35,810 research outputs found
Globally Optimal Spectrum- and Energy-Efficient Beamforming for Rate Splitting Multiple Access
Rate splitting multiple access (RSMA) is a promising non-orthogonal
transmission strategy for next-generation wireless networks. It has been shown
to outperform existing multiple access schemes in terms of spectral and energy
efficiency when suboptimal beamforming schemes are employed. In this work, we
fill the gap between suboptimal and truly optimal beamforming schemes and
conclusively establish the superior spectral and energy efficiency of RSMA. To
this end, we propose a successive incumbent transcending (SIT) branch and bound
(BB) algorithm to find globally optimal beamforming solutions that maximize the
weighted sum rate or energy efficiency of RSMA in Gaussian multiple-input
single-output (MISO) broadcast channels. Numerical results show that RSMA
exhibits an explicit globally optimal spectral and energy efficiency gain over
conventional multi-user linear precoding (MU-LP) and power-domain
non-orthogonal multiple access (NOMA). Compared to existing globally optimal
beamforming algorithms for MU-LP, the proposed SIT BB not only improves the
numerical stability but also achieves faster convergence. Moreover, for the
first time, we show that the spectral/energy efficiency of RSMA achieved by
suboptimal beamforming schemes (including weighted minimum mean squared error
(WMMSE) and successive convex approximation) almost coincides with the
corresponding globally optimal performance, making it a valid choice for
performance comparisons. The globally optimal results provided in this work are
imperative to the ongoing research on RSMA as they serve as benchmarks for
existing suboptimal beamforming strategies and those to be developed in
multi-antenna broadcast channels
Power Allocation and Time-Domain Artificial Noise Design for Wiretap OFDM with Discrete Inputs
Optimal power allocation for orthogonal frequency division multiplexing
(OFDM) wiretap channels with Gaussian channel inputs has already been studied
in some previous works from an information theoretical viewpoint. However,
these results are not sufficient for practical system design. One reason is
that discrete channel inputs, such as quadrature amplitude modulation (QAM)
signals, instead of Gaussian channel inputs, are deployed in current practical
wireless systems to maintain moderate peak transmission power and receiver
complexity. In this paper, we investigate the power allocation and artificial
noise design for OFDM wiretap channels with discrete channel inputs. We first
prove that the secrecy rate function for discrete channel inputs is nonconcave
with respect to the transmission power. To resolve the corresponding nonconvex
secrecy rate maximization problem, we develop a low-complexity power allocation
algorithm, which yields a duality gap diminishing in the order of
O(1/\sqrt{N}), where N is the number of subcarriers of OFDM. We then show that
independent frequency-domain artificial noise cannot improve the secrecy rate
of single-antenna wiretap channels. Towards this end, we propose a novel
time-domain artificial noise design which exploits temporal degrees of freedom
provided by the cyclic prefix of OFDM systems {to jam the eavesdropper and
boost the secrecy rate even with a single antenna at the transmitter}.
Numerical results are provided to illustrate the performance of the proposed
design schemes.Comment: 12 pages, 7 figures, accepted by IEEE Transactions on Wireless
Communications, Jan. 201
Computation Alignment: Capacity Approximation without Noise Accumulation
Consider several source nodes communicating across a wireless network to a
destination node with the help of several layers of relay nodes. Recent work by
Avestimehr et al. has approximated the capacity of this network up to an
additive gap. The communication scheme achieving this capacity approximation is
based on compress-and-forward, resulting in noise accumulation as the messages
traverse the network. As a consequence, the approximation gap increases
linearly with the network depth.
This paper develops a computation alignment strategy that can approach the
capacity of a class of layered, time-varying wireless relay networks up to an
approximation gap that is independent of the network depth. This strategy is
based on the compute-and-forward framework, which enables relays to decode
deterministic functions of the transmitted messages. Alone, compute-and-forward
is insufficient to approach the capacity as it incurs a penalty for
approximating the wireless channel with complex-valued coefficients by a
channel with integer coefficients. Here, this penalty is circumvented by
carefully matching channel realizations across time slots to create
integer-valued effective channels that are well-suited to compute-and-forward.
Unlike prior constant gap results, the approximation gap obtained in this paper
also depends closely on the fading statistics, which are assumed to be i.i.d.
Rayleigh.Comment: 36 pages, to appear in IEEE Transactions on Information Theor
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