Optimal Resource Allocation in Coordinated Multi-Cell Systems

Abstract

The use of multiple antennas at base stations is a key component in the design of cellular communication systems that can meet high-capacity demands in the downlink. Under ideal conditions, the gain of employing multiple antennas is well-recognized: the data throughput increases linearly with the number of transmit antennas if the spatial dimension is utilized to serve many users in parallel. The practical performance of multi-cell systems is, however, limited by a variety of nonidealities, such as insufficient channel knowledge, high computational complexity, heterogeneous user conditions, limited backhaul capacity, transceiver impairments, and the constrained level of coordination between base stations. This tutorial presents a general framework for modeling different multi-cell scenarios, including clustered joint transmission, coordinated beamforming, interference channels, cognitive radio, and spectrum sharing between operators. The framework enables joint analysis and insights that are both scenario independent and dependent.The performance of multi-cell systems depends on the resource allocation; that is, how the time, power, frequency, and spatial resources are divided among users. A comprehensive characterization of resource allocation problem categories is provided, along with the signal processing algorithms that solve them. The inherent difficulties are revealed: (a) the overwhelming spatial degrees-of-freedom created by the multitude of transmit antennas; and (b) the fundamental tradeoff between maximizing aggregate system throughput and maintaining user fairness. The tutorial provides a pragmatic foundation for resource allocation where the system utility metric can be selected to achieve practical feasibility. The structure of optimal resource allocation is also derived, in terms of beamforming parameterizations and optimal operating points.This tutorial provides a solid ground and understanding for optimization of practical multi-cell systems, including the impact of the nonidealities mentioned above. The Matlab code is available online for some of the examples and algorithms in this tutorial. IntroductionThis section describes a general framework for modeling different types of multi-cell systems and measuring their performance -both in terms of system utility and individual user performance. The framework is based on the concept of dynamic cooperation clusters, which enables unified analysis of everything from interference channels and cognitive radio to cellular networks with global joint transmission. The concept of resource allocation is defined as allocating transmit power among users and spatial directions, while satisfying a set of power constraints that have physical, regulatory, and economic implications. A major complication in resource allocation is the inter-user interference that arises and limits the performance when multiple users are served in parallel. Resource allocation is particularly complex when multiple antennas are employed at each base station. However, the throu...

On the impact of transceiver impairments on af relaying

“…However, in practice, hardware suffers from impairments; for example, phase noise, IQ imbalance, and amplifier non-linearities [4][5][6]. These have a deleterious impact on the achievable performance [7][8][9][10][11][12][13]. This effect is more pronounced in high-rate systems, especially those employing inexpensive hardware [5].…”

“…This effect is more pronounced in high-rate systems, especially those employing inexpensive hardware [5]. For instance, some recent works have demonstrated that non-ideal hardware severely affects single-hop multiantenna systems; [6,8] proved that there is a finite capacity limit at high SNR, while [9,13] showed that existing signal processing algorithms need to be re-designed to account for these impairments.…”

Closed-form optimality characterization of network-assisted device-to-device communications

“…Besides the conclusions in Section III-A, (8) shows that D2D mode is optimal when the interference received at UE 2 is much smaller than the one received at the base station, i.e., I D2D I ul . By solving (8) as a quadratic equation, we have…”

“…As shown in [8], the optimization problems (P1) and (P2) are tightly connected [8]: let the optimal solution to (P1) with transmit power p * UE be denotedR * , then the optimal solution to (P2) with the QoS levelR * is exactly p * UE . Nevertheless, we show that these optimization problems behave differently in terms of when D2D mode is preferable over cellular mode, and vice versa.…”

“…Furthermore, we take into account the effects of multiple antennas in the BS as it is an important feature of LTE and IMT-Advanced systems that enables simultaneous scheduling of spatially separated users [1], [8], [9]. The mode selection problem is formulated with two objectives: maximizing the QoS for a given transmit power, and minimizing the power for a given QoS.…”