4,206 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
Performance Gains of Optimal Antenna Deployment for Massive MIMO Systems
We consider the uplink of a single-cell multi-user multiple-input
multiple-output (MIMO) system with several single antenna transmitters/users
and one base station with antennas in the regime. The
base station antennas are evenly distributed to admissable locations
throughout the cell.
First, we show that a reliable (per-user) rate of is achievable
through optimal locational optimization of base station antennas. We also prove
that an rate is the best possible. Therefore, in contrast to a
centralized or circular deployment, where the achievable rate is at most a
constant, the rate with a general deployment can grow logarithmically with ,
resulting in a certain form of "macromultiplexing gain."
Second, using tools from high-resolution quantization theory, we derive an
accurate formula for the best achievable rate given any and any user
density function. According to our formula, the dependence of the optimal rate
on the user density function is curiously only through the differential
entropy of . In fact, the optimal rate decreases linearly with the
differential entropy, and the worst-case scenario is a uniform user density.
Numerical simulations confirm our analytical findings.Comment: GLOBECOM 201
Transmission Schemes based on Sum Rate Analysis in Distributed Antenna Systems
In this paper, we study single cell multi-user downlink distributed antenna
systems (DAS) where antenna ports are geographically separated in a cell.
First, we derive an expression of the ergodic sum rate for the DAS in the
presence of pathloss. Then, we propose a transmission selection scheme based on
the derived expressions which does not require channel state information at the
transmitter. Utilizing the knowledge of distance information from a user to
each distributed antenna (DA) port, we consider the optimization of pairings of
DA ports and users to maximize the system performance. Based on the ergodic sum
rate expressions, the proposed scheme chooses the best mode maximizing the
ergodic sum rate among mode candidates. In our proposed scheme, the number of
mode candidates are greatly reduced compared to that of ideal mode selection.
In addition, we analyze the signal to noise ratio cross-over point for
different modes using the sum rate expressions. Through Monte Carlo
simulations, we show the accuracy of our derivations for the ergodic sum rate.
Moreover, simulation results with the pathloss modeling confirm that the
proposed scheme produces the average sum rate identical to the ideal mode
selection with significantly reduced candidates.Comment: 25 pages, 8 figures, submitted to IEEE Transactions on Wireless
Communications, May 201
Low-Complexity Multi-User MIMO Algorithms for mmWave WLANs
Very high throughput and high-efficiency wireless local area networks (WLANs) have become essential for today's significant global Internet traffic and the expected significant global increase of public WiFi hotspots. Total Internet traffic is predicted to expand 3.7-fold from 2017 to 2022. In 2017, 53% of overall Internet traffic used by WiFi networks, and that number is expected to increase to 56.8% by 2022. Furthermore, 80% of overall Internet traffic is expected to be video traffic by 2022, up from 70% in 2017. WiFi networks are also expected to move towards denser deployment scenarios, such as stadiums, large office buildings, and airports, with very high data rate applications, such as ultra-high definition video wireless streaming. Thus, in order to meet the predicted growth of wireless traffic and the number of WiFi networks in the world, an efficient Internet access solution is required for the current IEEE 802.11 standards.
Millimeter wave (mmWave) communication technology is expected to play a crucial role in future wireless networks with large user populations because of the large spectrum band it can provide. To further improve spectrum efficiency over mmWave bands in WLANs with large numbers of users, the IEEE 802.11ay standard was developed from the traditional IEEE 802.11ad standard, aiming to support multi-user MIMO. Propagation challenges associated with mmWave bands necessitate the use of analog beamforming (BF) technologies that employ directional transmissions to determine the optimal sector beam between a transmitter and a receiver. However, the multi-user MIMO is not exploited, since analog BF is limited to a single-user, single-transmission. The computational complexity of achieving traditional multi-user MIMO BF methods, such as full digital BF, in the mmWave systems becomes significant due to the hardware constraints. Our research focuses on how to effectively and efficiently realize multi-user MIMO transmission to improve spectrum efficiency over the IEEE 802.11ay mmWave band system while also resolving the computational complexity challenges for achieving a multi-user MIMO in mmWave systems.
This thesis focuses on MAC protocol algorithms and analysis of the IEEE 802.11ay mmWave WLANs to provide multi-user MIMO support in various scenarios to improve the spectrum efficiency and system throughput. Specifically, from a downlink single-hop scenario perspective, a VG algorithm is proposed to schedule simultaneous downlink transmission links while mitigating the multi-user interference with no additional computational complexity. From a downlink multi-hop scenario perspective, a low-complexity MHVG algorithm is conducted to realize simultaneous transmissions and improve the network performance by taking advantage of the spatial reuse in a dense network. The proposed MHVG algorithm permits simultaneous links scheduling and mitigates both the multi-user interference and co-channel interference based only on analog BF information, without the necessity for feedback overhead, such as channel state information (CSI). From an uplink scenario perspective, a low-complexity user selection algorithm, HBF-VG, incorporates user selection with the HBF algorithm to achieve simultaneous uplink transmissions for IEEE 802.11ay mmWave WLANs. With the HBF-VG algorithm, the users can be selected based on an orthogonality criterion instead of collecting CSI from all potential users. We optimize the digital BF to mitigate the residual interference among selected users. Extensive analytical and simulation evaluations are provided to validate the performance of the proposed algorithms with respect to average throughput per time slot, average network throughput, average sum-rate, energy efficiency, signal-to-interference-plus-noise ratio (SINR), and spatial multiplexing gain
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