677 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
Beamforming Techniques for Non-Orthogonal Multiple Access in 5G Cellular Networks
In this paper, we develop various beamforming techniques for downlink
transmission for multiple-input single-output (MISO) non-orthogonal multiple
access (NOMA) systems. First, a beamforming approach with perfect channel state
information (CSI) is investigated to provide the required quality of service
(QoS) for all users. Taylor series approximation and semidefinite relaxation
(SDR) techniques are employed to reformulate the original non-convex power
minimization problem to a tractable one. Further, a fairness-based beamforming
approach is proposed through a max-min formulation to maintain fairness between
users. Next, we consider a robust scheme by incorporating channel
uncertainties, where the transmit power is minimized while satisfying the
outage probability requirement at each user. Through exploiting the SDR
approach, the original non-convex problem is reformulated in a linear matrix
inequality (LMI) form to obtain the optimal solution. Numerical results
demonstrate that the robust scheme can achieve better performance compared to
the non-robust scheme in terms of the rate satisfaction ratio. Further,
simulation results confirm that NOMA consumes a little over half transmit power
needed by OMA for the same data rate requirements. Hence, NOMA has the
potential to significantly improve the system performance in terms of transmit
power consumption in future 5G networks and beyond.Comment: accepted to publish in IEEE Transactions on Vehicular Technolog
Resource Allocation for Downlink Multi-Cell OFDMA Cognitive Radio Network Using Hungarian Method
This paper considers the problem of resource allocation for downlink part of an OFDM-based multi-cell cognitive radio network which consists of multiple secondary transmitters and receivers communicating simultaneously in the presence of multiple primary users. We present a new framework to maximize the total data throughput of secondary users by means of subchannel assignment, while ensuring interference leakage to PUs is below a threshold. In this framework, we first formulate the resource allocation problem as a nonlinear and non-convex optimization problem. Then we represent the problem as a maximum weighted matching in a bipartite graph and propose an iterative algorithm based on Hungarian method to solve it. The present contribution develops an efficient subchannel allocation algorithm that assigns subchannels to the secondary users without the perfect knowledge of fading channel gain between cognitive radio transmitter and primary receivers. The performance of the proposed subcarrier allocation algorithm is compared with a blind subchannel allocation as well as another scheme with the perfect knowledge of channel-state information. Simulation results reveal that a significant performance advantage can still be realized, even if the optimization at the secondary network is based on imperfect network information
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