58 research outputs found
Dynamic Resource Allocation in Cognitive Radio Networks: A Convex Optimization Perspective
This article provides an overview of the state-of-art results on
communication resource allocation over space, time, and frequency for emerging
cognitive radio (CR) wireless networks. Focusing on the
interference-power/interference-temperature (IT) constraint approach for CRs to
protect primary radio transmissions, many new and challenging problems
regarding the design of CR systems are formulated, and some of the
corresponding solutions are shown to be obtainable by restructuring some
classic results known for traditional (non-CR) wireless networks. It is
demonstrated that convex optimization plays an essential role in solving these
problems, in a both rigorous and efficient way. Promising research directions
on interference management for CR and other related multiuser communication
systems are discussed.Comment: to appear in IEEE Signal Processing Magazine, special issue on convex
optimization for 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
Cooperative Precoding/Resource Allocation Games under Spectral Mask and Total Power Constraints
The use of orthogonal signaling schemes such as time-, frequency-, or
code-division multiplexing (T-, F-, CDM) in multi-user systems allows for
power-efficient simple receivers. It is shown in this paper that by using
orthogonal signaling on frequency selective fading channels, the cooperative
Nash bargaining (NB)-based precoding games for multi-user systems, which aim at
maximizing the information rates of all users, are simplified to the
corresponding cooperative resource allocation games. The latter provides
additional practically desired simplifications to transmitter design and
significantly reduces the overhead during user cooperation. The complexity of
the corresponding precoding/resource allocation games, however, depends on the
constraints imposed on the users. If only spectral mask constraints are
present, the corresponding cooperative NB problem can be formulated as a convex
optimization problem and solved efficiently in a distributed manner using dual
decomposition based algorithm. However, the NB problem is non-convex if total
power constraints are also imposed on the users. In this case, the complexity
associate with finding the NB solution is unacceptably high. Therefore, the
multi-user systems are categorized into bandwidth- and power-dominant based on
a bottleneck resource, and different manners of cooperation are developed for
each type of systems for the case of two-users. Such classification guarantees
that the solution obtained in each case is Pareto-optimal and actually can be
identical to the optimal solution, while the complexity is significantly
reduced. Simulation results demonstrate the efficiency of the proposed
cooperative precoding/resource allocation strategies and the reduced complexity
of the proposed algorithms.Comment: 33 pages, 8 figures, Submitted to the IEEE Trans. Signal Processing
in Oct. 200
Energy Efficient Communications in MIMO Wireless Channels: Information Theoretical Limits
ISBN : 978-1466501072This chapter is focused on defining and optimizing an energy-efficiency metric for MIMO systems. This metric, which expresses in bit per Joule, allows one to measure how much information is effectively transferred to the transmitter per unit cost of energy consumed at the transmitter. For a MIMO point-to-point communication (single user MIMO channels) this metric can be useful to determine what power level, precoding scheme, training length, or number of antennas have to be used for obtaining the maximum information that is effectively transferred per unit energy spent. Then, we move from a physical layer-type approach to a cross-layer design of energy-efficient power control by including the effects a queue with finite size at the transmitter. As a last step we study a distributed multiple user scenario (MIMO multiple access channels) where each user selfishly maximizes its energy-efficiency by choosing its best individual power allocation policy. Here, we present the most relevant results in this field in a concise and comprehensible manner
Resource Allocation for Multiple-Input and Multiple-Output Interference Networks
To meet the exponentially increasing traffic data driven by the rapidly growing mobile subscriptions, both industry and academia are exploring the potential of a new genera- tion (5G) of wireless technologies. An important 5G goal is to achieve high data rate. Small cells with spectrum sharing and multiple-input multiple-output (MIMO) techniques are one of the most promising 5G technologies, since it enables to increase the aggregate data rate by improving the spectral efficiency, nodes density and transmission bandwidth, respectively. However, the increased interference in the densified networks will in return limit the achievable rate performance if not properly managed.
The considered setup can be modeled as MIMO interference networks, which can be classified into the K-user MIMO interference channel (IC) and the K-cell MIMO interfering broadcast channel/multiple access channel (MIMO-IBC/IMAC) according to the number of mobile stations (MSs) simultaneously served by each base station (BS). The thesis considers two physical layer (PHY) resource allocation problems that deal with the interference for both models: 1) Pareto boundary computation for the achiev- able rate region in a K-user single-stream MIMO IC and 2) grouping-based interference alignment (GIA) with optimized IA-Cell assignment in a MIMO-IMAC under limited feedback. In each problem, the thesis seeks to provide a deeper understanding of the system and novel mathematical results, along with supporting numerical examples. Some of the main contributions can be summarized as follows.
It is an open problem to compute the Pareto boundary of the achievable rate region for a K-user single-stream MIMO IC. The K-user single-stream MIMO IC models multiple transmitter-receiver pairs which operate over the same spectrum simultaneously. Each transmitter and each receiver is equipped with multiple antennas, and a single desired data stream is communicated in each transmitter-receiver link. The individual achievable rates of the K users form a K-dimensional achievable rate region. To find efficient operating points in the achievable rate region, the Pareto boundary computation problem, which can be formulated as a multi-objective optimization problem, needs to be solved. The thesis transforms the multi-objective optimization problem to two single-objective optimization problems–single constraint rate maximization problem and alternating rate profile optimization problem, based on the formulations of the ε-constraint optimization and the weighted Chebyshev optimization, respectively. The thesis proposes two alternating optimization algorithms to solve both single-objective optimization problems. The convergence of both algorithms is guaranteed. Also, a heuristic initialization scheme is provided for each algorithm to achieve a high-quality solution. By varying the weights in each single-objective optimization problem, numerical results show that both algorithms provide an inner bound very close to the Pareto boundary. Furthermore, the thesis also computes some key points exactly on the Pareto boundary in closed-form.
A framework for interference alignment (IA) under limited feedback is proposed for a MIMO-IMAC. The MIMO-IMAC well matches the uplink scenario in cellular system, where multiple cells share their spectrum and operate simultaneously. In each cell, a BS receives the desired signals from multiple MSs within its own cell and each BS and each MS is equipped with multi-antenna. By allowing the inter-cell coordination, the thesis develops a distributed IA framework under limited feedback from three aspects: the GIA, the IA-Cell assignment and dynamic feedback bit allocation (DBA), respec- tively. Firstly, the thesis provides a complete study along with some new improvements of the GIA, which enables to compute the exact IA precoders in closed-form, based on local channel state information at the receiver (CSIR). Secondly, the concept of IA-Cell assignment is introduced and its effect on the achievable rate and degrees of freedom (DoF) performance is analyzed. Two distributed matching approaches and one centralized assignment approach are proposed to find a good IA-Cell assignment in three scenrios with different backhaul overhead. Thirdly, under limited feedback, the thesis derives an upper bound of the residual interference to noise ratio (RINR), formulates and solves a corresponding DBA problem. Finally, numerical results show that the proposed GIA with optimized IA-Cell assignment and the DBA greatly outperforms the traditional GIA algorithm
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