Yo-Seb Jeon

On Achievable Multiplexing Gain of BD in MIMO Broadcast Channels With Limited Feedback

To maintain a specific degree of multiplexing gain with limited feedback in multiple-input/multiple-output broadcast channels, the number of feedback bits has been increased linearly with the signal-to-noise ratio (SNR) measured in decibel, and conditions for the slope of this increase have been derived in previous studies. However, previous studies mostly focused on the chordal distance (ChD) for channel quantization, although it is not an optimally designed distance measure. Thus, the possibility exists that the same degree of multiplexing gain can be obtained by using fewer feedback bits than the existing method requires. In this paper, we propose an optimal distance measure that can maximize the multiplexing gain achieved by limited-feedback-based block diagonalization over broadcast channels. Then, we provide a sufficient condition for the number of feedback bits to achieve a specific degree of multiplexing gain with the proposed method. The sufficient number of feedback bits is given by a linear function of the SNR (decibel) as in previous studies, but the slope of the linear increase can be much less than that obtained in previous studies. As a consequence, the proposed method achieves higher multiplexing gain and corresponding throughput than the existing methods with the same number of feedback bits.

Blind detection for MIMO systems with low-resolution ADCs using supervised learning

This paper considers a multiple-input-multiple-output (MIMO) system with low-resolution analog-to-digital converters (ADCs). In this system, we propose a novel detection framework that performs data symbol detection without explicitly knowing channel state information at a receiver. The underlying idea of the proposed framework is to exploit supervised learning. Specifically, during channel training, the proposed approach sends a sequence of data symbols as pilots so that the receiver learns a nonlinear function that is determined by both a channel matrix and a quantization function of the ADCs. During data transmission, the receiver uses the learned nonlinear function to detect which data symbols were transmitted. In this context, we propose two blind detection methods to determine the nonlinear function from the training-data set. We also provide an analytical expression for the symbol-vector-error probability of the MIMO systems with one-bit ADCs when employing the proposed framework. Simulations demonstrate the performance improvement of the proposed framework compared to existing detection techniques.

Time-Domain Differential Feedback for Massive MISO-OFDM Systems in Correlated Channels

Massive multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) is a promising technology for next-generation wireless communications. However, when channel state information (CSI) at the transmitter is obtained using channel feedback, the benefits of this system are severely limited by the tradeoff between downlink capacity and feedback overhead. To solve this problem, we propose a time-domain differential feedback scheme for massive multiple-input single-output (MISO) OFDM systems. The proposed scheme exploits channel correlations in time, frequency, and space domains simultaneously by considering differential channel impulse response (CIR). To simplify the codebook design, and to reduce codeword-search complexity, we partition and then quantize the differential CIR using a number of subcodebooks. Because the total feedback bits are shared by the subcodebooks, we further optimize the bit allocation for them to minimize the total quantization error. For this, we analyze the quantization error of the proposed scheme and then use the analysis results for the bit-allocation optimization. In simulations, the proposed scheme efficiently exploits all correlations and achieves significant spectral-efficiency gain compared to conventional feedback schemes.

On Achievable Rate of User Selection for MIMO Broadcast Channels With Limited Feedback

We consider the block diagonalization (BD) and user selection based on limited feedback in multiple antenna broadcast channels. With limited feedback, due to the imperfect channel state information at the transmitter (CSIT), BD cannot completely eliminate multiuser interference, and the throughput is correspondingly lower than that achieved with perfect CSIT. Nevertheless, the achievable multiuser diversity gain can be the same, such that limited-feedback-based BD can achieve an optimal throughput growth as the number of users increases. To show this, we first propose a channel quality indicator (CQI) for user selection. The CQI is designed to accurately estimate an achievable rate of each user and is given by an expected rate, where the expectation is solely taken over precoding matrices which cannot be known at the feedback stage. With the proper CQI, user selection can benefit from a large number of users in the system. As a result, we show that the BD can achieve an asymptotically optimal growth in throughput with the proposed CQI, based solely on a finite-rate feedback of channel information.

Energy Beamforming for Wireless Power Transfer in MISO Heterogeneous Network With Power Beacon

This letter considers an energy harvesting heterogeneous network (EHHN) in which a power beacon (PB) for radio-frequency-energy transfer coexists with a base station for information transfer. In this network, a tradeoff exists between harvested energy and information rate, because the energy transfer of the PB causes the interference to the information transfer. To determine the optimal energy-transfer strategy under this tradeoff, we propose an energy beamforming scheme that maximizes the weighted sum of the harvested energy and the information rate in multiple-input single-output EHHN. Because the weighted-sum-maximization problem is nonconvex, we partition it into a difference of convex functions problem and a convex-maximization problem, and then solve them using efficient convex-based algorithms. In simulation, the proposed beamforming scheme is shown to achieve a near-optimal rate-energy region for the considered problem.

Block diagonalization and user selection for MIMO broadcast channels with limited feedback

We consider limited-feedback-based block diagonalization (BD) and user selection for multiple antenna broadcast channels. With limited feedback, the conventional channel norm can not be a good estimate for channel quality of each user, because multiuser interference is not completely eliminated by using BD under limited channel direction information. Therefore, we need an appropriate channel quality indicator (CQI) to increase the diversity gain achieved by multiuser scheduling. To this end, we consider a determinant-based instantaneous channel capacity and propose a new CQI by taking an expectation over precoding matrices that can not be known at the feedback stage. Simulation results show that the sum rate with the proposed CQI is much higher than the sum rate with conventional CQIs.

Distributed Block Diagonalization with Selective Zero Forcing for Multicell MU-MIMO Systems

This letter proposes a distributed beamforming scheme based on block diagonalization (BD) for multicell multiuser multiple-input multiple-output (MU-MIMO) downlink systems. Although conventional BD can be directly extended to these systems in a distributed manner, it suffers from sum-rate degradation due to strict zero-forcing (ZF) constraints. To overcome this problem, we introduce a sum-rate maximization problem for BD, to find the optimal selection of ZF constraints. Then we propose a search algorithm to solve the problem in a distributed and heuristic manner. Simulation results show that the proposed scheme effectively finds a near-optimal solution and significantly improves simple extended BD such that it can provide higher sum rate than the conventional schemes.

MIMO systems with low-resolution ADCs: Linear coding approach

This paper considers a multiple-input multiple-output (MIMO) system with low-resolution analog-to-digital converters (ADCs). In this system, we present a new MIMO detection approach using coding theory. The principal idea of the proposed approach is to transform a non-linear MIMO channel to a linear MIMO channel by leveraging both a p-level quantizer and a lattice code where p ≥ 2. After transforming to the linear MIMO channel with the sets of finite input and output elements, efficient MIMO detection methods are proposed to attain both diversity and multiplexing gains by using algebraic coding theory. In particular, using the proposed methods, the analytical characterizations of achievable rates are derived for different MIMO configurations. One major observation is that the proposed approach is particularly useful for a large MIMO system with the ADCs that use a few bits.

On the degrees of freedom of wide-band multi-cell multiple access channels with No CSIT

This paper considers a K-cell multiple access channel with inter-symbol interference (ISI). The primary finding of this paper is that, without instantaneous channel state information at a transmitter, the interference-free sum degrees of freedom of K is asymptotically achievable when the number of users per cell is sufficiently large, and also when the number of channel-impulse-response taps of desired links is greater than that of interfering links. This achievability is shown by a blind interference management method that exploits the relativity in delay spreads between desired and interfering links.