Beamforming Techniques for Nonorthogonal Multiple Access in 5G Cellular Networks

Abstract

Abstract-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.Index Terms-Non-orthogonal multiple access (NOMA), Maxmin fairness, Robust beamforming, Outage Probability.

On Optimal Beamforming Design for Downlink MISO NOMA Systems

“…where R min k represents the QoS threshold of UE k . The similar problems have been studied in [11], [28]- [31], whereas the optimal beamforming was only found for limited number of users, i.e., two users, but it is unknown for the general multiple users case.…”

“…There are some relative works also focusing on beamforming design for minimizing total transmission power with QoS constraints in the MISO NOMA systems. In [28], the authors exploited the SCA method to solve the formulated optimization problem and provided the suboptimal solution. In [29], the authors only considered the special case with two users and proposed an iterative algorithm to find the beamforming solution, which is suboptimal and complex.…”

Sum Rate Fairness Trade-off-based Resource Allocation Technique for MISO NOMA Systems

“…By introducing a new set of slack variables, the constraint in (18) can be rewritten into the following set of constraints:…”

Reckoning Network Lifetime Ratio for Wireless Sensor Network

“…It is a mechanism of using the sensor nodes in the right fashion to deliver the data to the right resource and send the jamming signal towards the wrong resource. The generation of maximum radiation towards the actual user and side lobe towards the interference user is called Beam forming [32]- [38]. There are many techniques which are used to achieve beam forming like autocorrelation and cross-correlation computation using Sample Matrix Inverse, reducing the mean square error by making use of Least Mean Square The antenna arrays which are distributed in nature will send selective beams towards the receiver which will increase the transmission range the amount of transmission power can be decreased by nodes due to energy dissipation being shared among the transmitting devices.…”