452 research outputs found
Constructive Multiuser Interference in Symbol Level Precoding for the MISO Downlink Channel
This paper investigates the problem of interference among the simultaneous
multiuser transmissions in the downlink of multiple antennas systems. Using
symbol level precoding, a new approach towards the multiuser interference is
discussed along this paper. The concept of exploiting the interference between
the spatial multiuser transmissions by jointly utilizing the data information
(DI) and channel state information (CSI), in order to design symbol-level
precoders, is proposed. In this direction, the interference among the data
streams is transformed under certain conditions to useful signal that can
improve the signal to interference noise ratio (SINR) of the downlink
transmissions. We propose a maximum ratio transmission (MRT) based algorithm
that jointly exploits DI and CSI to glean the benefits from constructive
multiuser interference. Subsequently, a relation between the constructive
interference downlink transmission and physical layer multicasting is
established. In this context, novel constructive interference precoding
techniques that tackle the transmit power minimization (min power) with
individual SINR constraints at each user's receivers is proposed. Furthermore,
fairness through maximizing the weighted minimum SINR (max min SINR) of the
users is addressed by finding the link between the min power and max min SINR
problems. Moreover, heuristic precoding techniques are proposed to tackle the
weighted sum rate problem. Finally, extensive numerical results show that the
proposed schemes outperform other state of the art techniques.Comment: Submitted to IEEE Transactions on Signal Processin
Achieving Global Optimality for Weighted Sum-Rate Maximization in the K-User Gaussian Interference Channel with Multiple Antennas
Characterizing the global maximum of weighted sum-rate (WSR) for the K-user
Gaussian interference channel (GIC), with the interference treated as Gaussian
noise, is a key problem in wireless communication. However, due to the users'
mutual interference, this problem is in general non-convex and thus cannot be
solved directly by conventional convex optimization techniques. In this paper,
by jointly utilizing the monotonic optimization and rate profile techniques, we
develop a new framework to obtain the globally optimal power control and/or
beamforming solutions to the WSR maximization problems for the GICs with
single-antenna transmitters and single-antenna receivers (SISO), single-antenna
transmitters and multi-antenna receivers (SIMO), or multi-antenna transmitters
and single-antenna receivers (MISO). Different from prior work, this paper
proposes to maximize the WSR in the achievable rate region of the GIC directly
by exploiting the facts that the achievable rate region is a "normal" set and
the users' WSR is a "strictly increasing" function over the rate region.
Consequently, the WSR maximization is shown to be in the form of monotonic
optimization over a normal set and thus can be solved globally optimally by the
existing outer polyblock approximation algorithm. However, an essential step in
the algorithm hinges on how to efficiently characterize the intersection point
on the Pareto boundary of the achievable rate region with any prescribed "rate
profile" vector. This paper shows that such a problem can be transformed into a
sequence of signal-to-interference-plus-noise ratio (SINR) feasibility
problems, which can be solved efficiently by existing techniques. Numerical
results validate that the proposed algorithms can achieve the global WSR
maximum for the SISO, SIMO or MISO GIC.Comment: This is the longer version of a paper to appear in IEEE Transactions
on Wireless Communication
Robust Transmission in Downlink Multiuser MISO Systems: A Rate-Splitting Approach
We consider a downlink multiuser MISO system with bounded errors in the
Channel State Information at the Transmitter (CSIT). We first look at the
robust design problem of achieving max-min fairness amongst users (in the
worst-case sense). Contrary to the conventional approach adopted in literature,
we propose a rather unorthodox design based on a Rate-Splitting (RS) strategy.
Each user's message is split into two parts, a common part and a private part.
All common parts are packed into one super common message encoded using a
public codebook, while private parts are independently encoded. The resulting
symbol streams are linearly precoded and simultaneously transmitted, and each
receiver retrieves its intended message by decoding both the common stream and
its corresponding private stream. For CSIT uncertainty regions that scale with
SNR (e.g. by scaling the number of feedback bits), we prove that a RS-based
design achieves higher max-min (symmetric) Degrees of Freedom (DoF) compared to
conventional designs (NoRS). For the special case of non-scaling CSIT (e.g.
fixed number of feedback bits), and contrary to NoRS, RS can achieve a
non-saturating max-min rate. We propose a robust algorithm based on the
cutting-set method coupled with the Weighted Minimum Mean Square Error (WMMSE)
approach, and we demonstrate its performance gains over state-of-the art
designs. Finally, we extend the RS strategy to address the Quality of Service
(QoS) constrained power minimization problem, and we demonstrate significant
gains over NoRS-based designs.Comment: Accepted for publication in IEEE Transactions on Signal Processin
Exploiting Amplitude Control in Intelligent Reflecting Surface Aided Wireless Communication with Imperfect CSI
Intelligent reflecting surface (IRS) is a promising new paradigm to achieve
high spectral and energy efficiency for future wireless networks by
reconfiguring the wireless signal propagation via passive reflection. To reap
the potential gains of IRS, channel state information (CSI) is essential,
whereas channel estimation errors are inevitable in practice due to limited
channel training resources. In this paper, in order to optimize the performance
of IRS-aided multiuser systems with imperfect CSI, we propose to jointly design
the active transmit precoding at the access point (AP) and passive reflection
coefficients of IRS, each consisting of not only the conventional phase shift
and also the newly exploited amplitude variation. First, the achievable rate of
each user is derived assuming a practical IRS channel estimation method, which
shows that the interference due to CSI errors is intricately related to the AP
transmit precoders, the channel training power and the IRS reflection
coefficients during both channel training and data transmission. Then, for the
single-user case, by combining the benefits of the penalty method, Dinkelbach
method and block successive upper-bound minimization (BSUM) method, a new
penalized Dinkelbach-BSUM algorithm is proposed to optimize the IRS reflection
coefficients for maximizing the achievable data transmission rate subjected to
CSI errors; while for the multiuser case, a new penalty dual decomposition
(PDD)-based algorithm is proposed to maximize the users' weighted sum-rate.
Simulation results are presented to validate the effectiveness of our proposed
algorithms as compared to benchmark schemes. In particular, useful insights are
drawn to characterize the effect of IRS reflection amplitude control
(with/without the conventional phase shift) on the system performance under
imperfect CSI.Comment: 15 pages, 10 figures, accepted by IEEE Transactions on Communication
Outage-Constrained Robust Beamforming for Intelligent Reflecting Surface Aided Wireless Communication
In intelligent reflecting surface (IRS) aided wireless communication systems,
channel state information (CSI) is crucial to achieve its promising passive
beamforming gains. However, CSI errors are inevitable in practice and generally
correlated over the IRS reflecting elements due to the limited training with
discrete phase shifts, which degrade the data transmission rate and
reliability. In this paper, we focus on investigating the effect of CSI errors
to the outage performance in an IRS-aided multiuser downlink communication
system. Specifically, we aim to jointly optimize the active transmit precoding
vectors at the access point (AP) and passive discrete phase shifts at the IRS
to minimize the AP's transmit power, subject to the constraints on the maximum
CSI-error induced outage probability for the users. First, we consider the
single-user case and derive the user's outage probability in terms of the mean
signal power (MSP) and variance of the received signal at the user. Since there
is a trade-off in tuning these two parameters to minimize the outage
probability, we propose to maximize their weighted sum with the optimal weight
found by one-dimensional search. Then, for the general multiuser case, since
the users' outage probabilities are difficult to obtain in closed-form due to
the inter-user interference, we propose a novel constrained stochastic
successive convex approximation (CSSCA) algorithm, which replaces the
non-convex outage probability constraints with properly designed convex
surrogate approximations. Simulation results verify the effectiveness of the
proposed robust beamfoming algorithms and show their significant performance
improvement over various benchmark schemes.Comment: 15 pages, 14 figures, accepted for publication in IEEE Transactions
on Signal Processin
Energy-efficiency for MISO-OFDMA based user-relay assisted cellular networks
The concept of improving energy-efficiency (EE) without sacrificing the service quality has become important nowadays. The combination of orthogonal frequency-division multiple-access (OFDMA) multi-antenna transmission technology and relaying is one of the key technologies to deliver the promise of reliable and high-data-rate coverage in the most cost-effective manner. In this paper, EE is studied for the downlink multiple-input single-output (MISO)-OFDMA based user-relay assisted cellular networks. EE maximization is formulated for decode and forward (DF) relaying scheme with the consideration of both transmit and circuit power consumption as well as the data rate requirements for the mobile users. The quality of-service (QoS)-constrained EE maximization, which is defined for multi-carrier, multi-user, multi-relay and multi-antenna networks, is a non-convex and combinatorial problem so it is hard to tackle. To solve this difficult problem, a radio resource management (RRM) algorithm that solves the subcarrier allocation, mode selection and power allocation separately is proposed. The efficiency of the proposed algorithm is demonstrated by numerical results for different system parameter
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