Sector coupling emerges as a potentially efficient strategy for emission reduction, particularly when the power sector is sufficiently decarbonized. This study aims to explore the effects of sector coupling on the decarbonization of the manufacturing sector. This study also develops the hybrid energy system model by integrating bottom-up energy system models for the power and manufacturing sectors with the computable general equilibrium model. The hybrid model helps to explain the integration of the power, hydrogen, and manufacturing sectors. The manufacturing sector is electrified and integrated with other sectors based on its use of decarbonized electricity and electrolysis hydrogen. With a carbon tax rate of 200,000 Korean won (161 U S. dollars) per ton of carbon dioxide equivalent, the percentage of electricity in the manufacturing sector's energy mix is expected to increase from 30 % to 46 % by 2050 if electrolysis hydrogen is considered. Moreover, emission reduction from sector coupling accounts for more than 50 % of the total emission reduction in the manufacturing sector. If policymakers consider the overall benefit of decarbonizing the power sector and aid the development of power-to-X technologies, sector coupling can contribute the decarbonization of the manufacturing sector significantly.

In this paper, we investigate a problem of optimal capacities of energy storage system for the residential users and an optimal unit price energy storage system for an aggregator. We suppose that the residential users have own photovoltaic generation system and a smart meter which can schedule activation of home appliances and controls. The aggregator participates in energy market to maximize his profit by selling the storage to the residential users. Each user determines his energy consumption schedule and a required amount of storage to minimize his energy cost depending on the unit price of energy storage, price profile of electricity from the main grid and his renewable power generation capacity. We consider electricity bill from main grid and storage bill from the aggregator as users' energy cost. We formulate a problem for the aggregator to decide an optimal unit price of energy storage and a problem for each user to decide energy consumption schedule and a required amount of storage capacity. With numerical investigation, it is shown that the energy storage can reduce the energy load to main grid and shave peak power. As a result, by purchasing energy storage, users can save their energy cost by 43% in average compared to the case without energy storage.

In this paper, we consider a smart grid network where customers have their own photovoltaic generation system (PVS) but an energy storage system (ESS) is shared. The energy generated in PVS located at customer n's home can be immediately used for customer n at that time or be stored in the shared ESS. Customers all belongs to the same entity or different entities with common interest and seek a common goal. We find an optimal capacity of shared ESS and individual photovoltaic generation system minimizing the total energy cost. For a predetermined daily pattern of generation and load, we formulate an liner programming problem to decide an optimal capacity of the shared ESS minimizing the total energy cost. The total energy cost is the sum of expenses to buy electricity and to install the shared ESS. Numerical examples show that as compared with the case of individually installed ESS for each customer, the shared ESS can decrease the electricity demand to the main grid and the total energy cost is slightly reduced while meet the demand of electricity by appliances.

This paper presents a price-searching model in which a source node (Alice) seeks friendly jammers that prevent eavesdroppers (Eves) from snooping legitimate communications by generating interference or noise. Unlike existing models, the distributed jammers also have data to send to their respective destinations and are allowed to access Alice's channel if it can transmit sufficient jamming power, which is referred to as collaborative jamming in this paper. For the power used to deliver its own signal, the jammer should pay Alice. The price of the jammers' signal power is set by Alice and provides a tradeoff between the signal and the jamming power. This paper presents, in closed-form, an optimal price that maximizes Alice's benefit and the corresponding optimal power allocation from a jammers' perspective by assuming that the network-wide channel knowledge is shared by Alice and jammers. For a multiple-jammer scenario where Alice hardly has the channel knowledge, this paper provides a distributed and interactive price-searching procedure that geometrically converges to an optimal price and shows that Alice by a greedy selection policy achieves certain diversity gain, which increases log-linearly as the number of (potential) jammers grows. Various numerical examples are presented to illustrate the behavior of the proposed model.

WiFi offloading is a cost-effective way of alleviating the problem of highly congested cellular networks. In this paper, we consider WiFi offloading problem in an integrated cellular WiFi system consisting of mobile base station (MBS) and WiFi access point (AP). We propose a probabilistic offloading scheme, where cellular packets that arrive at the queue of MBS are offloaded to the queue of WiFi AP with an offload probability. The offload probability is determined to minimize the average delay experienced by the cellular packets while guaranteeing stability of both cellular and WiFi system. We model the arrivals and fulfillments of data services of a cellular operator as an M/M/1 queue. We investigate two priority disciplines: First-In-First-Out (FIFO) and Non-Preemptive Priority Rule (NPPR). We provide an exact optimal offload probability for FIFO, but present an upper bound on the optimal probability for NPPR. Numerical investigation is used to verify the optimality of the proposed solutions, to examine the effect of packet arrival rate of MBS and compare the average delays for FIFO and NPPR.

Mobile data offloading plays an important role to alleviate congestion and make better use of available network resources. When a mobile network operator (MNO) wants to offload its cellular traffic to multiple WiFi access points (APs), the problem of how much traffic is allocated to each APs arises. In this paper, a utility function of MNO having the Cobb-Douglas form is constructed. A convex optimization problem to maximize the utility is formulated to decide the optimal volume of offload traffic onto APs. An exact optimal volume of offload traffic is provided by solving Karush-Kuhn-Tucker (KKT) conditions. Also we show that the optimal utility is convex function of a utility parameter for an efficiency of AP, which reflects the performance of wireless connectivity, the placement and the operating environment of an AP, etc. Numerical investigation verifies the optimality of the proposed offload traffic allocation and gives an insight that how the optimal utility changes depending on the utility parameter.

In this paper, we investigate a multiple-sensor relaying network where a relay helps multiple sensor-destination pairs. We propose probabilistic relaying scheme and provide an optimal relay sharing ratio for the proposed scheme. Based on statistical channel information, the maximum outage probability between all users is minimized. Numerical results show that the proposed probabilistic relaying scheme outperforms no-relay scheme and exclusive relaying scheme .

For wireless multi-sensor networks with a single decode-and-forward (DF) relay, this letter provides an optimal power allocation that minimizes the total sum of costs of sensor- and relay-transmitting powers and recharging the battery in the sensors, which is referred to operational power cost. We assume a remote-area wireless sensor application and hence that the direct communication between the sensors and a destination does not exist. Motivated by an observation that the proposed power allocation is a function of the location of relay, we also provide a near-optimal location of the relay that minimizes the operational cost. Optimality of the proposed solutions is verified with numerical investigation. Numerical results show that the proposed power allocation saves the total cost by nearly up to 15% compared to the previous source-sum-power minimizing allocation when the relay location is fixed. Furthermore, the proposed relay location is shown to save the cost by about 59-77% compared to randomly-chosen relay locations.

This paper proposes a cost-aware opportunistic relaying scheme to minimize total cost in decoding and forwarding (DF) relay networks with power constraints. In the proposed scheme, a relay is selected such that it costs minimally among relays which correctly decode the signal from the source and have sufficient power to transmit the signal to the destination. We derive the outage probability and the average total cost of the proposed relay selection scheme. By means of simulation we show that the numerical results perfectly match our theoretical analysis. Simulations demonstrate that the proposed scheme can effectively reduce total cost compared to the best relay selection scheme. It is also shown that there exists an optimal source power which minimizes the average total cost.

In this study, the authors investigate a maximum incentive that a primary user (PU) can charge a secondary user (SU) on SU's power consumption devoted to delivering its own signal. As a constraint, the PU also should achieve its data rate at a predefined level. Working as a half-duplex relay, SU hears PU's signal in the first phase and sends its own signal superimposed on PU's signal in the second phase. SU optimises power allocation between the two signals in the second phase, accounting for the expense-benefit trade-off in sending its own signal. Depending on SU's receiver mode or the number of SU's receiving nodes, SU's decision on power allocation is differently optimised. The authors also provide optimal incentive for PU by optimal pricing for different occasions. The optimality is proved in this study. Since the optimality is purchased in practice when PU has perfect knowledge of the SU's response on the PU-issuing price, the authors also propose an efficient interactive price-searching protocol between distributed PU and SU. Numerical investigations are given to verify and illustrate the optimality of the proposed price as well as the protocol.

We consider a wireless sensor network where a relay helps multiple source-destination pairs. The relay helps communication pairs in a probabilistic manner during several relaying slots. We formulate an optimization problem to find slot-sharing ratio maximizing an upper bound of the expected sum rate while the upper bound of each user's rate satisfying a predetermined target for the given number of relaying slots. An exact optimal value of slot-sharing ratio is provided by using Karush-Kuhn-Tucker (KKT) conditions. And we propose an algorithm to search the optimal number of relaying slots. Numerical results are presented to illustrate the optimal slotsharing ratio and the optimal number of relaying slots.

The aim of this paper is to design a bandwidth-efficient multiuser network. We consider a wireless sensor network where a relay helps multiple source-destination pairs which require a minimum data rate. The relay helps communication pairs in probabilistic manner during relaying slots. In the proposed scheme, the relay does not need orthogonal channels (time slots) as many as the number of source-destination pairs but the channels are shared by the relay with some probability. We optimize the slot-sharing ratio to maximize the expected sum rate while guaranteeing the minimum rate for a given number of relaying slots. Numerical examples show that the proposed relaying scheme improves the expected data rate even though the number of relaying slots is less than the number of source-destination pairs.

This paper considers minimizing source-sum-power consumption under outage constraints in a wireless multiple-sensor single-relay network with direct link communication. We consider decode-and-forward (DF) relaying. It is difficult to find the exact expression of the optimal solution to the problem. A suboptimal power allocation and lower bound of the minimum source power consumption are provided. Numerical results show that the performance gap between the proposed suboptimal power allocation and the lower bound becomes small as relay power capacity increases.

In this paper, we consider cooperative communication by secondary users in a multicast primary network. Secondary users act as relays for primary network and are allowed to access primary channel during a part of the transmission time. We consider that the secondary system pays incentive to encourage the primary system in transmission time sharing. We formulate an optimization problem to find the time allocation between the primary and the secondary transmission to maximize the primary utility while keeping the secondary utility above certain level. However, the optimal time allocation problem turns out to be intractable. We simplify the optimal time allocation problem by approximation. The simplified problem falls into a linear programming and is easily solved. Numerical examples are presented to give insight of optimal time sharing and to unveil the impact of system characteristics on.

This letter provides an exact and optimal power allocation for a sensor network that consists of multiple sensor-destination pairs and a single relay. The objective of the power allocation is to minimize the sum of transmitting powers by the sensors that have a signal-to-noise-ratio (SNR) constraint. After checking the feasibility of power allocation, an optimal solution is provided using Karush-Kuhn-Tucker (KKT) condition. With numerical investigation, the optimality of the proposed solution is illustratively verified and it is also shown that the extent of reduction in the sum-power consumption achieved by introducing the relay depends on the available relaying power and the number of sources.

This letter focuses on a cognitive radio network where two primary frequency channels (PCs) are opportunistically used by a secondary user (SU). Before transmitting the SU senses, during a given period of time, whether the PCs are idle or active. An allocation problem of the sensing period between two PCs is formulated as a convex optimization problem and an optimal solution is provided in this letter. Numerical investigation shows that the proposed optimal allocation improves the throughput of the SU under a miss-detection constraint to protect the PCs.

We consider a cognitive radio network where many secondary users compete with each other for a primary spectrum. The design objective is to determine, in each frame, which secondary user (SU) transmits data when primary user (PU) is idle. We propose a sensing and access scheme which is adopted in a fully distributed manner. First we assume that there is no sensing error and compare the average throughput of the proposed scheme with that of optimum. The performance of the proposed scheme if there is no sensing error has a feature similar to optimal case. And the performance is improved in a large network with much more secondary users. Next we analyze the performance of the proposed scheme if there is sensing error. Under imperfect sensing, the average throughput of the proposed scheme shows a different feature. The numerical examples show that there exists the optimal number of SUs and sensing duration to maximize the average throughput for a given target detection probability.

We study a problem of pricing-based power allocation in collaborative primary-secondary transmission using superposition coding (SC) where secondary power is assigned to primary and secondary signals with fraction α and (1 -α), respectively. The power allocation problem for the secondary user considering price of power level is formulated as a convex optimization problem. And an optimal power allocation for a known primary's pricing policy is provided. For the primary user, a convex optimization problem is formulated to find an optimal pricing policy. With numerical examples, optimal power allocation solutions and pricing policy for different network topologies are provided.