15 research outputs found
Massive MIMO Multicast Beamforming Via Accelerated Random Coordinate Descent
One key feature of massive multiple-input multiple-output systems is the
large number of antennas and users. As a result, reducing the computational
complexity of beamforming design becomes imperative. To this end, the goal of
this paper is to achieve a lower complexity order than that of existing
beamforming methods, via the parallel accelerated random coordinate descent
(ARCD). However, it is known that ARCD is only applicable when the problem is
convex, smooth, and separable. In contrast, the beamforming design problem is
nonconvex, nonsmooth, and nonseparable. Despite these challenges, this paper
shows that it is possible to incorporate ARCD for multicast beamforming by
leveraging majorization minimization and strong duality. Numerical results show
that the proposed method reduces the execution time by one order of magnitude
compared to state-of-the-art methods.Comment: IEEE ICASSP'19, Brighton, UK, May 201
Over-the-Air Computation via Intelligent Reflecting Surfaces
Over-the-air computation (AirComp) becomes a promising approach for fast
wireless data aggregation via exploiting the superposition property in a
multiple access channel. To further overcome the unfavorable signal propagation
conditions for AirComp, in this paper, we propose an intelligent reflecting
surface (IRS) aided AirComp system to build controllable wireless environments,
thereby boosting the received signal power significantly. This is achieved by
smartly tuning the phase shifts for the incoming electromagnetic waves at IRS,
resulting in reconfigurable signal propagations. Unfortunately, it turns out
that the joint design problem for AirComp transceivers and IRS phase shifts
becomes a highly intractable nonconvex bi-quadratic programming problem, for
which a novel alternating difference-of-convex (DC) programming algorithm is
developed. This is achieved by providing a novel DC function representation for
the rank-one constraint in the low-rank matrix optimization problem via matrix
lifting. Simulation results demonstrate the algorithmic advantages and
admirable performance of the proposed approaches compared with the state-of-art
solutions
A Novel Alternative Optimization Method for Joint Power and Trajectory Design in UAV-Enabled Wireless Network
This letter aims to maximize the average throughput via the joint design of
the transmit power and trajectory for unmanned aerial vehicle (UAV)-enabled
network. The conventional way to tackle this problem is based on the
alternating optimization (AO) method by iteratively updating power and
trajectory until convergence, resulting in a non-convex trajectory subproblem
which is difficult to deal with. To develop more efficient methods, we propose
a novel AO method by incorporating both power and trajectory into an
intermediate variable, and then iteratively updating power and the newly
introduced variable. This novel variable transformation makes it easier to
decompose the original problem into two convex subproblems, namely a throughput
maximization subproblem and a feasibility subproblem. Consequently, both of
these subproblems can be solved in a globally optimal fashion. We further
propose a low-complexity algorithm for the feasibility subproblem by exploiting
the alternating directional method of multipliers (ADMM), whose updating step
is performed in closed-form solutions. Simulation results demonstrate that our
proposed method reduces the computation time by orders of magnitude, while
achieving higher performance than the conventional methods
Federated Learning via Over-the-Air Computation
The stringent requirements for low-latency and privacy of the emerging
high-stake applications with intelligent devices such as drones and smart
vehicles make the cloud computing inapplicable in these scenarios. Instead,
edge machine learning becomes increasingly attractive for performing training
and inference directly at network edges without sending data to a centralized
data center. This stimulates a nascent field termed as federated learning for
training a machine learning model on computation, storage, energy and bandwidth
limited mobile devices in a distributed manner. To preserve data privacy and
address the issues of unbalanced and non-IID data points across different
devices, the federated averaging algorithm has been proposed for global model
aggregation by computing the weighted average of locally updated model at each
selected device. However, the limited communication bandwidth becomes the main
bottleneck for aggregating the locally computed updates. We thus propose a
novel over-the-air computation based approach for fast global model aggregation
via exploring the superposition property of a wireless multiple-access channel.
This is achieved by joint device selection and beamforming design, which is
modeled as a sparse and low-rank optimization problem to support efficient
algorithms design. To achieve this goal, we provide a
difference-of-convex-functions (DC) representation for the sparse and low-rank
function to enhance sparsity and accurately detect the fixed-rank constraint in
the procedure of device selection. A DC algorithm is further developed to solve
the resulting DC program with global convergence guarantees. The algorithmic
advantages and admirable performance of the proposed methodologies are
demonstrated through extensive numerical results
Non-Orthogonal Unicast and Broadcast Transmission via Joint Beamforming and LDM in Cellular Networks
Limited bandwidth resources and higher energy efficiency requirements
motivate incorporating multicast and broadcast transmission into the
next-generation cellular network architectures, particularly for multimedia
streaming applications. Layered division multiplexing (LDM), a form of NOMA,
can potentially improve unicast throughput and broadcast coverage with respect
to traditional orthogonal frequency division multiplexing (FDM) or time
division multiplexing (TDM), by simultaneously using the same frequency and
time resources for multiple unicast or broadcast transmissions. In this paper,
the performance of LDM-based unicast and broadcast transmission in a cellular
network is studied by assuming a single frequency network (SFN) operation for
the broadcast layer, while allowing arbitrarily clustered cooperation among the
base stations (BSs) for the transmission of unicast data streams. Beamforming
and power allocation between unicast and broadcast layers, the so-called
injection level in the LDM literature, are optimized with the aim of minimizing
the sum-power under constraints on the user-specific unicast rates and on the
common broadcast rate. The effects of imperfect channel coding and imperfect
CSI are also studied to gain insights into robust implementation in practical
systems. The non-convex optimization problem is tackled by means of successive
convex approximation (SCA) techniques. Performance upper bounds are also
presented by means of the -procedure followed by semidefinite
relaxation (SDR). Finally, a dual decomposition-based solution is proposed to
facilitate an efficient distributed implementation of LDM where the optimal
unicast beamforming vectors can be obtained locally by the cooperating BSs.
Numerical results are presented, which show the tightness of the proposed
bounds and hence the near-optimality of the proposed solutions.Comment: This work has been submitted to IEEE for possible publicatio
Secure Multigroup Multicast Communication Systems via Intelligent Reflecting Surface
This paper considers a secure multigroup multicast multiple-input
single-output (MISO) communication system aided by an intelligent reflecting
surface (IRS). Specifically, we aim to minimize the transmit power at the Alice
via jointly optimizing the transmit beamformer, AN vector and phase shifts at
the IRS subject to the secrecy rate constraints as well as the unit modulus
constraints of IRS phase shifts. However, the optimization problem is
non-convex and directly solving it is intractable. To tackle the optimization
problem, we first transform it into a semidefinite relaxation (SDR) problem,
and then alternately update the transmit beamformer and AN matrix as well as
the phase shifts at the IRS. In order to reduce the high computational
complexity, we further propose a low-complexity algorithm based on second-order
cone programming (SOCP). We decouple the optimization problem into two
sub-problems and optimize the transmit beamformer, AN vector and the phase
shifts alternately by solving two corresponding SOCP sub-problem. Simulation
results show that the proposed SDR and SOCP schemes require half or less
transmit power than the scheme without IRS, which demonstrates the advantages
of introducing IRS and the effectiveness of the proposed methods.Comment: 10 pages,8 figure
Bandwidth Gain from Mobile Edge Computing and Caching in Wireless Multicast Systems
In this paper, we present a novel mobile edge computing (MEC) model where the
MEC server has the input and output data of all computation tasks and
communicates with multiple caching-and-computing-enabled mobile devices via a
shared wireless link. Each task request can be served from local output
caching, local computing with input caching, local computing or MEC computing,
each of which incurs a unique bandwidth requirement of the multicast link.
Aiming to minimize the transmission bandwidth, we design and optimize the local
caching and computing policy at mobile devices subject to latency, caching,
energy and multicast transmission constraints. The joint policy optimization
problem is shown to be NP-hard. When the output data size is smaller than the
input data size, we reformulate the problem as minimization of a monotone
submodular function over matroid constraints and obtain the optimal solution
via a strongly polynomial algorithm of Schrijver. On the other hand, when the
output data size is larger than the input data size, by leveraging sample
approximation and concave convex procedure together with the alternating
direction method of multipliers, we propose a low-complexity high-performance
algorithm and prove it converges to a stationary point. Furthermore, we
theoretically reveal how much bandwidth gain can be achieved from computing and
caching resources at mobile devices or the multicast transmission for symmetric
case. Our results indicate that exploiting the computing and caching resources
at mobile devices as well as multicast transmission can provide significant
bandwidth savings.Comment: submitted to IEEE Trans. Wireless Communications. arXiv admin note:
text overlap with arXiv:1807.0553
Multi-Group Multicast Beamforming: Optimal Structure and Efficient Algorithms
This paper considers the multi-group multicast beamforming optimization
problem, for which the optimal solution has been unknown due to the non-convex
and NP-hard nature of the problem. By utilizing the successive convex
approximation numerical method and Lagrangian duality, we obtain the optimal
multicast beamforming solution structure for both the quality-of-service (QoS)
problem and the max-min fair (MMF) problem. The optimal structure brings
valuable insights into multicast beamforming: We show that the notion of
uplink-downlink duality can be generalized to the multicast beamforming
problem. The optimal multicast beamformer is a weighted MMSE filter based on a
group-channel direction: a generalized version of the optimal downlink
multi-user unicast beamformer. We also show that there is an inherent
low-dimensional structure in the optimal multicast beamforming solution
independent of the number of transmit antennas, leading to efficient numerical
algorithm design, especially for systems with large antenna arrays. We propose
efficient algorithms to compute the multicast beamformer based on the optimal
beamforming structure. Through asymptotic analysis, we characterize the
asymptotic behavior of the multicast beamformers as the number of antennas
grows, and in turn, provide simple closed-form approximate multicast
beamformers for both the QoS and MMF problems. This approximation offers
practical multicast beamforming solutions with a near-optimal performance at
very low computational complexity for large-scale antenna systems.Comment: 16 pages, 6 figures, 5 tables. In IEEE Trans. Signal Processing, 202
Pilot Spoofing Attack by Multiple Eavesdroppers
In this paper, we investigate the design of a pilot spoofing attack (PSA)
carried out by multiple single-antenna eavesdroppers (Eves) in a downlink
time-division duplex (TDD) system, where a multiple antenna base station (BS)
transmits confidential information to a single-antenna legitimate user (LU).
During the uplink channel training phase, multiple Eves collaboratively impair
the channel acquisition of the legitimate link, aiming at maximizing the
wiretapping signal-to-noise ratio (SNR) in the subsequent downlink data
transmission phase. Two different scenarios are investigated: (1) the BS is
unaware of the PSA, and (2) the BS attempts to detect the presence of the PSA.
For both scenarios, we formulate wiretapping SNR maximization problems. For the
second scenario, we also investigate the probability of successful detection
and constrain it to remain below a pre-designed threshold. The two resulting
optimization problems can be unified into a more general non-convex
optimization problem, and we propose an efficient algorithm based on the
minorization-maximization (MM) method and the alternating direction method of
multipliers (ADMM) to solve it. The proposed MM-ADMM algorithm is shown to
converge to a stationary point of the general problem. In addition, we propose
a semidefinite relaxation (SDR) method as a benchmark to evaluate the
efficiency of the MM-ADMM algorithm. Numerical results show that the MM-ADMM
algorithm achieves near-optimal performance and is computationally more
efficient than the SDRbased method.Comment: Accepted by IEEE Transaction on Wireless Communication
Fast Algorithms for Joint Multicast Beamforming and Antenna Selection in Massive MIMO
Massive MIMO is currently a leading physical layer technology candidate that
can dramatically enhance throughput in 5G systems, for both unicast and
multicast transmission modalities. As antenna elements are becoming smaller and
cheaper in the mmW range compared to radio frequency (RF) chains, it is crucial
to perform antenna selection at the transmitter, such that the available RF
chains are switched to an appropriate subset of antennas. This paper considers
the joint problem of multicast beamforming and antenna selection for a single
multicast group in massive MIMO systems. The prior state-of-art for this
problem relies on semi-definite relaxation (SDR), which cannot scale up to the
massive MIMO regime. A successive convex approximation (SCA) based approach is
proposed to tackle max-min fair joint multicast beamforming and antenna
selection. The key idea of SCA is to successively approximate the non-convex
problem by a class of non-smooth, convex optimization problems. Two fast and
memory efficient first-order methods are proposed to solve each SCA subproblem.
Simulations demonstrate that the proposed algorithms outperform the existing
state-of-art approach in terms of solution quality and run time, in both
traditional and especially in massive MIMO settings