2,022 research outputs found
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
Maximizing sum rate and minimizing MSE on multiuser downlink: Optimality, fast algorithms and equivalence via max-min SIR
Maximizing the minimum weighted SIR, minimizing the weighted sum MSE and maximizing the weighted sum rate in a multiuser downlink system are three important performance objectives in joint transceiver and power optimization, where all the users have a total power constraint. We show that, through connections with the nonlinear Perron-Frobenius theory, jointly optimizing power and beamformers in the max-min weighted SIR problem can be solved optimally in a distributed fashion. Then, connecting these three performance objectives through the arithmetic-geometric mean inequality and nonnegative matrix theory, we solve the weighted sum MSE minimization and weighted sum rate maximization in the low to moderate interference regimes using fast algorithms
Receive Combining vs. Multi-Stream Multiplexing in Downlink Systems with Multi-Antenna Users
In downlink multi-antenna systems with many users, the multiplexing gain is
strictly limited by the number of transmit antennas and the use of these
antennas. Assuming that the total number of receive antennas at the
multi-antenna users is much larger than , the maximal multiplexing gain can
be achieved with many different transmission/reception strategies. For example,
the excess number of receive antennas can be utilized to schedule users with
effective channels that are near-orthogonal, for multi-stream multiplexing to
users with well-conditioned channels, and/or to enable interference-aware
receive combining. In this paper, we try to answer the question if the data
streams should be divided among few users (many streams per user) or many users
(few streams per user, enabling receive combining). Analytic results are
derived to show how user selection, spatial correlation, heterogeneous user
conditions, and imperfect channel acquisition (quantization or estimation
errors) affect the performance when sending the maximal number of streams or
one stream per scheduled user---the two extremes in data stream allocation.
While contradicting observations on this topic have been reported in prior
works, we show that selecting many users and allocating one stream per user
(i.e., exploiting receive combining) is the best candidate under realistic
conditions. This is explained by the provably stronger resilience towards
spatial correlation and the larger benefit from multi-user diversity. This
fundamental result has positive implications for the design of downlink systems
as it reduces the hardware requirements at the user devices and simplifies the
throughput optimization.Comment: Published in IEEE Transactions on Signal Processing, 16 pages, 11
figures. The results can be reproduced using the following Matlab code:
https://github.com/emilbjornson/one-or-multiple-stream
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