1,625 research outputs found
Decentralized Fair Scheduling in Two-Hop Relay-Assisted Cognitive OFDMA Systems
In this paper, we consider a two-hop relay-assisted cognitive downlink OFDMA
system (named as secondary system) dynamically accessing a spectrum licensed to
a primary network, thereby improving the efficiency of spectrum usage. A
cluster-based relay-assisted architecture is proposed for the secondary system,
where relay stations are employed for minimizing the interference to the users
in the primary network and achieving fairness for cell-edge users. Based on
this architecture, an asymptotically optimal solution is derived for jointly
controlling data rates, transmission power, and subchannel allocation to
optimize the average weighted sum goodput where the proportional fair
scheduling (PFS) is included as a special case. This solution supports
decentralized implementation, requires small communication overhead, and is
robust against imperfect channel state information at the transmitter (CSIT)
and sensing measurement. The proposed solution achieves significant throughput
gains and better user-fairness compared with the existing designs. Finally, we
derived a simple and asymptotically optimal scheduling solution as well as the
associated closed-form performance under the proportional fair scheduling for a
large number of users. The system throughput is shown to be
, where is the
number of users in one cluster, is the number of subchannels and is
the active probability of primary users.Comment: 29 pages, 9 figures, IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL
PROCESSIN
Genetic Algorithm-Holt-Winters Based Minute Spectrum Occupancy Prediction: An Investigation
In this research, the suitability of a genetic algorithm (GA) modified Holt-Winters (HW) exponential model for the prediction of spectrum occupancy data was investigated. Firstly, a description of spectrum measurement that was done during a two-week duration at locations (8.511 °N, 4.594 °E) and (8.487 °N, 4.573 °E) of the 900 MHz and 1800 MHz bands is given. In computing the spectrum duty cycle, different decision thresholds per band link were employed due to differing noise levels. A frequency point with a power spectral density less than the decision threshold was considered unoccupied and was assigned a value of 0, while a frequency point with a power spectral density larger than the decision threshold was considered occupied and was assigned a value of 1. Secondly, the spectrum duty cycle was used in the evaluation of the forecast behavior of the forecasting methods. The HW approach uses exponential smoothing to encode the spectrum data and uses them to forecast typical values in present and future states. The mean square error (MSE) of prediction was minimized using a GA by iteratively adjusting the HW discount factors to improve the forecast accuracy. A decrease in MSE of between 8.33 to 44.6% was observed
Genetic Algorithm-Holt-Winters Based Minute Spectrum Occupancy Prediction: An Investigation
In this research, the suitability of a genetic algorithm (GA) modified Holt-Winters (HW) exponential model for the prediction of spectrum occupancy data was investigated. Firstly, a description of spectrum measurement that was done during a two-week duration at locations (8.511 °N, 4.594 °E) and (8.487 °N, 4.573 °E) of the 900 MHz and 1800 MHz bands is given. In computing the spectrum duty cycle, different decision thresholds per band link were employed due to differing noise levels. A frequency point with a power spectral density less than the decision threshold was considered unoccupied and was assigned a value of 0, while a frequency point with a power spectral density larger than the decision threshold was considered occupied and was assigned a value of 1. Secondly, the spectrum duty cycle was used in the evaluation of the forecast behavior of the forecasting methods. The HW approach uses exponential smoothing to encode the spectrum data and uses them to forecast typical values in present and future states. The mean square error (MSE) of prediction was minimized using a GA by iteratively adjusting the HW discount factors to improve the forecast accuracy. A decrease in MSE of between 8.33 to 44.6% was observed
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Throughput Analysis of Primary and Secondary Networks in a Shared IEEE 802.11 System
In this paper, we analyze the coexistence of a primary and a secondary
(cognitive) network when both networks use the IEEE 802.11 based distributed
coordination function for medium access control. Specifically, we consider the
problem of channel capture by a secondary network that uses spectrum sensing to
determine the availability of the channel, and its impact on the primary
throughput. We integrate the notion of transmission slots in Bianchi's Markov
model with the physical time slots, to derive the transmission probability of
the secondary network as a function of its scan duration. This is used to
obtain analytical expressions for the throughput achievable by the primary and
secondary networks. Our analysis considers both saturated and unsaturated
networks. By performing a numerical search, the secondary network parameters
are selected to maximize its throughput for a given level of protection of the
primary network throughput. The theoretical expressions are validated using
extensive simulations carried out in the Network Simulator 2. Our results
provide critical insights into the performance and robustness of different
schemes for medium access by the secondary network. In particular, we find that
the channel captures by the secondary network does not significantly impact the
primary throughput, and that simply increasing the secondary contention window
size is only marginally inferior to silent-period based methods in terms of its
throughput performance.Comment: To appear in IEEE Transactions on Wireless Communication
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