5,492 research outputs found
The Impact of QoS Constraints on the Energy Efficiency of Fixed-Rate Wireless Transmissions
Transmission over wireless fading channels under quality of service (QoS)
constraints is studied when only the receiver has channel side information.
Being unaware of the channel conditions, transmitter is assumed to send the
information at a fixed rate. Under these assumptions, a two-state (ON-OFF)
transmission model is adopted, where information is transmitted reliably at a
fixed rate in the ON state while no reliable transmission occurs in the OFF
state. QoS limitations are imposed as constraints on buffer violation
probabilities, and effective capacity formulation is used to identify the
maximum throughput that a wireless channel can sustain while satisfying
statistical QoS constraints. Energy efficiency is investigated by obtaining the
bit energy required at zero spectral efficiency and the wideband slope in both
wideband and low-power regimes assuming that the receiver has perfect channel
side information (CSI). In both wideband and low-power regimes, the increased
energy requirements due to the presence of QoS constraints are quantified.
Comparisons with variable-rate/fixed-power and variable-rate/variable-power
cases are given. Energy efficiency is further analyzed in the presence of
channel uncertainties. The optimal fraction of power allocated to training is
identified under QoS constraints. It is proven that the minimum bit energy in
the low-power regime is attained at a certain nonzero power level below which
bit energy increases without bound with vanishing power
Transmit Power Minimization in Small Cell Networks Under Time Average QoS Constraints
We consider a small cell network (SCN) consisting of N cells, with the small
cell base stations (SCBSs) equipped with Nt \geq 1 antennas each, serving K
single antenna user terminals (UTs) per cell. Under this set up, we address the
following question: given certain time average quality of service (QoS) targets
for the UTs, what is the minimum transmit power expenditure with which they can
be met? Our motivation to consider time average QoS constraint comes from the
fact that modern wireless applications such as file sharing, multi-media etc.
allow some flexibility in terms of their delay tolerance. Time average QoS
constraints can lead to greater transmit power savings as compared to
instantaneous QoS constraints since it provides the flexibility to dynamically
allocate resources over the fading channel states. We formulate the problem as
a stochastic optimization problem whose solution is the design of the downlink
beamforming vectors during each time slot. We solve this problem using the
approach of Lyapunov optimization and characterize the performance of the
proposed algorithm. With this algorithm as the reference, we present two main
contributions that incorporate practical design considerations in SCNs. First,
we analyze the impact of delays incurred in information exchange between the
SCBSs. Second, we impose channel state information (CSI) feedback constraints,
and formulate a joint CSI feedback and beamforming strategy. In both cases, we
provide performance bounds of the algorithm in terms of satisfying the QoS
constraints and the time average power expenditure. Our simulation results show
that solving the problem with time average QoS constraints provide greater
savings in the transmit power as compared to the instantaneous QoS constraints.Comment: in Journal on Selected Areas of Communications (JSAC), 201
Performance Analysis of Cognitive Radio Systems under QoS Constraints and Channel Uncertainty
In this paper, performance of cognitive transmission over time-selective flat
fading channels is studied under quality of service (QoS) constraints and
channel uncertainty. Cognitive secondary users (SUs) are assumed to initially
perform channel sensing to detect the activities of the primary users, and then
attempt to estimate the channel fading coefficients through training. Energy
detection is employed for channel sensing, and different minimum
mean-square-error (MMSE) estimation methods are considered for channel
estimation. In both channel sensing and estimation, erroneous decisions can be
made, and hence, channel uncertainty is not completely eliminated. In this
setting, performance is studied and interactions between channel sensing and
estimation are investigated.
Following the channel sensing and estimation tasks, SUs engage in data
transmission. Transmitter, being unaware of the channel fading coefficients, is
assumed to send the data at fixed power and rate levels that depend on the
channel sensing results. Under these assumptions, a state-transition model is
constructed by considering the reliability of the transmissions, channel
sensing decisions and their correctness, and the evolution of primary user
activity which is modeled as a two-state Markov process. In the data
transmission phase, an average power constraint on the secondary users is
considered to limit the interference to the primary users, and statistical
limitations on the buffer lengths are imposed to take into account the QoS
constraints of the secondary traffic. The maximum throughput under these
statistical QoS constraints is identified by finding the effective capacity of
the cognitive radio channel. Numerical results are provided for the power and
rate policies
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