More Like this

1. MISO Broadcast Channel under Unequal Link Coherence Times and Channel State Information

   Fadel Shady, Mohammed ; Nosratinia, Aria ( September 2020 , Entropy)

   null (Ed.)

   The broadcast channel may experience unequal link coherence times due to a number of factors including variation in node mobility or local scattering conditions. This means the block fading model for different links may have nonidentical block length, and the channel state information for the links may also not be identical. The faster the fading and the shorter the fading block length, the more often the link needs to be trained and estimated at the receiver, and the more likely that channel state information (CSI) is stale or unavailable at the transmitter. This paper investigates a MISO broadcast channel where some receivers experience longer coherence intervals and other receivers experience shorter coherence intervals and must estimate their receive-side CSI (CSIR) frequently. We consider a variety of transmit-side CSI (CSIT) conditions for the above mentioned model, including no CSIT, delayed CSIT, or hybrid CSIT. To investigate the degrees of freedom region, we employ interference alignment and beamforming along with a product superposition that allows simultaneous but noncontaminating transmission of pilots and data to different receivers. Outer bounds employ the extremal entropy inequality as well as a bounding of the performance of a discrete, memoryless, multiuser, multilevel broadcast channel. For several cases, inner and outer bounds are established that either partially meet, or the gap diminishes with increasing coherence time. 

   more » « less
2. Transmit Correlation Diversity: Generalization, New Techniques, and Improved Bounds

   https://doi.org/10.1109/TIT.2022.3146523  

   Zhang, Fan ; Ngo, Khac-Hoang ; Yang, Sheng ; Nosratinia, Aria ( January 2022 , IEEE Transactions on Information Theory)

   When the users in a MIMO broadcast channel experience different spatial transmit correlation matrices, a class of gains is produced that is denoted transmit correlation diversity. This idea was conceived for channels in which transmit correlation matrices have mutually exclusive eigenspaces, allowing non-interfering training and transmission. This paper broadens the scope of transmit correlation diversity to the case of partially and fully overlapping eigenspaces and introduces techniques to harvest these generalized gains. For the two-user MIMO broadcast channel, we derive achievable degrees of freedom (DoF) and achievable rate regions with/without channel state information at the receiver (CSIR). When CSIR is available, the proposed achievable DoF region is tight in some configurations of the number of receive antennas and the channel correlation ranks. We then extend the DoF results to the K-user case by analyzing the interference graph that characterizes the overlapping structure of the eigenspaces. Our achievability results employ a combination of product superposition in the common part of the eigenspaces, and pre-beamforming (rate splitting) to create multiple data streams in non-overlapping parts of the eigenspaces. Massive MIMO is a natural example in which spatially correlated link gains are likely to occur. We study the achievable downlink sum rate for a frequency-division duplex massive MIMO system under transmit correlation diversity. 

   more » « less
3. Spatially Correlated MIMO Broadcast Channel with Partially Overlapping Correlation Eigenspaces

   https://doi.org/10.1109/ISIT.2018.8437592  

   Zhang, Fan ; Nosratinia, Aria ( June 2018 , IEEE International Symposium on Information Theory (ISIT))

   The spatially correlated MIMO broadcast channel has grown in importance due to emerging interest in massive MIMO and mm-wave communication, but much about this channel remains unknown. In this paper, we study a two-user MIMO broadcast channel where the spatial correlation matrices corresponding to the two receivers have eigenspaces that are neither identical nor disjoint, but are partially overlapped. Spatially correlated channels occur in e.g. massive MIMO and furthermore different links may credibly have correlation eigenspaces that are neither disjoint nor equal, therefore this problem is practically motivated. This paper develops a new approach for this scenario and calculates the corresponding degrees of freedom. Our technique involves a careful decomposition of the signaling space to allow a combination of pre-beamforming along directions that depend on the relative positioning of the non-overlapping and overlapping components of the eigenspaces, along with the product superposition technique. The ideas are demonstrated with a toy example, are developed in two conditions of varying complexity, and are illuminated by numerical results. 

   more » « less
4. A Markovian Model-Driven Deep Learning Framework for Massive MIMO CSI Feedback

   Zhenyu Liu, Mason del ( January 2021 , IEEE transactions on wireless communications)

   null (Ed.)

   Channel state information (CSI) plays a vital role in scheduling and capacity-approaching transmission optimization of massive MIMO communication systems. In frequency division duplex (FDD) MIMO systems, forward link CSI reconstruction at transmitter relies on CSI feedback from receiving nodes and must carefully weigh the tradeoff between reconstruction accuracy and feedback bandwidth. Recent application of recurrent neural networks (RNN) has demonstrated promising results of massive MIMO CSI feedback compression. However, the cost of computation and memory associated with RNN deep learning remains high. In this work, we exploit channel temporal coherence to improve learning accuracy and feedback efficiency. Leveraging a Markovian model, we develop a deep convolutional neural network (CNN)-based framework called MarkovNet to efficiently encode CSI feedback to improve accuracy and efficiency. We explore important physical insights including spherical normalization of input data and deep learning network optimizations in feedback compression. We demonstrate that MarkovNet provides a substantial performance improvement and computational complexity reduction over the RNN-based work.We demonstrate MarkovNet’s performance under different MIMO configurations and for a range of feedback intervals and rates. CSI recovery with MarkovNet outperforms RNN-based CSI estimation with only a fraction of computational cost. 

   more » « less
5. Joint Relay Selection and Beam Management Based on Deep Reinforcement Learning for Millimeter Wave Vehicular Communication

   https://doi.org/10.1109/TVT.2023.3274763  

   Kim, Dohyun ; Castellanos, Miguel R. ; Heath, Robert W. ( October 2023 , IEEE Transactions on Vehicular Technology)

   Cooperative relays improve reliability and coverage in wireless networks by providing multiple paths for data transmission. Relaying will play an essential role in vehicular networks at higher frequency bands, where mobility and frequent signal blockages cause link outages. To ensure connectivity in a relay-aided vehicular network, the relay selection policy should be designed to efficiently find unblocked relays. Inspired by recent advances in beam management in mobile millimeter wave (mmWave) networks, this paper address the question: how can the best relay be selected with minimal overhead from beam management? In this regard, we formulate a sequential decision problem to jointly optimize relay selection and beam management. We propose a joint relay selection and beam management policy based on deep reinforcement learning (DRL) using the Markov property of beam in- dices and beam measurements. The proposed DRL-based algorithm learns time-varying thresholds that adapt to the dynamic channel conditions and traffic patterns. Numeri- cal experiments demonstrate that the proposed algorithm outperforms baselines without prior channel knowledge. Moreover, the DRL-based algorithm can maintain high spectral efficiency under fast-varying channels. 

   more » « less