11 research outputs found
Group Sparse Precoding for Cloud-RAN with Multiple User Antennas
Cloud radio access network (C-RAN) has become a promising network
architecture to support the massive data traffic in the next generation
cellular networks. In a C-RAN, a massive number of low-cost remote antenna
ports (RAPs) are connected to a single baseband unit (BBU) pool via high-speed
low-latency fronthaul links, which enables efficient resource allocation and
interference management. As the RAPs are geographically distributed, the group
sparse beamforming schemes attracts extensive studies, where a subset of RAPs
is assigned to be active and a high spectral efficiency can be achieved.
However, most studies assumes that each user is equipped with a single antenna.
How to design the group sparse precoder for the multiple antenna users remains
little understood, as it requires the joint optimization of the mutual coupling
transmit and receive beamformers. This paper formulates an optimal joint RAP
selection and precoding design problem in a C-RAN with multiple antennas at
each user. Specifically, we assume a fixed transmit power constraint for each
RAP, and investigate the optimal tradeoff between the sum rate and the number
of active RAPs. Motivated by the compressive sensing theory, this paper
formulates the group sparse precoding problem by inducing the -norm as
a penalty and then uses the reweighted heuristic to find a solution.
By adopting the idea of block diagonalization precoding, the problem can be
formulated as a convex optimization, and an efficient algorithm is proposed
based on its Lagrangian dual. Simulation results verify that our proposed
algorithm can achieve almost the same sum rate as that obtained from exhaustive
search
Downlink massive full dimension-multiple input multiple output downlink beamforming analysis at 3.5 GHz using coordinated ON-OFF switching
The long-term evolution and advancement (LTE-A) of the 5G wireless network depends critically on energy consumption. Many existing solutions focus on limiting power constraints and consequently system coverage. So, improving the antenna array elements of the base station (BS) can solve this issue. In this paper, introduce a coordinated ON-OFF switching method in the massive full dimensional multiple input multiple output (massive-FD-MIMO) system. It enhances the radiation pattern of the antenna array element by adjusting the angular power spectra at the BS. By the way, it allows to select the minimum number of antennas for effective beamforming toward specific user equipment’s (UEs). In this context, part of antenna element should be active mode and remining should be sleep mode at the time of signal beamforming. The multipath spatial profiles are decided the beamforming frequency band with minimize energy consumption. As part of the method, we used a conjugated beamforming with power optimization scheme to determine the individual antenna potential and fading channel condition, power optimization is performed. This method quality of service, reliability, energy consumption and data rate can all be evaluated by experimenting with different-sized antenna arrays such as 16×16, 32×32, 64×64 and 128×128
Cell-Free Massive MIMO versus Small Cells
A Cell-Free Massive MIMO (multiple-input multiple-output) system comprises a
very large number of distributed access points (APs)which simultaneously serve
a much smaller number of users over the same time/frequency resources based on
directly measured channel characteristics. The APs and users have only one
antenna each. The APs acquire channel state information through time-division
duplex operation and the reception of uplink pilot signals transmitted by the
users. The APs perform multiplexing/de-multiplexing through conjugate
beamforming on the downlink and matched filtering on the uplink. Closed-form
expressions for individual user uplink and downlink throughputs lead to max-min
power control algorithms. Max-min power control ensures uniformly good service
throughout the area of coverage. A pilot assignment algorithm helps to mitigate
the effects of pilot contamination, but power control is far more important in
that regard.
Cell-Free Massive MIMO has considerably improved performance with respect to
a conventional small-cell scheme, whereby each user is served by a dedicated
AP, in terms of both 95%-likely per-user throughput and immunity to shadow
fading spatial correlation. Under uncorrelated shadow fading conditions, the
cell-free scheme provides nearly 5-fold improvement in 95%-likely per-user
throughput over the small-cell scheme, and 10-fold improvement when shadow
fading is correlated.Comment: EEE Transactions on Wireless Communications, accepted for publicatio
Energy-Efficient Clustered Cell-Free Networking with Access Point Selection
Ultra-densely deploying access points (APs) to support the increasing data
traffic would significantly escalate the cell-edge problem resulting from
traditional cellular networks. By removing the cell boundaries and coordinating
all APs for joint transmission, the cell-edge problem can be alleviated, which
in turn leads to unaffordable system complexity and channel measurement
overhead. A new scalable clustered cell-free network architecture has been
proposed recently, under which the large-scale network is flexibly partitioned
into a set of independent subnetworks operating parallelly. In this paper, we
study the energy-efficient clustered cell-free networking problem with AP
selection. Specifically, we propose a user-centric ratio-fixed AP-selection
based clustering (UCR-ApSel) algorithm to form subnetworks dynamically.
Following this, we analyze the average energy efficiency achieved with the
proposed UCR-ApSel scheme theoretically and derive an effective closed-form
upper-bound. Based on the analytical upper-bound expression, the optimal
AP-selection ratio that maximizes the average energy efficiency is further
derived as a simple explicit function of the total number of APs and the number
of subnetworks. Simulation results demonstrate the effectiveness of the derived
optimal AP-selection ratio and show that the proposed UCR-ApSel algorithm with
the optimal AP-selection ratio achieves around 40% higher energy efficiency
than the baselines. The analysis provides important insights to the design and
optimization of future ultra-dense wireless communication systems