occur, the required capacities for virtual machines are allocated to the dedicated backup physical machines, which are determined in advance. We consider two uncertainties: failure event and virtual machine capacity. By adopting a robust optimization technique, we formulate six mixed integer linear programming problems. Numerical results show that for a small size problem, our presented models are applicable to the case that virtual machine capacities are uncertain, and by using these models, we can obtain the optimal solution of the allocation of virtual machines under the uncertainty. A simulated annealing heuristic is presented to solve large size problems. By using this heuristic, an approximate solution is obtained for a large size problem.

Heterogeneous networks (HetNets) are emerging as an inevitable method to tackle the capacity crunch of the cellular networks. Due to the complicated network environment and a large number of configured parameters, coverage and capacity optimization (CCO) is a challenging issue in heterogeneous cellular networks. By combining the self-optimizing algorithm for radio frequency (RF) parameters with the power control mechanism of small cells, the CCO problem of self-organizing network is addressed in this paper. First, the optimization of RF parameters is solved based on reinforcement learning (RL), where the base station is modeled as an agent that can learn effective strategies to control the tunable parameters by interacting with the surrounding environment. Second, the small cell can autonomously change the state of wireless transmission by comparing its distance from the user equipment with the virtual cell size. Simulation results show that the proposed algorithm can achieve better performance on user throughput compared to different conventional methods.

This paper introduces a method to estimate latent traffic from its origin to destination from the link packet loss rate and traffic volume. In addition, we propose a method for the joint optimization of routing and link provisioning based on the estimated latent traffic. Observed traffic could deviate from the original traffic demand and become latent when the traffic passes through congested links because of changes in user behavioral and/or applications as a result of degraded quality of experience (QoE). The latent traffic is actualized by improving congested link capacity. When link provisioning is based on observed traffic, actual traffic might cause new congestion at other links. Thus, network providers need to estimate the origin-destination (OD) original traffic demand for network planning. Although the estimation of original traffic has been well studied, the estimation was only applicable for links. In this paper, we propose a method to estimate latent OD traffic by combining and expanding techniques. The method consists of three steps. The first step is to estimate the actual OD traffic and loss rate from the actual traffic and packet loss rate of the links. The second step is to estimate the latent traffic demand. Finally, using this estimated demand, the link capacity and routing matrix are optimized. We evaluate our method by simulation and confirm that congestion could be avoided by capacity provisioning based on estimated latent traffic, while provisioning based on observed traffic retains the congestion. The combined method can avoid congestion with an increment of 23% compared with capacity provisioning only. We also evaluated our method's adaptability, i.e., the ability to estimate the required parameter for the estimations using fewer given values, but values obtained in the environment.

This paper proposes an optimization model under uncertain traffic demands to design the backup network to minimize the total capacity of a backup network to protect the primary network from multiple link failures, where the probability of link failure is specified. The hose uncertainty is adopted to express uncertain traffic demands. The probabilistic survivability guarantee is provided by determining both primary and backup network routing, simultaneously. Robust optimization is introduced to provide probabilistic survivability guarantees for different link capacities in the primary network model under the hose uncertainty. Robust optimization in the proposed model handles two uncertain items: uncertain failed primary link with different capacities and uncertain traffic demands. We formulate an optimization problem for the proposed model. Since it is difficult to directly solve it, we introduce a heuristic approach for the proposed model. By using the heuristic approach, we investigate how the probability of link failure affects both primary and backup network routing. Numerical results show that the proposed model yields a backup network with lower total capacity requirements than the conventional model for the link failure probabilities examined in this paper. The results indicate that the proposed model reduces the total capacity of the backup network compared to the conventional model under the hose uncertainty. The proposed model shares more effectively the backup resources to protect primary links by determining routing in both primary and backup networks.

In this paper, we investigate the subcarriers combination selection and the subcarriers activation of OFDM-IM system. Firstly, we propose an algorithm to solve the problem of subcarriers combination selection based on the transmission rate and diversity gain. Secondly, we ropose a more concise algorithm to solve the problem of power allocation and carrier combination activation probability under this combination to improve system capacity. Finally, we verify the robustness of the algorithm and the superiority of the system scheme in the block error rate (BLER) and system capacity by numerical results.

In this study, we investigate fundamental trade-off among identification, secrecy, template, and privacy-leakage rates in biometric identification system. Ignatenko and Willems (2015) studied this system assuming that the channel in the enrollment process of the system is noiseless and they did not consider the template rate. In the enrollment process, however, it is highly considered that noise occurs when bio-data is scanned. In this paper, we impose a noisy channel in the enrollment process and characterize the capacity region of the rate tuples. The capacity region is proved by a novel technique via two auxiliary random variables, which has never been seen in previous studies. As special cases, the obtained result shows that the characterization reduces to the one given by Ignatenko and Willems (2015) where the enrollment channel is noiseless and there is no constraint on the template rate, and it also coincides with the result derived by Günlü and Kramer (2018) where there is only one individual.

This paper investigates the resource allocation problem for the downlink of non-orthogonal multiple access (NOMA) networks. A novel resource allocation method is proposed to deal with the problem of maximizing the system capacity while taking into account user fairness. Since the optimization problem is nonconvex and intractable, we adopt the idea of step-by-step optimization, decomposing it into user pairing, subchannel and power allocation subproblems. First, all users are paired according to their different channel gains. Then, the subchannel allocation is executed by the proposed subchannel selection algorithm (SSA) based on channel priority. Once the subchannel allocation is fixed, to further improve the system capacity, the subchannel power allocation is implemented by the successive convex approximation (SCA) approach where the nonconvex optimization problem is transformed into the approximated convex optimization problem in each iteration. To ensure user fairness, the upper and lower bounds of the power allocation coefficients are derived and combined by introducing the tuning coefficients. The power allocation coefficients are dynamically adjustable by adjusting the tuning coefficients, thus the diversified quality of service (QoS) requirements can be satisfied. Finally, simulation results demonstrate the superiority of the proposed method over the existing methods in terms of system performance, furthermore, a good tradeoff between the system capacity and user fairness can be achieved.

Two-ray (TR) fading model is one of the fading models to represent a worst-case fading scenario. We derive the exact closed-form expressions of the generalized moment generating function (G-MGF) for the TR fading model, which enables us to analyze the numerous types of wireless communication applications. Among them, we carry out several analytical results for the TR fading model, including the exact ergodic capacity along with asymptotic expressions and energy detection performance. Finally, we provide numerical results to validate our evaluations.

We propose a coding/decoding strategy that surpasses the symmetric information rate of a binary insertion/deletion (ID) channel and approaches the Markov capacity of the channel. The proposed codes comprise inner trellis codes and outer irregular low-density parity-check (LDPC) codes. The trellis codes are designed to mimic the transition probabilities of a Markov input process that achieves a high information rate, whereas the LDPC codes are designed to maximize an iterative decoding threshold in the superchannel (concatenation of the ID channels and trellis codes).

This paper proposes an optimization approach that designs the backup network with the minimum total capacity to protect the primary network from random multiple link failures with link failure probability. In the conventional approach, the routing in the primary network is not considered as a factor in minimizing the total capacity of the backup network. Considering primary routing as a variable when deciding the backup network can reduce the total capacity in the backup network compared to the conventional approach. The optimization problem examined here employs robust optimization to provide probabilistic survivability guarantees for different link capacities in the primary network. The proposed approach formulates the optimization problem as a mixed integer linear programming (MILP) problem with robust optimization. A heuristic implementation is introduced for the proposed approach as the MILP problem cannot be solved in practical time when the network size increases. Numerical results show that the proposed approach can achieve lower total capacity in the backup network than the conventional approach.

This letter investigates the secure transmission improvement scheme for indoor visible light communications (VLC) by using the protected zone. Firstly, the system model is established. For the input signal, the non-negativity and the dimmable average optical intensity constraint are considered. Based on the system model, the secrecy capacity for VLC without considering the protected zone is obtained. After that, the protected zone is determined, and the construction of the protected zone is also provided. Finally, the secrecy capacity for VLC with the protected zone is derived. Numerical results show that the secure performance of VLC improves dramatically by employing the protected zone.

In this study, product of two independent and non-identically distributed (i.n.i.d.) random variables (RVs) for κ-µ fading distribution and α-µ fading distribution is considered. The statistics of the product of RVs has been broadly applied in a large number of communications fields, such as cascaded fading channels, multiple input multiple output (MIMO) systems, radar communications and cognitive radios (CR). Exact close-form expressions of probability density function (PDF) and cumulative distribution function (CDF) with exact series formulas for the product of two i.n.i.d. fading distributions κ-µ and α-µ are deduced more accurately to represent the provided product expressions and generalized composite multipath shadowing models. Furthermore, ergodic channel capacity (ECC) is obtained to measure maximum fading channel capacity. At last, interestingly unlike κ-µ, η-µ, α-µ in [9], [17], [18], these analytical results are validated with Monte Carlo simulations and it shows that for provided κ-µ/α-µ model, non-linear parameter has more important influence than multipath component in PDF and CDF, and when the ratio between the total power of the dominant components and the total power of the scattered waves is same, higher α can significantly improve channel capacity over composite fading channels.

For high transmission efficiency, good modulation schemes are expected. This paper focuses on the enhancement of the modulation scheme of free space optical turbo coded system. A free space optical turbo coded system using a new signaling scheme called hybrid PPM-OOK signaling (HPOS) is proposed and investigated. The theoretical formula of the bit error rate of the uncoded HPOS system is derived. The effective information rate performances (i.e. channel capacity) of the proposed HPOS turbo coded system are evaluated through computer simulation in free space optical channel, with weak, moderate, strong scintillation. The performance of the proposed HPOS turbo coded system is compared with those of the conventional OOK (On-Off Keying) turbo coded system and BPPM (Binary Pulse Position Modulation) turbo coded system. As results, the proposed HPOS turbo coded system shows the same tolerance capability to background noise and atmospheric turbulence as the conventional BPPM turbo coded system, and it has 1.5 times larger capacity.

A novel adaptive cross-layer optimal power allocation (OPA) scheme over physical layer and data-link layer for two-way relaying system with amplify-and-forward policy (TWR-AF) is proposed in this paper. Our goal is to find the optimal power allocation factors under each channel state information (CSI) to maximize the sum throughput of two sources under total transmit power constraint in the physical layer while guaranteeing the statistical delay quality-of-service (QoS) requirement in the data-link layer. By integrating information theory with the concept of effective capacity, the OPA problem is formulated into an optimization problem to maximize the sum effective capacity. It is solved through Lagrange multiplier approach, and the optimal power allocation factors are presented. Simulations are developed and the results show that the proposed cross-layer OPA scheme can achieve the best sum effective capacity with relatively low complexity when compared with other schemes. In addition, the proposed cross-layer OPA scheme achieves the maximal sum effective capacity when the relay is located in (or near) the middle of the two source nodes, and the sum effective capacity becomes smaller when the difference between two QoS exponents becomes larger.

This paper analyzes the performance of single-cell massive multiple-input multiple-output (MIMO) systems with non-orthogonal pilots. Specifically, closed-form expressions of the normalized channel estimation error and achievable uplink capacity are derived for both least squares (LS) and minimum mean square error (MMSE) estimation. Then a pilot reconstruction scheme based on orthogonal Procrustes principle (OPP) is provided to reduce the total normalized mean square error (NMSE) of channel estimations. With these reconstructed pilots, a two-step pilot assignment method is formulated by considering the correlation coefficient among pilots to reduce the maximum NMSE. Based on this assignment method, a step-by-step pilot power allocation scheme is further proposed to improve the average uplink signal-to-interference and noise ratio (SINR). At last, simulation results demonstrate the superiority of the proposed approaches.