Hyukjin Chae

Feedback-Topology Designs for Interference Alignment in MIMO Interference Channels

Interference alignment (IA) is a joint-transmission technique for the interference channel that achieves the maximum degrees-of-freedom and provides linear scaling of the capacity with the number of users for high signal-to-noise ratios (SNRs). Most prior work on IA is based on the impractical assumption that perfect and global channel-state information (CSI) is available at all transmitters. However, to implement IA, each receiver has to feed back CSI to all interferers, resulting in overwhelming feedback overhead. In particular, the sum feedback rate of each receiver scales quadratically with the number of users even if the feedback CSI is quantized. To substantially suppress feedback overhead, this paper focuses on designing efficient arrangements of feedback links, called feedback topologies, under the IA constraint. For the multiple-input multiple-output (MIMO) K-user interference channel, we propose the feedback topology that supports sequential CSI exchange (feedback and feedforward) between transmitters and receivers so as to achieve IA progressively. This feedback topology is shown to reduce the network feedback overhead from a quadratic function of K to a linear one. To reduce the delay in the sequential CSI exchange, an alternative feedback topology is designed for supporting two-hop feedback via a control station, which also achieves the linear feedback scaling with K. Next, given the proposed feedback topologies, the feedback-bit allocation algorithm is designed for allocating feedback bits by each receiver to different feedback links so as to regulate the residual interference caused by finite-rate feedback. Simulation results demonstrate that the proposed bit allocation leads to significant throughput gains especially in strong interference environments.

Energy-Efficient Resource Allocation for Mobile-Edge Computation Offloading

Mobile-edge computation offloading (MECO) off-loads intensive mobile computation to clouds located at the edges of cellular networks. Thereby, MECO is envisioned as a promising technique for prolonging the battery lives and enhancing the computation capacities of mobiles. In this paper, we study resource allocation for a multiuser MECO system based on time-division multiple access (TDMA) and orthogonal frequency-division multiple access (OFDMA). First, for the TDMA MECO system with infinite or finite cloud computation capacity, the optimal resource allocation is formulated as a convex optimization problem for minimizing the weighted sum mobile energy consumption under the constraint on computation latency. The optimal policy is proved to have a threshold-based structure with respect to a derived offloading priority function, which yields priorities for users according to their channel gains and local computing energy consumption. As a result, users with priorities above and below a given threshold perform complete and minimum offloading, respectively. Moreover, for the cloud with finite capacity, a sub-optimal resource-allocation algorithm is proposed to reduce the computation complexity for computing the threshold. Next, we consider the OFDMA MECO system, for which the optimal resource allocation is formulated as a mixed-integer problem. To solve this challenging problem and characterize its policy structure, a low-complexity sub-optimal algorithm is proposed by transforming the OFDMA problem to its TDMA counterpart. The corresponding resource allocation is derived by defining an average offloading priority function and shown to have close-to-optimal performance in simulation.

Energy Efficient Mobile Cloud Computing Powered by Wireless Energy Transfer

Achieving long battery lives or even self sustainability has been a long standing challenge for designing mobile devices. This paper presents a novel solution that seamlessly integrates two technologies, mobile cloud computing and microwave power transfer (MPT), to enable computation in passive low-complexity devices such as sensors and wearable computing devices. Specifically, considering a single-user system, a base station (BS) either transfers power to or offloads computation from a mobile to the cloud; the mobile uses harvested energy to compute given data either locally or by offloading. A framework for energy efficient computing is proposed that comprises a set of policies for controlling CPU cycles for the mode of local computing, time division between MPT and offloading for the other mode of offloading, and mode selection. Given the CPU-cycle statistics information and channel state information (CSI), the policies aim at maximizing the probability of successfully computing given data, called computing probability, under the energy harvesting and deadline constraints. The policy optimization is translated into the equivalent problems of minimizing the mobile energy consumption for local computing and maximizing the mobile energy savings for offloading which are solved using convex optimization theory. The structures of the resultant policies are characterized in closed form. Furthermore, given non-causal CSI, the said analytical framework is further developed to support computation load allocation over multiple channel realizations, which further increases the computing probability. Last, simulation demonstrates the feasibility of wirelessly powered mobile cloud computing and the gain of its optimal control.

Efficient Feedback Design for Interference Alignment in MIMO Interference Channel

Interference alignment (IA) is a joint-transmission technique that achieves the capacity of the interference channel for high signal-to-noise ratios (SNRs). However, most prior works on IA are based on the impractical assumption that perfect and global channel-state information(CSI) is available at all transmitters, resulting in overwhelming feedback overhead. To substantially suppress the feedback overhead, this paper proposes an efficient design of the feedback framework for IA in the K-user multiple-input multiple-output (MIMO) interference channel. The proposed feedback topology supports sequential CSI exchange (feedback and feedforward) between transmitters and receivers and reduces the feedback overhead from a cubic function of K to a linear one, compared to conventional feedback approaches. Given the proposed feedback topology, we consider the limited feedback channel from the receivers to corresponding interferers and analyze the effect of quantization error which generates the residual interference. Also, an efficient feedback-bit allocation algorithm that minimizes the upper-bound of sum residual interference is proposed.

Generalized Inverse Aided PAPR-Aware Linear Precoder Design for MIMO-OFDM System

We propose a linear precoding scheme for a single user multiple-input-multiple-output orthogonal frequency division multiplexing (OFDM) system to minimize peak to average power ratio (PAPR) by using redundant spatial resources at the transmitter through a singular-value-decomposition-based generalized inverse. The proposed precoder based on the generalized inverse is composed of two parts. One is for minimizing PAPR, and the other is for obtaining the multiplexing gain. Moreover, the proposed precoder contains a scalar parameter α that quantifies the received signal-to-noise power ratio (SNR) loss at the cost of PAPR reduction. Even in cases of small SNR loss, the proposed scheme dramatically reduces PAPR. Furthermore, simulation results show that we can obtain a PAPR close to 1 by using dozens of transmission antennas with small SNR loss.

Multimode Random Beamforming for Multiuser Downlink MIMO System with Limited Feedback

Multimode transmission has been studied to improve BER and outage capacity in the point-to-point system. In this paper, we consider the multimode transmission for downlink MIMO based on random beamforming(RBF) with limited feedback. We propose four heuristic mode selection and feedback policies to improve the sum rate and outage performance of downlink RBF system based on two approaches: distributed and centralized. Simulation results show that the centralized mode selection scheme with reasonable feedback amount provides waterfilling effect as well as the multiuser diversity gain, leading to the performance improvement of RBF in terms of sum rate and outage probability.

Live Prefetching for Mobile Computation Offloading

Mobile computation offloading refers to techniques for offloading computation intensive tasks from mobile devices to the cloud so as to lengthen the formers’ battery lives and enrich their features. The conventional designs fetch (transfer) user-specific data from mobiles to the cloud prior to computing, called offline prefetching. However, this approach can potentially result in excessive fetching of large volumes of data and cause heavy loads on radio-access networks. To solve this problem, the novel technique of live prefetching, which seamlessly integrates the task-level computation prediction and prefetching within the cloud-computing process of a large program with numerous tasks, is proposed in this paper. The technique avoids excessive fetching but retains the feature of leveraging prediction to reduce the program runtime and mobile transmission energy. By modeling the tasks in an offloaded program as a stochastic sequence, stochastic optimization is applied to design fetching policies to minimize mobile energy consumption under a deadline constraint. The policies enable real-time control of the prefetched-data sizes of candidates for future tasks. For slow fading, the optimal policy is derived and shown to have a threshold-based structure, selecting candidate tasks for prefetching and controlling their prefetched data based on their likelihoods. The result is extended to design close-to-optimal prefetching policies to fast fading channels. Compared with fetching without prediction, live prefetching is shown theoretically to always achieve reduction on mobile energy consumption.

Interference alignment with range expansion in a heterogeneous MIMO network

Managing smaller cells such as micro and picocells in conventional macro cellular networks is expected to provide better quality of data service. However, frequency reuse-1 operation of smaller cells with the macrocells can create additional “cell edge” area that can degrade the system performance significantly. This paper proposes an interference avoidance scheme based on interference alignment and region based zero-forcing in a heterogeneous cellular system consisting of two overlaying macro and picocells with multiple antennas. The proposed transmission strategy effectively suppresses the cross-tier interference at both macro and pico users who are in severe interference limited region, providing the benefits of coverage expansion of picoocells. Simulation results show that the proposed interference avoidance scheme provides a significant performance gain over the non-coordinated scheme in the two macro/picocells network.

A Closed Form Approximation of the Sum Rate of Random Beamforming with Multiple Codebooks and its Application to Control of the Number of Codebooks

In this letter, a closed form approximation of the sum rate of random beamforming with multiple codebooks is derived. It is also proved that signal to interference plus noise ratios (SINRs) over different codeboks are independently distributed on the contrast to existing belief on dependence of SINRs. Through numerical simulation, it is shown that the derived approximation is tightly matched to the actual sum rate, and control of the number of codebooks based on the closed form approximation achieves near optimal sum rate performance.

Low Complexity Zeroforcing Precoder Design Under Per-Antenna Power Constraints

The K-user multiple-input single-output broadcast channel is considered under per-antenna power constraints, i.e., each transmit antenna must satisfy its own power constraint. A low complexity zeroforcing (ZF) precoder is proposed when the number of transmit antennas M is greater than K. The proposed precoder design significantly reduces computational complexity for the precoder construction while attaining the sum spectral efficiency close to that achievable by the optimal ZF precoder.