128 research outputs found
Efficient Heuristic for Resource Allocation in Zero-forcing OFDMA-SDMA Systems with Minimum Rate Constraints
4G wireless access systems require high spectral efficiency to support the
ever increasing number of users and data rates for real time applications.
Multi-antenna OFDM-SDMA systems can provide the required high spectral
efficiency and dynamic usage of the channel, but the resource allocation
process becomes extremely complex because of the augmented degrees of freedom.
In this paper, we propose two heuristics to solve the resource allocation
problem that have very low computational complexity and give performances not
far from the optimal. The proposed heuristics select a set of users for each
subchannel, but contrary to the reported methods that solve the throughput
maximization problem, our heuristics consider the set of real-time (RT) users
to ensure that their minimum rate requirements are met. We compare the
heuristics' performance against an upper bound and other methods proposed in
the literature and find that they give a somewhat lower performance, but
support a wider range of minimum rates while reducing the computational
complexity. The gap between the objective achieved by the heuristics and the
upper bound is not large. In our experiments this gap is 10.7% averaging over
all performed numerical evaluations for all system configurations. The increase
in the range of the supported minimum rates when compared with a method
reported in the literature is 14.6% on average.Comment: 8 figure
Learning Energy-Efficient Hardware Configurations for Massive MIMO Beamforming
Hybrid beamforming (HBF) and antenna selection are promising techniques for
improving the energy efficiency~(EE) of massive multiple-input
multiple-output~(mMIMO) systems. However, the transmitter architecture may
contain several parameters that need to be optimized, such as the power
allocated to the antennas and the connections between the antennas and the
radio frequency chains. Therefore, finding the optimal transmitter architecture
requires solving a non-convex mixed integer problem in a large search space. In
this paper, we consider the problem of maximizing the EE of fully digital
precoder~(FDP) and hybrid beamforming~(HBF) transmitters. First, we propose an
energy model for different beamforming structures. Then, based on the proposed
energy model, we develop an unsupervised deep learning method to maximize the
EE by designing the transmitter configuration for FDP and HBF. The proposed
deep neural networks can provide different trade-offs between spectral
efficiency and energy consumption while adapting to different numbers of active
users. Finally, to ensure that the proposed method can be implemented in
practice, we investigate the ability of the model to be trained exclusively
using imperfect channel state information~(CSI), both for the input to the deep
learning model and for the calculation of the loss function. Simulation results
show that the proposed solutions can outperform conventional methods in terms
of EE while being trained with imperfect CSI. Furthermore, we show that the
proposed solutions are less complex and more robust to noise than conventional
methods.Comment: This preprint comprises 15 pages and features 15 figures. Copyright
may be transferred without notic
RSSI-Based Hybrid Beamforming Design with Deep Learning
Hybrid beamforming is a promising technology for 5G millimetre-wave
communications. However, its implementation is challenging in practical
multiple-input multiple-output (MIMO) systems because non-convex optimization
problems have to be solved, introducing additional latency and energy
consumption. In addition, the channel-state information (CSI) must be either
estimated from pilot signals or fed back through dedicated channels,
introducing a large signaling overhead. In this paper, a hybrid precoder is
designed based only on received signal strength indicator (RSSI) feedback from
each user. A deep learning method is proposed to perform the associated
optimization with reasonable complexity. Results demonstrate that the obtained
sum-rates are very close to the ones obtained with full-CSI optimal but complex
solutions. Finally, the proposed solution allows to greatly increase the
spectral efficiency of the system when compared to existing techniques, as
minimal CSI feedback is required.Comment: Published in IEEE-ICC202
Priority queueing models for cognitive radio networks with traffic differentiation
In this paper, we present a new queueing model providing the accurate average system time for packets transmitted over a cognitive radio (CR) link for multiple traffic classes with the preemptive and non-preemptive priority service disciplines. The analysis considers general packet service time, general distributions for the channel availability periods and service interruption periods, and a service-resume transmission. We further introduce and analyze two novel priority service disciplines for opportunistic spectrum access (OSA) networks which take advantage of interruptions to preempt low priority traffic at a low cost. Analytical results, in addition to simulation results to validate their accuracy, are also provided and used to illustrate the impact of different OSA network parameters on the average system time. We particularly show that, for the same average CR transmission link availability, the packet system time significantly increases in a semi-static network with long operating and interruption periods compared to an OSA network with fast alternating operating and interruption periods. We also present results indicating that, due to the presence of interruptions, priority queueing service disciplines provide a greater differentiated service in OSA networks than in traditional networks. The analytical tools presented in this paper are general and can be used to analyze the traffic metrics of most OSA networks carrying multiple classes of traffic with priority queueing service differentiation
Dual-based bounds for resource allocation in zero-forcing beamforming OFDMA-SDMA systems
We consider multi-antenna base stations using orthogonal frequency-division multiple access and space division multiple access techniques to serve single-antenna users. Some users, called real-time users, have minimum rate requirements and must be served in the current time slot while others, called non real-time users, do not have strict timing constraints and are served on a best-effort basis. The resource allocation (RA) problem is to find the assignment of users to subcarriers and the transmit beamforming vectors that maximize the total user rates subject to power and minimum rate constraints. In general, this is a nonlinear and non-convex program and the zero-forcing technique used here makes it integer as well, exact optimal solutions cannot be computed in reasonable time for realistic cases. For this reason, we present a technique to compute both upper and lower bounds and show that these are quite close for some realistic cases. First, we formulate the dual problem whose optimum provides an upper bound to all feasible solutions. We then use a simple method to get a primal-feasible point starting from the dual optimal solution, which is a lower bound on the primal optimal solution. Numerical results for several cases show that the two bounds are close so that the dual method can be used to benchmark any heuristic used to solve this problem. As an example, we provide numerical results showing the performance gap of the well-known weight adjustment method and show that there is considerable room for improvement
Evolution of High Throughput Satellite Systems: Vision, Requirements, and Key Technologies
High throughput satellites (HTS), with their digital payload technology, are
expected to play a key role as enablers of the upcoming 6G networks. HTS are
mainly designed to provide higher data rates and capacities. Fueled by
technological advancements including beamforming, advanced modulation
techniques, reconfigurable phased array technologies, and electronically
steerable antennas, HTS have emerged as a fundamental component for future
network generation. This paper offers a comprehensive state-of-the-art of HTS
systems, with a focus on standardization, patents, channel multiple access
techniques, routing, load balancing, and the role of software-defined
networking (SDN). In addition, we provide a vision for next-satellite systems
that we named as extremely-HTS (EHTS) toward autonomous satellites supported by
the main requirements and key technologies expected for these systems. The EHTS
system will be designed such that it maximizes spectrum reuse and data rates,
and flexibly steers the capacity to satisfy user demand. We introduce a novel
architecture for future regenerative payloads while summarizing the challenges
imposed by this architecture
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