Performance of an Adaptive Aggregation Mechanism in a Noisy WLAN Downlink MU-MIMO Channel

This paper investigates an adaptive frame aggregation technique in the medium access control (MAC) layer for the Wireless Local Area Network (WALN) downlink Multi-User–Multiple-In Multiple-Out (MU-MIMO) channel. In tackling the challenges of heterogeneous traffic demand among spatial streams, we proposed a new adaptive aggregation algorithm which has a superior performance over the baseline First-in–First-Out (FIFO) scheme in terms of system throughput performance and channel utilization. However, this earlier work does not consider the effects of wireless channel error. In addressing the limitations of this work, this study contributes an enhanced version of the earlier model considering the effect of channel error. In this approach, a dynamic adaptive aggregation selection scheme is proposed by employing novel criteria for selecting the optimal aggregation policy in WLAN downlink MU-MIMO channel. Two simulation setups are conducted to achieve this approach. The simulation setup in Step 1 performs the dynamic optimal aggregation policy selection strategy as per the channel condition, traffic pattern, and number of stations in the network. Step 2 then performed the optimal wireless frame construction that would be transmitted in the wireless channel in adopting the optimal aggregation policy obtained from Step 1 that maximizes the system performance. The proposed adaptive algorithm not only achieve the optimal system throughput in minimizing wasted space channel time but also provide a good performance under the effects of different channel conditions, different traffic models such as Pareto, Weibull, and fBM, and number of users using the traffic mix of VoIP and video data. Through system-level simulation, our results again show the superior performance of our proposed aggregation mechanism in terms of system throughput performance and space channel time compared to the baseline FIFO aggregation approach

Wireless industrial monitoring and control networks: the journey so far and the road ahead

While traditional wired communication technologies have played a crucial role in industrial monitoring and control networks over the past few decades, they are increasingly proving to be inadequate to meet the highly dynamic and stringent demands of today’s industrial applications, primarily due to the very rigid nature of wired infrastructures. Wireless technology, however, through its increased pervasiveness, has the potential to revolutionize the industry, not only by mitigating the problems faced by wired solutions, but also by introducing a completely new class of applications. While present day wireless technologies made some preliminary inroads in the monitoring domain, they still have severe limitations especially when real-time, reliable distributed control operations are concerned. This article provides the reader with an overview of existing wireless technologies commonly used in the monitoring and control industry. It highlights the pros and cons of each technology and assesses the degree to which each technology is able to meet the stringent demands of industrial monitoring and control networks. Additionally, it summarizes mechanisms proposed by academia, especially serving critical applications by addressing the real-time and reliability requirements of industrial process automation. The article also describes certain key research problems from the physical layer communication for sensor networks and the wireless networking perspective that have yet to be addressed to allow the successful use of wireless technologies in industrial monitoring and control networks

Separation Framework: An Enabler for Cooperative and D2D Communication for Future 5G Networks

Soaring capacity and coverage demands dictate that future cellular networks need to soon migrate towards ultra-dense networks. However, network densification comes with a host of challenges that include compromised energy efficiency, complex interference management, cumbersome mobility management, burdensome signaling overheads and higher backhaul costs. Interestingly, most of the problems, that beleaguer network densification, stem from legacy networks' one common feature i.e., tight coupling between the control and data planes regardless of their degree of heterogeneity and cell density. Consequently, in wake of 5G, control and data planes separation architecture (SARC) has recently been conceived as a promising paradigm that has potential to address most of aforementioned challenges. In this article, we review various proposals that have been presented in literature so far to enable SARC. More specifically, we analyze how and to what degree various SARC proposals address the four main challenges in network densification namely: energy efficiency, system level capacity maximization, interference management and mobility management. We then focus on two salient features of future cellular networks that have not yet been adapted in legacy networks at wide scale and thus remain a hallmark of 5G, i.e., coordinated multipoint (CoMP), and device-to-device (D2D) communications. After providing necessary background on CoMP and D2D, we analyze how SARC can particularly act as a major enabler for CoMP and D2D in context of 5G. This article thus serves as both a tutorial as well as an up to date survey on SARC, CoMP and D2D. Most importantly, the article provides an extensive outlook of challenges and opportunities that lie at the crossroads of these three mutually entangled emerging technologies.Comment: 28 pages, 11 figures, IEEE Communications Surveys & Tutorials 201

CogCell: Cognitive Interplay between 60GHz Picocells and 2.4/5GHz Hotspots in the 5G Era

Rapid proliferation of wireless communication devices and the emergence of a variety of new applications have triggered investigations into next-generation mobile broadband systems, i.e., 5G. Legacy 2G--4G systems covering large areas were envisioned to serve both indoor and outdoor environments. However, in the 5G-era, 80\% of overall traffic is expected to be generated in indoors. Hence, the current approach of macro-cell mobile network, where there is no differentiation between indoors and outdoors, needs to be reconsidered. We envision 60\,GHz mmWave picocell architecture to support high-speed indoor and hotspot communications. We envisage the 5G indoor network as a combination of-, and interplay between, 2.4/5\,GHz having robust coverage and 60\,GHz links offering high datarate. This requires an intelligent coordination and cooperation. We propose 60\,GHz picocellular network architecture, called CogCell, leveraging the ubiquitous WiFi. We propose to use 60\,GHz for the data plane and 2.4/5GHz for the control plane. The hybrid network architecture considers an opportunistic fall-back to 2.4/5\,GHz in case of poor connectivity in the 60\,GHz domain. Further, to avoid the frequent re-beamforming in 60\,GHz directional links due to mobility, we propose a cognitive module -- a sensor-assisted intelligent beam switching procedure -- which reduces the communication overhead. We believe that the CogCell concept will help future indoor communications and possibly outdoor hotspots, where mobile stations and access points collaborate with each other to improve the user experience.Comment: 14 PAGES in IEEE Communications Magazine, Special issue on Emerging Applications, Services and Engineering for Cognitive Cellular Systems (EASE4CCS), July 201

An Adaptive Packet Aggregation Algorithm (AAM) for Wireless Networks

Packet aggregation algorithms are used to improve the throughput performance by combining a number of packets into a single transmission unit in order to reduce the overhead associated with each transmission within a packet-based communications network. However, the throughput improvement is also accompanied by a delay increase. The biggest drawback of a significant number of the proposed packet aggregation algorithms is that they tend to only optimize a single metric, i.e. either to maximize throughput or to minimize delay. They do not permit an optimal trade-off between maximizing throughput and minimizing delay. Therefore, these algorithms cannot achieve the optimal network performance for mixed traffic loads containing a number of different types of applications which may have very different network performance requirements. In this thesis an adaptive packet aggregation algorithm called the Adaptive Aggregation Mechanism (AAM) is proposed which achieves an aggregation trade-off in terms of realizing the largest average throughput with the smallest average delay compared to a number of other popular aggregation algorithms under saturation conditions in wireless networks. The AAM algorithm is the first packet aggregation algorithm that employs an adaptive selection window mechanism where the selection window size is adaptively adjusted in order to respond to the varying nature of both the packet size and packet rate. This algorithm is essentially a feedback control system incorporating a hybrid selection strategy for selecting the packets. Simulation results demonstrate that the proposed algorithm can (a) achieve a large number of sub-packets per aggregate packet for a given delay and (b) significantly improve the performance in terms of the aggregation trade-off for different traffic loads. Furthermore, the AAM algorithm is a robust algorithm as it can significantly improve the performance in terms of the average throughput in error-prone wireless networks