Interference Alignment for Partially Connected MIMO Cellular Networks

In this paper, we propose an iterative interference alignment (IA) algorithm for MIMO cellular networks with partial connectivity, which is induced by heterogeneous path losses and spatial correlation. Such systems impose several key technical challenges in the IA algorithm design, namely the overlapping between the direct and interfering links due to the MIMO cellular topology as well as how to exploit the partial connectivity. We shall address these challenges and propose a three stage IA algorithm. As illustration, we analyze the achievable degree of freedom (DoF) of the proposed algorithm for a symmetric partially connected MIMO cellular network. We show that there is significant DoF gain compared with conventional IA algorithms due to partial connectivity. The derived DoF bound is also backward compatible with that achieved on fully connected K-pair MIMO interference channels.Comment: Submitted to IEEE Transactions on Signal Processing, accepte

Multi-stream iterative SVD for massive MIMO communication systems under time varying channels

Singular value decomposition (SVD) plays an important role in signal processing for multi-input multi-output (MIMO) communication systems. Under massive MIMO scenarios, as the channel matrix is very large, implementing SVD at every frame is highly inefficient. Existing literature on iterative SVD algorithms are mostly heuristic based, and the associated tracking performance under time-varying channels is not clear. The difficulties of deriving and analyzing SVD algorithms are due to the non-convexity of the associated optimization problem and the time-varying nature of the MIMO channel. In this paper, we formulate the problem on Grassmann manifolds and derive a multi-stream iterative SVD algorithm using optimization techniques. To enhance the tracking performance under time-varying channels, we propose a compensation algorithm to offset the motion of the time-varying target eigenspace. We analyze the convergence behavior of the proposed algorithm, where we show that under some mild conditions, the proposed iterative SVD algorithm with compensations has zero tracking error, despite the underlying problem being non-convex and the channel being time-varying. The complexity of the algorithm is only O(n2p) for estimating p singular vectors, compared with O(n3) for the SVD of a n × n channel matrix. © 2014 IEEE

Delay-aware cross-layer design for device-to-device communications in future cellular systems

Device-to-device (D2D) communications allow direct communications between nodes without transmitting data via the base stations in cellular systems, which could bring significant performance improvement. Since most applications are delay-sensitive, it is very important to consider delay performance in addition to physical layer throughput for D2D communications. To improve delay performance it is necessary to dynamically control the radio resource in a cross-layer way according to both the channel fading information and the queue length information. The former allows an observation of good transmission opportunity and the latter provides the urgency of data flows. However, the resource control with delay constraints involves stochastic optimization, which is very challenging. In this article we first summarize various approaches to solve the delay-aware resource allocation problems for D2D communications. We propose a low complexity practical solution by exploiting the interference filtering property of CSMA-like MAC protocols in the D2D system. Based on the solution structure, we further discuss the implementation issues based on LTE-Advanced systems and evaluate the associated performance and complexity. Finally we discuss the choice of MAC parameters for the overall D2D system performance. © 1979-2012 IEEE

Partial CSI feedback design for interference alignment in MIMO cellular networks

Interference alignment (IA) can achieve the optimal capacity scaling with respect to SNR but most existing IA designs require full channel state information (CSI) at the transmitters. In this paper, we consider IA processing with partial CSI feedback in MIMO cellular networks and we use the feedback dimension to quantify the first order CSI feedback cost. Conventional IA cannot be used because only partial CSI knowledge can be used to design the IA pre-coders. Therefore, we establish a new set of feasibility conditions for IA under the proposed partial CSI feedback scheme. Based on these results, we formulate the problem of CSI feedback dimension minimization subject to the constraints of IA feasibility. We further propose an asymptotically optimal solution and derive closed-form trade-off results between the CSI feedback cost and IA performance in MIMO cellular networks. © 2014 IEEE

Cache-Enabled Opportunistic Cooperative MIMO for Video Streaming in Wireless Systems

We propose a cache-enabled opportunistic cooperative MIMO (CoMP) framework for wireless video streaming. By caching a portion of the video files at the relays (RS) using a novel MDS-coded random cache scheme, the base station (BS) and RSs opportunistically employ CoMP to achieve spatial multiplexing gain without expensive payload backhaul. We study a two timescale joint optimization of power and cache control to support real-time video streaming. The cache control is to create more CoMP opportunities and is adaptive to the long-term popularity of the video files. The power control is to guarantee the QoS requirements and is adaptive to the channel state information (CSI), the cache state at the RS and the queue state information (QSI) at the users. The joint problem is decomposed into an inner power control problem and an outer cache control problem. We first derive a closed-form power control policy from an approximated Bellman equation. Based on this, we transform the outer problem into a convex stochastic optimization problem and propose a stochastic subgradient algorithm to solve it. Finally, the proposed solution is shown to be asymptotically optimal for high SNR and small timeslot duration. Its superior performance over various baselines is verified by simulations

Large deviation delay analysis of queue-aware multi-user MIMO systems with two timescale mobile-driven feedback

Multi-user multi-input-multi-output (MU-MIMO) systems usually require users to feedback the channel state information (CSI) for scheduling. Most of the existing literature on the reduced feedback user scheduling focused on the throughput performance and the queueing delay was usually ignored. As the delay is important for real-time applications, it is desirable to have a low feedback queue-aware user scheduling algorithm for MU-MIMO systems. This paper proposes a two timescale queue-aware user scheduling algorithm, which consists of a queue-aware mobile-driven feedback filtering stage and a SINR-based user scheduling stage. The feedback policy is obtained by solving a queue-weighted optimization problem. In addition, we evaluate the associated queueing delay performance by using the large deviation analysis. The large deviation decay rate for the proposed algorithm is shown to be much larger than the CSI-only scheduling algorithm. Numerical results demonstrate the large performance gain of the proposed algorithm over the CSI-only algorithm, while the proposed one requires only a small amount of feedback. © 2013 IEEE

Hierarchical Interference Mitigation for Massive MIMO Cellular Networks

We propose a hierarchical interference mitigation scheme for massive MIMO cellular networks. The MIMO pre-coder at each base station (BS) is partitioned into an inner precoder and an outer precoder. The inner precoder controls the intra-cell interference and is adaptive to local channel state information (CSI) at each BS (CSIT). The outer precoder controls the inter-cell interference and is adaptive to channel statistics. Such hierarchical precoding structure reduces the number of pilot symbols required for CSI estimation in massive MIMO downlink and is robust to the backhaul latency. We study joint optimization of the outer precoders, the user selection, and the power allocation to maximize a general concave utility which has no closed-form expression. We first apply random matrix theory to obtain an approximated problem with closed-form objective. Then using the hidden convexity of the problem, we propose an iterative algorithm to find the optimal solution for the approximated problem. We also obtain a low complexity algorithm with provable convergence. Simulations show that the proposed design has significant gain over various state-of-the-art baselines

Mixed-timescale precoding and cache control in cached MIMO interference network

Consider media streaming in MIMO interference networks whereby multiple base stations (BS) simultaneously deliver media to their associated users using fixed data rates. The performance is fundamentally limited by the cross-link interference. We propose a cache-induced opportunistic cooperative MIMO (CoMP) for interference mitigation. By caching a portion of the media files, the BSs opportunistically employ CoMP to transform the cross-link interference into spatial multiplexing gain. We study a mixed-timescale optimization of MIMO precoding and cache control to minimize the transmit power under the rate constraint. The cache control is to create more CoMP opportunities and is adaptive to the long-term popularity of the media files. The precoding is to guarantee the rate requirement and is adaptive to the channel state information and cache state at the BSs. The joint stochastic optimization problem is decomposed into a short-term precoding and a long-term cache control problem. We propose a precoding algorithm which converges to a stationary point of the short-term problem. Based on this, we exploit the hidden convexity of the long-term problem and propose a low complexity and robust solution using stochastic subgradient. The solution has significant gains over various baselines and does not require explicit knowledge of the media popularity. © 2013 IEEE

Joint Power and Antenna Selection Optimization for Energy-Efficient Large Distributed MIMO Networks

Large multiple-input multiple-output (MIMO) network promises high energy efficiency using a large number of antennas. To reduce the signaling overhead of obtaining the full channel state information, we propose a downlink antenna selection scheme for large distributed MIMO networks with regularized zero-forcing (RZF) precoding. We study the joint optimization of antenna selection, regularization factor, and power allocation to maximize the average weighted sum-rate. The problem is non-trivial due to its combinatorial and non-convex nature. We decompose the problem into subproblems, each of which is solved by an efficient algorithm. For very large distributed MIMO networks, we obtain a capacity scaling law and show that there is an asymptotic decoupling effect, which can be exploited to simplify algorithms and physical layer processing. Simulations show that the proposed scheme achieves significant gain over the baseline