911 research outputs found
Power Allocation for Adaptive OFDM Index Modulation in Cooperative Networks
In this paper, we propose a power allocation strategy for the adaptive
orthogonal frequency-division multiplexing (OFDM) index modulation (IM) in
cooperative networks. The allocation strategy is based on the
Karush-Kuhn-Tucker (KKT) conditions, and aims at maximizing the average network
capacity according to the instantaneous channel state information (CSI). As the
transmit power at source and relay is constrained separately, we can thus
formulate an optimization problem by allocating power to active subcarriers.
Compared to the conventional uniform power allocation strategy, the proposed
dynamic strategy can lead to a higher average network capacity, especially in
the low signal-to-noise ratio (SNR) region. The analysis is also verified by
numerical results produced by Monte Carlo simulations. By applying the proposed
power allocation strategy, the efficiency of adaptive OFDM IM can be enhanced
in practice, which paves the way for its implementation in the future,
especially for cell-edge communications
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
Enhancing Physical Layer Security in AF Relay Assisted Multi-Carrier Wireless Transmission
In this paper, we study the physical layer security (PLS) problem in the dual
hop orthogonal frequency division multiplexing (OFDM) based wireless
communication system. First, we consider a single user single relay system and
study a joint power optimization problem at the source and relay subject to
individual power constraint at the two nodes. The aim is to maximize the end to
end secrecy rate with optimal power allocation over different sub-carriers.
Later, we consider a more general multi-user multi-relay scenario. Under high
SNR approximation for end to end secrecy rate, an optimization problem is
formulated to jointly optimize power allocation at the BS, the relay selection,
sub-carrier assignment to users and the power loading at each of the relaying
node. The target is to maximize the overall security of the system subject to
independent power budget limits at each transmitting node and the OFDMA based
exclusive sub-carrier allocation constraints. A joint optimization solution is
obtained through duality theory. Dual decomposition allows to exploit convex
optimization techniques to find the power loading at the source and relay
nodes. Further, an optimization for power loading at relaying nodes along with
relay selection and sub carrier assignment for the fixed power allocation at
the BS is also studied. Lastly, a sub-optimal scheme that explores joint power
allocation at all transmitting nodes for the fixed subcarrier allocation and
relay assignment is investigated. Finally, simulation results are presented to
validate the performance of the proposed schemes.Comment: 10 pages, 7 figures, accepted in Transactions on Emerging
Telecommunications Technologies (ETT), formerly known as European
Transactions on Telecommunications (ETT
Energy-efficiency for MISO-OFDMA based user-relay assisted cellular networks
The concept of improving energy-efficiency (EE) without sacrificing the service quality has become important nowadays. The combination of orthogonal frequency-division multiple-access (OFDMA) multi-antenna transmission technology and relaying is one of the key technologies to deliver the promise of reliable and high-data-rate coverage in the most cost-effective manner. In this paper, EE is studied for the downlink multiple-input single-output (MISO)-OFDMA based user-relay assisted cellular networks. EE maximization is formulated for decode and forward (DF) relaying scheme with the consideration of both transmit and circuit power consumption as well as the data rate requirements for the mobile users. The quality of-service (QoS)-constrained EE maximization, which is defined for multi-carrier, multi-user, multi-relay and multi-antenna networks, is a non-convex and combinatorial problem so it is hard to tackle. To solve this difficult problem, a radio resource management (RRM) algorithm that solves the subcarrier allocation, mode selection and power allocation separately is proposed. The efficiency of the proposed algorithm is demonstrated by numerical results for different system parameter
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