Average energy efficiency contours with multiple decoding policies

This letter addresses energy-efficient design in multi-user, single-carrier uplink channels by employing multiple decoding policies. The comparison metric used in this study is based on average energy efficiency contours, where an optimal rate vector is obtained based on four system targets: Maximum energy efficiency, a trade-off between maximum energy efficiency and rate fairness, achieving energy efficiency target with maximum sum-rate and achieving energy efficiency target with fairness. The transmit power function is approximated using Taylor series expansion, with simulation results demonstrating the achievability of the optimal rate vector, and negligible performance difference in employing this approximation

Throughput analysis for cognitive radio networks with multiple primary users and imperfect spectrum sensing

In cognitive radio networks, the licensed frequency bands of the primary users (PUs) are available to the secondary user (SU) provided that they do not cause significant interference to the PUs. In this study, the authors analysed the normalised throughput of the SU with multiple PUs coexisting under any frequency division multiple access communication protocol. The authors consider a cognitive radio transmission where the frame structure consists of sensing and data transmission slots. In order to achieve the maximum normalised throughput of the SU and control the interference level to the legal PUs, the optimal frame length of the SU is found via simulation. In this context, a new analytical formula has been expressed for the achievable normalised throughput of SU with multiple PUs under prefect and imperfect spectrum sensing scenarios. Moreover, the impact of imperfect sensing, variable frame length of SU and the variable PU traffic loads, on the normalised throughput has been critically investigated. It has been shown that the analytical and simulation results are in perfect agreement. The authors analytical results are much useful to determine how to select the frame duration length subject to the parameters of cognitive radio network, such as network traffic load, achievable sensing accuracy and number of coexisting PUs

Interworking and integration of the Inmarsat Standard-M with Pan-European GSM system

The market demand on mobile telephone communications has been increasing since the introduction of the cellular mobile telephone systems about twelve years ago. In Europe, projections indicate a demand of about 17 million subscribers for a fully deployed Pan European system. The Pan-European GSM system is to harmonize the growth of the terrestrial mobile system. Studies conducted by the European Space Agency (ESA) indicate that even with 60-70 percent of the area being covered by the GSM, a significant traffic demand (voice and data) will still exist for areas not covered by the terrestrial systems. This demand could be satisfied by a land mobile satellite system. The satellite system is therefore seen in a complementary role rather than in competition with the terrestrial system in an integrated telecommunications network. One possible scenario may be that initially the satellite system provides services to the rural areas together with areas still not covered by the GSM system. This service area is then gradually diminished as the terrestrial system expands until an optimum point is reached where the systems co-exist optimally. The objective of this paper is to investigate the possibility of integration of a space based system, in this case Inmarsat Standard-M, with the GSM (Global System for Mobile communications). One very important advantage of incorporating GSM services in Standard-M is that it will be feasible to extend the GSM services economically worldwide, not only to land mobiles but also to aeronautical and maritime mobiles

A Distributed SON-Based User-Centric Backhaul Provisioning Scheme

5G definition and standardization projects are well underway, and governing characteristics and major challenges have been identified. A critical network element impacting the potential performance of 5G networks is the backhaul, which is expected to expand in length and breadth to cater to the exponential growth of small cells while offering high throughput in the order of gigabit per second and less than 1 ms latency with high resilience and energy efficiency. Such performance may only be possible with direct optical fiber connections that are often not available country-wide and are cumbersome and expensive to deploy. On the other hand, a prime 5G characteristic is diversity, which describes the radio access network, the backhaul, and also the types of user applications and devices. Thus, we propose a novel, distributed, self-optimized, end-to-end user-cell-backhaul association scheme that intelligently associates users with candidate cells based on corresponding dynamic radio and backhaul conditions while abiding by users' requirements. Radio cells broadcast multiple bias factors, each reflecting a dynamic performance indicator (DPI) of the end-to-end network performance such as capacity, latency, resilience, energy consumption, and so on. A given user would employ these factors to derive a user-centric cell ranking that motivates it to select the cell with radio and backhaul performance that conforms to the user requirements. Reinforcement learning is used at the radio cells to optimise the bias factors for each DPI in a way that maximise the system throughput while minimising the gap between the users' achievable and required end-to-end quality of experience (QoE). Preliminary results show considerable improvement in users' QoE and cumulative system throughput when compared with the state-of-the-art user-cell association schemes

A Roadmap for Interdisciplinary Research on the Internet of Things

In mid-2011, the Technology Strategy Board started an integrated programme of work focused on the Internet of Things (IoT), which included strategic investment and the establishment of a Special Interest Group aimed at building and engaging a UK community of innovators and researchers in the IoT. As the portfolio of activities with businesses, academics and other stakeholders progressed, it became apparent to us that the community had a keen interest in taking a more concerted and deeper look at the fundamental research issues in the IoT and that a more interdisciplinary approach was needed.Responding to this level of interest, the Technology Strategy Board joined forces with the Arts and Humanities Research Council, the Economic and Social Research Council, the Engineering and Physical Sciences Research Council and the Research Councils UK Digital Economy Programme and agreed to collaborate on an interdisciplinary R&D roadmapping activity, arguably the first of its kind in the UK. The activity, led by Professors Rahim Tafazolli, Hamid Aghvami, Rachel Cooper, William Dutton and Dr Colin Upstill brought together insight from a wide group of leaders and culminated in a two-day ‘meeting of minds’ in Loughborough on 11 and 12 July 2012. This report summarises the outcomes of the activity and makes important wide-ranging recommendations

Per-user service model for opportunistic scheduling scheme over fading channels

In this paper, we propose a ¯nite-state Markov model for per-user service of an oppor- tunistic scheduling scheme over Rayleigh fading channels, where a single base station serves an arbitrary number of users. By approximating the power gain of Rayleigh fading chan- nels as ¯nite-state Markov processes, we develop an algorithm to obtain dynamic stochastic model of the transmission service, received by an individual user for a saturated scenario, where user data queues are highly loaded. The proposed analytical model is a ¯nite-state Markov process. We provide a comprehensive comparison between the predicted results by the proposed analytical model and the simulation results, which demonstrate a high degree of match between the two sets

TAN: A Distributed Algorithm for Dynamic Task Assignment in WSNs

We consider the scenario of wireless sensor networks where a given application has to be deployed and each application task has to be assigned to each node in the best possible way. Approaches where decisions on task execution are taken by a single central node can avoid the exchange of data packets between task execution nodes but cannot adapt to dynamic network conditions, and suffer from computational complexity. To address this issue, in this paper, we propose an adaptive and decentralized task allocation negotiation algorithm (TAN) for cluster network topologies. It is based on noncooperative game theory, where neighboring nodes engage in negotiations to maximize their own utility functions to agree on which of them should execute single application tasks. Performance is evaluated in a city scenario, where the urban streets are equipped with different sensors and the application target is the detection of the fastest way to reach a destination, and in random WSN scenarios. Comparisons are made with three other algorithms: 1) baseline setting with no task assignment to multiple nodes; 2) centralized task assignment lifetime optimization; and 3) a dynamic distributed algorithm, DLMA. The result is that TAN outperforms these algorithms in terms of application completion time and average energy consumption. Published in