26 research outputs found
Joint Scheduling and Resource Allocation for Packets with Deadlines and Priorities
Cellular networks provide communication for different applications. Some
applications have strict and very short latency requirements, while others
require high bandwidth with varying priorities. The challenge of satisfying the
requirements grows in congested traffic where some packets might miss their
deadlines. Unfortunately, we prove that the problem is NP-Hard. To overcome
this, we propose a new scheduling policy for packets with multiple priorities,
latency requirements, and strict deadlines. To alleviate the complexity, our
solution incorporates a novel time domain relaxation solved by linear
programming. Simulation results show that this method outperforms existing
scheduling strategies
Predictive Precoder Design for OTFS-Enabled URLLC: A Deep Learning Approach
This paper investigates the orthogonal time frequency space (OTFS)
transmission for enabling ultra-reliable low-latency communications (URLLC). To
guarantee excellent reliability performance, pragmatic precoder design is an
effective and indispensable solution. However, the design requires accurate
instantaneous channel state information at the transmitter (ICSIT) which is not
always available in practice. Motivated by this, we adopt a deep learning (DL)
approach to exploit implicit features from estimated historical delay-Doppler
domain channels (DDCs) to directly predict the precoder to be adopted in the
next time frame for minimizing the frame error rate (FER), that can further
improve the system reliability without the acquisition of ICSIT. To this end,
we first establish a predictive transmission protocol and formulate a general
problem for the precoder design where a closed-form theoretical FER expression
is derived serving as the objective function to characterize the system
reliability. Then, we propose a DL-based predictive precoder design framework
which exploits an unsupervised learning mechanism to improve the practicability
of the proposed scheme. As a realization of the proposed framework, we design a
DDCs-aware convolutional long short-term memory (CLSTM) network for the
precoder design, where both the convolutional neural network and LSTM modules
are adopted to facilitate the spatial-temporal feature extraction from the
estimated historical DDCs to further enhance the precoder performance.
Simulation results demonstrate that the proposed scheme facilitates a flexible
reliability-latency tradeoff and achieves an excellent FER performance that
approaches the lower bound obtained by a genie-aided benchmark requiring
perfect ICSI at both the transmitter and receiver.Comment: 31 pages, 12 figure
Age of Information in Downlink Systems: Broadcast or Unicast Transmission?
We analytically decide whether the broadcast transmission scheme or the
unicast transmission scheme achieves the optimal age of information (AoI)
performance of a multiuser system where a base station (BS) generates and
transmits status updates to multiple user equipments (UEs). In the broadcast
transmission scheme, the status update for all UEs is jointly encoded into a
packet for transmission, while in the unicast transmission scheme, the status
update for each UE is encoded individually and transmitted by following the
round robin policy. For both transmission schemes, we examine three packet
management strategies, namely the non-preemption strategy, the preemption in
buffer strategy, and the preemption in serving strategy. We first derive new
closed-form expressions for the average AoI achieved by two transmission
schemes with three packet management strategies. Based on them, we compare the
AoI performance of two transmission schemes in two systems, namely, the remote
control system and the dynamic system. Aided by simulation results, we verify
our analysis and investigate the impact of system parameters on the average
AoI. For example, the unicast transmission scheme is more appropriate for the
system with a large number UEs. Otherwise, the broadcast transmission scheme is
more appropriate
Joint User Scheduling and Beamforming Design for Multiuser MISO Downlink Systems
In multiuser communication systems, user scheduling and beamforming (US-BF)
design are two fundamental problems that are usually studied separately in the
existing literature. In this work, we focus on the joint US-BF design with the
goal of maximizing the set cardinality of scheduled users, which is
computationally challenging due to the non-convex objective function and the
coupled constraints with discrete-continuous variables. To tackle these
difficulties, a successive convex approximation based US-BF (SCA-USBF)
optimization algorithm is firstly proposed. Then, inspired by wireless
intelligent communication, a graph neural network based joint US-BF (J-USBF)
learning algorithm is developed by combining the joint US and power allocation
network model with the BF analytical solution. The effectiveness of SCA-USBF
and J-USBF is verified by various numerical results, the latter achieves close
performance and higher computational efficiency. Furthermore, the proposed
J-USBF also enjoys the generalizability in dynamic wireless network scenarios.Comment: 31 pages, 9 figures, submit to IEEE Transactions on Wireless
Communication
Joint Optimization for Secure and Reliable Communications in Finite Blocklength Regime
To realize ultra-reliable low latency communications with high spectral
efficiency and security, we investigate a joint optimization problem for
downlink communications with multiple users and eavesdroppers in the finite
blocklength (FBL) regime. We formulate a multi-objective optimization problem
to maximize a sum secrecy rate by developing a secure precoder and to minimize
a maximum error probability and information leakage rate. The main challenges
arise from the complicated multi-objective problem, non-tractable back-off
factors from the FBL assumption, non-convexity and non-smoothness of the
secrecy rate, and the intertwined optimization variables. To address these
challenges, we adopt an alternating optimization approach by decomposing the
problem into two phases: secure precoding design, and maximum error probability
and information leakage rate minimization. In the first phase, we obtain a
lower bound of the secrecy rate and derive a first-order Karush-Kuhn-Tucker
(KKT) condition to identify local optimal solutions with respect to the
precoders. Interpreting the condition as a generalized eigenvalue problem, we
solve the problem by using a power iteration-based method. In the second phase,
we adopt a weighted-sum approach and derive KKT conditions in terms of the
error probabilities and leakage rates for given precoders. Simulations validate
the proposed algorithm.Comment: 30 pages, 8 figure
Differential Modulation for Short Packet Transmission in URLLC
One key feature of ultra-reliable low-latency communications (URLLC) in 5G is
to support short packet transmission (SPT). However, the pilot overhead in SPT
for channel estimation is relatively high, especially in high Doppler
environments. In this paper, we advocate the adoption of differential
modulation to support ultra-low latency services, which can ease the channel
estimation burden and reduce the power and bandwidth overhead incurred in
traditional coherent modulation schemes. Specifically, we consider a
multi-connectivity (MC) scheme employing differential modulation to enable
URLLC services. The popular selection combining and maximal ratio combining
schemes are respectively applied to explore the diversity gain in the MC
scheme. A first-order autoregressive model is further utilized to characterize
the time-varying nature of the channel. Theoretically, the maximum achievable
rate and minimum achievable block error rate under ergodic fading channels with
PSK inputs and perfect CSI are first derived by using the non-asymptotic
information-theoretic bounds. The performance of SPT with differential
modulation and MC schemes is then analysed by characterizing the effect of
differential modulation and time-varying channels as a reduction in the
effective SNR. Simulation results show that differential modulation does offer
a significant advantage over the pilot-assisted coherent scheme for SPT,
especially in high Doppler environments.Comment: 15 pages, 9 figure
xURLLC-Aware Service Provisioning in Vehicular Networks: A Semantic Communication Perspective
Semantic communication (SemCom), as an emerging paradigm focusing on meaning
delivery, has recently been considered a promising solution for the inevitable
crisis of scarce communication resources. This trend stimulates us to explore
the potential of applying SemCom to wireless vehicular networks, which normally
consume a tremendous amount of resources to meet stringent reliability and
latency requirements. Unfortunately, the unique background knowledge matching
mechanism in SemCom makes it challenging to simultaneously realize efficient
service provisioning for multiple users in vehicle-to-vehicle networks. To this
end, this paper identifies and jointly addresses two fundamental problems of
knowledge base construction (KBC) and vehicle service pairing (VSP) inherently
existing in SemCom-enabled vehicular networks in alignment with the
next-generation ultra-reliable and low-latency communication (xURLLC)
requirements. Concretely, we first derive the knowledge matching based queuing
latency specific for semantic data packets, and then formulate a
latency-minimization problem subject to several KBC and VSP related reliability
constraints. Afterward, a SemCom-empowered Service Supplying Solution
(S) is proposed along with the theoretical analysis of its
optimality guarantee and computational complexity. Numerical results
demonstrate the superiority of S in terms of average queuing
latency, semantic data packet throughput, user knowledge matching degree and
knowledge preference satisfaction compared with two benchmarks.Comment: This paper has been submitted to IEEE Transactions on Wireless
Communications for the second round of peer review after a major revisio