6 research outputs found
Securing Downlink Massive MIMO-NOMA Networks with Artificial Noise
In this paper, we focus on securing the confidential information of massive
multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA)
networks by exploiting artificial noise (AN). An uplink training scheme is
first proposed with minimum mean squared error estimation at the base station.
Based on the estimated channel state information, the base station precodes the
confidential information and injects the AN. Following this, the ergodic
secrecy rate is derived for downlink transmission. An asymptotic secrecy
performance analysis is also carried out for a large number of transmit
antennas and high transmit power at the base station, respectively, to
highlight the effects of key parameters on the secrecy performance of the
considered system. Based on the derived ergodic secrecy rate, we propose the
joint power allocation of the uplink training phase and downlink transmission
phase to maximize the sum secrecy rates of the system. Besides, from the
perspective of security, another optimization algorithm is proposed to maximize
the energy efficiency. The results show that the combination of massive MIMO
technique and AN greatly benefits NOMA networks in term of the secrecy
performance. In addition, the effects of the uplink training phase and
clustering process on the secrecy performance are revealed. Besides, the
proposed optimization algorithms are compared with other baseline algorithms
through simulations, and their superiority is validated. Finally, it is shown
that the proposed system outperforms the conventional massive MIMO orthogonal
multiple access in terms of the secrecy performance
Edge Cache-assisted Secure Low-Latency Millimeter Wave Transmission
In this paper, we consider an edge cache-assisted millimeter wave cloud radio
access network (C-RAN). Each remote radio head (RRH) in the C-RAN has a local
cache, which can pre-fetch and store the files requested by the actuators.
Multiple RRHs form a cluster to cooperatively serve the actuators, which
acquire their required files either from the local caches or from the central
processor via multicast fronthaul links. For such a scenario, we formulate a
beamforming design problem to minimize the secure transmission delay under
transmit power constraint of each RRH. Due to the difficulty of directly
solving the formulated problem, we divide it into two independent ones:
{\textit{i)}} minimizing the fronthaul transmission delay by jointly optimizing
the transmit and receive beamforming; {\textit{ii)}} minimizing the maximum
access transmission delay by jointly designing cooperative beamforming among
RRHs. An alternatively iterative algorithm is proposed to solve the first
optimization problem. For the latter, we first design the analog beamforming
based on the channel state information of the actuators. Then, with the aid of
successive convex approximation and -procedure techniques, a semidefinite
program (SDP) is formulated, and an iterative algorithm is proposed through SDP
relaxation. Finally, simulation results are provided to verify the performance
of the proposed schemes.Comment: IEEE_IoT, Accep
Energy-Efficient Hybrid Precoding Design for Integrated Multicast-Unicast Millimeter Wave Communications with SWIPT
In this paper, we investigate the energy-efficient hybrid precoding design
for integrated multicast-unicast millimeter wave (mmWave) system, where the
simultaneous wireless information and power transform is considered at
receivers. We adopt two sparse radio frequency chain antenna structures at the
base station (BS), i.e., fully-connected and subarray structures, and design
the codebook-based analog precoding according to the different structures.
Then, we formulate a joint digital multicast, unicast precoding and power
splitting ratio optimization problem to maximize the energy efficiency of the
system, while the maximum transmit power at the BS and minimum harvested energy
at receivers are considered. Due to its difficulty to directly solve the
formulated problem, we equivalently transform the fractional objective function
into a subtractive form one and propose a two-loop iterative algorithm to solve
it. For the outer loop, the classic Bi-section iterative algorithm is applied.
For the inner loop, we transform the formulated problem into a convex one by
successive convex approximation techniques and propose an iterative algorithm
to solve it. Meanwhile, to reduce the complexity of the inner loop, we develop
a zero forcing (ZF) technique-based low complexity iterative algorithm.
Specifically, the ZF technique is applied to cancel the inter-unicast
interference and the first order Taylor approximation is used for the
convexification of the non-convex constraints in the original problem. Finally,
simulation results are provided to compare the performance of the proposed
algorithms under different schemes.Comment: IEEE_TVT, Accep