• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.5A
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• pp.593-601
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• 2000

In a multi-bandwidth CDMA system, the performance of multiple order selection combining rake receivers are analyzed according to the spreading bandwidth of the system and the delay spread of the Rayleigh fading channel. The results for various channel environments indicate a tradeoff between total received signal energy and multipath fading immunity. Increasing the occupied bandwidth of the system(wide-bandwidth spreading) gives better performance for small delay spread environments, while gathering more energy(narrow-bandwidth spreading)gives better performance for large delay spread environments. It is shown that the performance difference between low and high order selection combining grows larger as the spreading bandwidth increases. It is also noted that performance degrades by increasing the bandwidth above a certain point and the optimum spreading bandwidth for each channel environment decreases as the delay spread of the channel increases.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.9
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• pp.2302-2322
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• 2012

Wireless Body Area Networks (WBANs) are introduced as an enabling technology in tele-health for patient monitoring. Designing an efficient Medium Access Control (MAC) protocol is the main challenge in WBANs because of their various applications and strict requirements such as low level of energy consumption, low transmission delay, the wide range of data rates and prioritizing emergency data. In this paper, we propose a new MAC protocol to provide different requirements of WBANs targeted for medical applications. The proposed MAC provides an efficient emergency response mechanism by considering the correlation between medical signals. It also reduces the power consumption of nodes by minimizing contention access, reducing the probability of the collision and using an efficient synchronization algorithm. In addition, the proposed MAC protocol increases the data rate of the nodes by allocating the resources according to the condition of the network. Analytical and simulation results show that the proposed MAC protocol outperforms IEEE 802.15.4 MAC protocol in terms of power consumption level as well as the average response delay. Also, the comparison results of the proposed MAC with IEEE 802.15.6 MAC protocol show a tradeoff between average response delay and medical data rate.

• Journal of Communications and Networks
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• v.14 no.2
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• pp.179-187
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• 2012

Power saving is one of the most important features that extends the lifetime of portable devices in mobile wireless networks. The IEEE 802.16e mobile broadband wireless access system adopts a power saving mechanism with a binary truncated exponent algorithm for determining sleep intervals. When using this standard power saving scheme, there is often a delay before data packets are received at the mobile subscriber station (MSS). In order to extend the lifetime of a MSS, the battery energy must be used efficiently. This paper presents a dynamically alternating sleep interval scheduling algorithm as a solution to deal with the power consumption problem. We take into account different traffic classes and schedule a proper sequence of power saving classes. The window size of the sleep interval is calculated dynamically according to the packet arrival rate. We make a tradeoff between the power consumption and packet delay. The method achieves the goal of efficiently reducing the listening window size, which leads to increased power saving. The performance of our proposed scheme is compared to that of the standard power saving scheme. Simulation results demonstrate the superior performance of our power saving scheme and its ability to strike the appropriate performance balance between power saving and packet delay for a MSS in an IEEE 802.16e mobile broadband wireless access system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.2
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• pp.216-233
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• 2013

Conventional optimization designs of wireless networks mainly focus on spectral efficiency (SE) as a performance metric. However, as diverse media services are emerging, a green wireless network, which not only meets the quality of experience (QoE) requirements for users and also improves energy efficiency (EE), is the most appropriate solution. In this paper, we firstly propose the unit QoE per Watt, which is termed QoE efficiency (QEE), as a user-oriented metric to evaluate EE for wireless networks. We then analyze which is the kind of wireless resource given priority to use under different scenarios to obtain an acceptable QEE. Particularly, power, delay and data-rate related to QoE are separately addressed for several typical services, such as file download, video stream and web browsing services. Next, the fundamental tradeoffs are investigated between QEE and SE for wireless networks. Our analytical results are helpful for network design and optimization to strike a good balance between the users perceived QoE and energy consumption.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.8B
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• pp.620-629
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• 2012

This paper proposes packet aggregation scheme for considering energy consumption of mobile devices on guaranteeing quality of voice. To our purpose, we analyze VoIP service on wireless multi-hop channel in terms of delay, packet loss and energy consumption when packet aggregation scheme is applied to VoIP service. Moreover, we induce a cost function with considering of tradeoff relation between quality of voice and energy consumption.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2282-2303
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• 2021

To solve the problems of heavy computing load and system transmission pressure in energy internet (EI), we establish a three-tier cloud-edge integrated EI network based on a cloud-edge collaborative computing to achieve the tradeoff between energy consumption and the system delay. A joint optimization problem for resource allocation and task offloading in the threetier cloud-edge integrated EI network is formulated to minimize the total system cost under the constraints of the task scheduling binary variables of each sensor node, the maximum uplink transmit power of each sensor node, the limited computation capability of the sensor node and the maximum computation resource of each edge server, which is a Mixed Integer Non-linear Programming (MINLP) problem. To solve the problem, we propose a joint task offloading and resource allocation algorithm (JTOARA), which is decomposed into three subproblems including the uplink transmission power allocation sub-problem, the computation resource allocation sub-problem, and the offloading scheme selection subproblem. Then, the power allocation of each sensor node is achieved by bisection search algorithm, which has a fast convergence. While the computation resource allocation is derived by line optimization method and convex optimization theory. Finally, to achieve the optimal task offloading, we propose a cloud-edge collaborative computation offloading schemes based on game theory and prove the existence of Nash Equilibrium. The simulation results demonstrate that our proposed algorithm can improve output performance as comparing with the conventional algorithms, and its performance is close to the that of the enumerative algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.4081-4098
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• 2022

With the aim of tackling the contradiction between computation intensive industrial applications and resource-weak Edge Devices (EDs) in Industrial Internet of Things (IIoT), a novel computation task offloading scheme in SDIN-enabled MEC based IIoT is proposed in this paper. With the aim of reducing the task accomplished latency and energy consumption of EDs, a joint optimization method is proposed for optimizing the local CPU-cycle frequency, offloading decision, and wireless and computation resources allocation jointly. Based on the optimization, the task offloading problem is formulated into a Mixed Integer Nonlinear Programming (MINLP) problem which is a large-scale NP-hard problem. In order to solve this problem in an accessible time complexity, a sub-optimal algorithm GPCOA, which is based on hybrid evolutionary computation, is proposed. Outcomes of emulation revel that the proposed method outperforms other baseline methods, and the optimization result shows that the latency-related weight is efficient for reducing the task execution delay and improving the energy efficiency.

• Journal of Communications and Networks
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• v.18 no.5
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• pp.784-795
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• 2016

In this paper, we investigate the resource allocation problem in time-varying heterogeneous wireless networks (HetNet) with multi-homing user equipments (UE). The stochastic optimization model is employed to maximize the network utility, which is defined as the difference between the HetNet's throughput and the total energy consumption cost. In harmony with the hierarchical architecture of HetNet, the problem of stochastic optimization of resource allocation is decomposed into two subproblems by the Lyapunov optimization theory, associated with the flow control in transport layer and the power allocation in physical (PHY) layer, respectively. For avoiding the signaling overhead, outdated dynamic information, and scalability issues, the distributed resource allocation method is developed for solving the two subproblems based on the primal-dual decomposition theory. After that, the adaptive resource allocation algorithm is developed to accommodate the timevarying wireless network only according to the current network state information, i.e. the queue state information (QSI) at radio access networks (RAN) and the channel state information (CSI) of RANs-UE links. The tradeoff between network utility and delay is derived, where the increase of delay is approximately linear in V and the increase of network utility is at the speed of 1/V with a control parameter V. Extensive simulations are presented to show the effectiveness of our proposed scheme.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.188-210
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• 2022

As a promising technique to support tremendous numbers of Internet of Things devices and a variety of applications efficiently, mobile edge computing (MEC) has attracted extensive studies recently. In this paper, we consider a MEC-assisted peer-to-peer (P2P) mobile communication system where MEC servers are deployed at access points to accelerate the communication process between mobile terminals. To capture the tradeoff between the time delay and the energy consumption of the system, a cost function is introduced to facilitate the optimization of the computation and communication resources. The formulated optimization problem is non-convex and is tackled by an iterative block coordinate descent algorithm that decouples the original optimization problem into two subproblems and alternately optimizes the computation and communication resources. Moreover, the MEC-assisted P2P communication system is compared with the conventional P2P communication system, then a condition is provided in closed-form expression when the MEC-assisted P2P communication system performs better. Simulation results show that the advantage of this system is enhanced when the computing capability of the receiver increases whereas it is reduced when the computing capability of the transmitter increases. In addition, the performance of this system is significantly improved when the signal-to-noise ratio of hop-1 exceeds that of hop-2.