Danny H. K. Tsang

Bio: Danny H. K. Tsang is an academic researcher from Hong Kong University of Science and Technology. The author has contributed to research in topics: Quality of service & Scheduling (computing). The author has an hindex of 43, co-authored 302 publications receiving 6674 citations. Previous affiliations of Danny H. K. Tsang include Dalhousie University & University at Buffalo.

Cooperative spectrum sensing in cognitive radio networks

Abstract: Providing for cooperative sensing in wireless communications that improves user terminal throughput and minimizes interference is described herein. According to some aspects, a network entity, such as a base station, can assign a set of SUTs to measure a subset of wireless channels in a target frequency according to a cooperative sensing arrangement. In particular aspects, this assignment can be implemented so as to increase or maximize potential traffic throughput of those SUTs within the target frequency, in a given wireless time slot. In this manner, cooperative sensing can be structured so as to provide more efficient traffic communications within the target frequency.

Performance analysis of IEEE 802.11e contention-based channel access

Abstract: The new standard IEEE 802.11e is specified to support quality-of-service in wireless local area networks. A comprehensive study of the performance of enhanced distributed channel access (EDCA), the fundamental medium access control mechanism in IEEE 802.11e, is reported in this paper. We present our development of an analytical model, in which most new features of the EDCA such as virtual collision, different arbitration interframe space (AIFS), and different contention window are taken into account. Based on the model, we analyze the throughput performance of differentiated service traffic and propose a recursive method capable of calculating the mean access delay. Service differentiation functionality and effectiveness of the EDCA are investigated through extensive numerical and simulation results. The model and the analysis provide an in-depth understanding and insights into the protocol and the effects of different parameters on the performance.

The stochastic knapsack problem

Abstract: The problem of packing a knapsack of integer volume F with objects from K different classes to maximize profit is studied. Optimization is carried out over the class of coordinate convex policies. For the case of K=2, it is shown for a wide range of parameters that the optimal control is of the threshold type. In the case of Poisson arrivals and of knapsack and object volumes being integer multiples of each other, it is shown that the optimal policy is always of the double-threshold type. An O(F) algorithm to determine the revenue of threshold policies is also given. For the general case of K classes, the problem of the optimal static control where for each class a portion of the knapsack is dedicated is considered. An efficient finite-stage dynamic programming algorithm for locating the optimal static control is presented. Furthermore, variants of the optimal static control which allow some sharing among classes are also discussed. >

NOMA-Assisted Multi-Access Mobile Edge Computing: A Joint Optimization of Computation Offloading and Time Allocation

Abstract: Multi-access mobile edge computing (MEC), which enables mobile users (MUs) to offload their computation-workloads to the computation-servers located at the edge of cellular networks via multi-access radio access, has been considered as a promising technique to address the explosively growing computation-intensive applications in mobile Internet services. In this paper, by exploiting non-orthogonal multiple access (NOMA) for improving the efficiency of multi-access radio transmission, we study the NOMA-enabled multi-access MEC. We aim at minimizing the overall delay of the MUs for finishing their computation requirements, by jointly optimizing the MUs’ offloaded workloads and the NOMA transmission-time. Despite the non-convexity of the formulated joint optimization problem, we propose efficient algorithms to find the optimal offloading solution. For the single-MU case, we exploit the layered structure of the problem and propose an efficient layered algorithm to find the MU's optimal offloading solution that minimizes its overall delay. For the multi-MU case, we propose a distributed algorithm (in which the MUs individually optimize their respective offloaded workloads) to determine the optimal offloading solution for minimizing the sum of all MUs’ overall delay. Extensive numerical results have been provided to validate the effectiveness of our proposed algorithms and the performance advantage of our NOMA-enabled multi-access MEC in comparison with conventional orthogonal multiple access enabled multi-access MEC.

A survey of spectrum sensing algorithms for cognitive radio applications

Abstract: The spectrum sensing problem has gained new aspects with cognitive radio and opportunistic spectrum access concepts. It is one of the most challenging issues in cognitive radio systems. In this paper, a survey of spectrum sensing methodologies for cognitive radio is presented. Various aspects of spectrum sensing problem are studied from a cognitive radio perspective and multi-dimensional spectrum sensing concept is introduced. Challenges associated with spectrum sensing are given and enabling spectrum sensing methods are reviewed. The paper explains the cooperative sensing concept and its various forms. External sensing algorithms and other alternative sensing methods are discussed. Furthermore, statistical modeling of network traffic and utilization of these models for prediction of primary user behavior is studied. Finally, sensing features of some current wireless standards are given.

A Survey on Mobile Edge Computing: The Communication Perspective

Abstract: Driven by the visions of Internet of Things and 5G communications, recent years have seen a paradigm shift in mobile computing, from the centralized mobile cloud computing toward mobile edge computing (MEC). The main feature of MEC is to push mobile computing, network control and storage to the network edges (e.g., base stations and access points) so as to enable computation-intensive and latency-critical applications at the resource-limited mobile devices. MEC promises dramatic reduction in latency and mobile energy consumption, tackling the key challenges for materializing 5G vision. The promised gains of MEC have motivated extensive efforts in both academia and industry on developing the technology. A main thrust of MEC research is to seamlessly merge the two disciplines of wireless communications and mobile computing, resulting in a wide-range of new designs ranging from techniques for computation offloading to network architectures. This paper provides a comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management. We also discuss a set of issues, challenges, and future research directions for MEC research, including MEC system deployment, cache-enabled MEC, mobility management for MEC, green MEC, as well as privacy-aware MEC. Advancements in these directions will facilitate the transformation of MEC from theory to practice. Finally, we introduce recent standardization efforts on MEC as well as some typical MEC application scenarios.

Sensing-Throughput Tradeoff for Cognitive Radio Networks

Abstract: In a cognitive radio network, the secondary users are allowed to utilize the frequency bands of primary users when these bands are not currently being used. To support this spectrum reuse functionality, the secondary users are required to sense the radio frequency environment, and once the primary users are found to be active, the secondary users are required to vacate the channel within a certain amount of time. Therefore, spectrum sensing is of significant importance in cognitive radio networks. There are two parameters associated with spectrum sensing: probability of detection and probability of false alarm. The higher the probability of detection, the better the primary users are protected. However, from the secondary users' perspective, the lower the probability of false alarm, the more chances the channel can be reused when it is available, thus the higher the achievable throughput for the secondary network. In this paper, we study the problem of designing the sensing duration to maximize the achievable throughput for the secondary network under the constraint that the primary users are sufficiently protected. We formulate the sensing-throughput tradeoff problem mathematically, and use energy detection sensing scheme to prove that the formulated problem indeed has one optimal sensing time which yields the highest throughput for the secondary network. Cooperative sensing using multiple mini-slots or multiple secondary users are also studied using the methodology proposed in this paper. Computer simulations have shown that for a 6 MHz channel, when the frame duration is 100 ms, and the signal-to-noise ratio of primary user at the secondary receiver is -20 dB, the optimal sensing time achieving the highest throughput while maintaining 90% detection probability is 14.2 ms. This optimal sensing time decreases when distributed spectrum sensing is applied.

A Survey on Mobile Edge Computing: The Communication Perspective

Abstract: Driven by the visions of Internet of Things and 5G communications, recent years have seen a paradigm shift in mobile computing, from the centralized Mobile Cloud Computing towards Mobile Edge Computing (MEC). The main feature of MEC is to push mobile computing, network control and storage to the network edges (e.g., base stations and access points) so as to enable computation-intensive and latency-critical applications at the resource-limited mobile devices. MEC promises dramatic reduction in latency and mobile energy consumption, tackling the key challenges for materializing 5G vision. The promised gains of MEC have motivated extensive efforts in both academia and industry on developing the technology. A main thrust of MEC research is to seamlessly merge the two disciplines of wireless communications and mobile computing, resulting in a wide-range of new designs ranging from techniques for computation offloading to network architectures. This paper provides a comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management. We also present a research outlook consisting of a set of promising directions for MEC research, including MEC system deployment, cache-enabled MEC, mobility management for MEC, green MEC, as well as privacy-aware MEC. Advancements in these directions will facilitate the transformation of MEC from theory to practice. Finally, we introduce recent standardization efforts on MEC as well as some typical MEC application scenarios.