192 research outputs found
Optimal Joint Power and Subcarrier Allocation for MC-NOMA Systems
In this paper, we investigate the resource allocation algorithm design for
multicarrier non-orthogonal multiple access (MC-NOMA) systems. The proposed
algorithm is obtained from the solution of a non-convex optimization problem
for the maximization of the weighted system throughput. We employ monotonic
optimization to develop the optimal joint power and subcarrier allocation
policy. The optimal resource allocation policy serves as a performance
benchmark due to its high complexity. Furthermore, to strike a balance between
computational complexity and optimality, a suboptimal scheme with low
computational complexity is proposed. Our simulation results reveal that the
suboptimal algorithm achieves a close-to-optimal performance and MC-NOMA
employing the proposed resource allocation algorithm provides a substantial
system throughput improvement compared to conventional multicarrier orthogonal
multiple access (MC-OMA).Comment: Submitted to Globecom 201
Power and Channel Allocation for Non-orthogonal Multiple Access in 5G Systems: Tractability and Computation
Network capacity calls for significant increase for 5G cellular systems. A
promising multi-user access scheme, non-orthogonal multiple access (NOMA) with
successive interference cancellation (SIC), is currently under consideration.
In NOMA, spectrum efficiency is improved by allowing more than one user to
simultaneously access the same frequency-time resource and separating
multi-user signals by SIC at the receiver. These render resource allocation and
optimization in NOMA different from orthogonal multiple access in 4G. In this
paper, we provide theoretical insights and algorithmic solutions to jointly
optimize power and channel allocation in NOMA. For utility maximization, we
mathematically formulate NOMA resource allocation problems. We characterize and
analyze the problems' tractability under a range of constraints and utility
functions. For tractable cases, we provide polynomial-time solutions for global
optimality. For intractable cases, we prove the NP-hardness and propose an
algorithmic framework combining Lagrangian duality and dynamic programming
(LDDP) to deliver near-optimal solutions. To gauge the performance of the
obtained solutions, we also provide optimality bounds on the global optimum.
Numerical results demonstrate that the proposed algorithmic solution can
significantly improve the system performance in both throughput and fairness
over orthogonal multiple access as well as over a previous NOMA resource
allocation scheme.Comment: IEEE Transactions on Wireless Communications, revisio
Joint Subcarrier and Power Allocation in NOMA: Optimal and Approximate Algorithms
Non-orthogonal multiple access (NOMA) is a promising technology to increase
the spectral efficiency and enable massive connectivity in 5G and future
wireless networks. In contrast to orthogonal schemes, such as OFDMA, NOMA
multiplexes several users on the same frequency and time resource. Joint
subcarrier and power allocation problems (JSPA) in NOMA are NP-hard to solve in
general. In this family of problems, we consider the weighted sum-rate (WSR)
objective function as it can achieve various tradeoffs between sum-rate
performance and user fairness. Because of JSPA's intractability, a common
approach in the literature is to solve separately the power control and
subcarrier allocation (also known as user selection) problems, therefore
achieving sub-optimal result. In this work, we first improve the computational
complexity of existing single-carrier power control and user selection schemes.
These improved procedures are then used as basic building blocks to design new
algorithms, namely Opt-JSPA, -JSPA and Grad-JSPA. Opt-JSPA
computes an optimal solution with lower complexity than current optimal schemes
in the literature. It can be used as a benchmark for optimal WSR performance in
simulations. However, its pseudo-polynomial time complexity remains impractical
for real-world systems with low latency requirements. To further reduce the
complexity, we propose a fully polynomial-time approximation scheme called
-JSPA. Since, no approximation has been studied in the literature,
-JSPA stands out by allowing to control a tight trade-off between
performance guarantee and complexity. Finally, Grad-JSPA is a heuristic based
on gradient descent. Numerical results show that it achieves near-optimal WSR
with much lower complexity than existing optimal methods
Cooperative Multi-Bitrate Video Caching and Transcoding in Multicarrier NOMA-Assisted Heterogeneous Virtualized MEC Networks
Cooperative video caching and transcoding in mobile edge computing (MEC)
networks is a new paradigm for future wireless networks, e.g., 5G and 5G
beyond, to reduce scarce and expensive backhaul resource usage by prefetching
video files within radio access networks (RANs). Integration of this technique
with other advent technologies, such as wireless network virtualization and
multicarrier non-orthogonal multiple access (MC-NOMA), provides more flexible
video delivery opportunities, which leads to enhancements both for the
network's revenue and for the end-users' service experience. In this regard, we
propose a two-phase RAF for a parallel cooperative joint multi-bitrate video
caching and transcoding in heterogeneous virtualized MEC networks. In the cache
placement phase, we propose novel proactive delivery-aware cache placement
strategies (DACPSs) by jointly allocating physical and radio resources based on
network stochastic information to exploit flexible delivery opportunities.
Then, for the delivery phase, we propose a delivery policy based on the user
requests and network channel conditions. The optimization problems
corresponding to both phases aim to maximize the total revenue of network
slices, i.e., virtual networks. Both problems are non-convex and suffer from
high-computational complexities. For each phase, we show how the problem can be
solved efficiently. We also propose a low-complexity RAF in which the
complexity of the delivery algorithm is significantly reduced. A Delivery-aware
cache refreshment strategy (DACRS) in the delivery phase is also proposed to
tackle the dynamically changes of network stochastic information. Extensive
numerical assessments demonstrate a performance improvement of up to 30% for
our proposed DACPSs and DACRS over traditional approaches.Comment: 53 pages, 24 figure
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