Frequency synchronization and reciprocity calibration across base stations (BSs) are crucial for coherent transmission in distributed massive multiple-input multiple-output (MIMO) networks, yet are often disrupted by carrier frequency offsets (CFOs) from distinct BS oscillators and phase offsets caused by radio frequency hardware impairments. Traditional approaches typically employ maximum likelihood or least squares estimation for CFOs and phase offsets, requiring additional estimation of inter-BS channels and resulting in high computational costs. This paper introduces an over-the-air protocol, named Sync4CT, which estimates and compensates CFOs and phase offsets to enable coherent transmission. Sync4CT estimates CFOs utilizing the autocorrelation matrices of the received pilot signals and assesses phase offsets in the frequency domain. Both procedures offer closed-form estimators without the need of inter-BS channel estimation, and are executed in parallel across BSs, thereby significantly reducing computational complexity. Theoretical analysis shows that, for Sync4CT, the mean square error (MSE) of CFO estimation approaches the Cramér-Rao bound (CRB), while the MSE of phase offset estimation achieves the CRB in narrowband systems. Simulations further validate the substantial performance improvements of Sync4CT for coherent transmission in distributed MIMO networks.

With the rapid development of artificial intelligence and the dramatic growth of communication services, the sixth-generation (6G) wireless network needs to handle communication tasks more flexibly and efficiently, significantly exacerbating the challenge of resource allocation. For the access network scenarios in 6G networks, the existing single-layer reinforcement learning resource allocation algorithms are hard to satisfy the diverse demands of users due to the complex and variable state space. Therefore, we propose a reinforcement learning-based two-timescale resource allocation scheme, aiming to jointly enhance the quality of service and system resource utilization. The proposed method comprises an upper-layer controller that allocates network resources to lower-layer controllers on a large time scale. Then, lower-layer controllers refine the resources based on user service types on a smaller time scale. To implement the proposed two-timescale allocation scheme, we propose a two-layer reinforcement learning framework consisting of a deep deterministic policy gradient (DDPG) and a dueling deep Q network (Dueling-DQN). Furthermore, recognizing that coupling multiple reinforcement learning processes may slow down algorithm convergence, we employ asynchronous training, transfer learning, and prediction-based action space simplification to expedite the model’s convergence speed. Finally, we build a prototyping network to verify the performance of the proposed small-timescale and the large-timescale allocation algorithms. Our proposed scheme demonstrates significant improvements in both resource utilization and quality of service compared to existing schemes.

The physical cell identity (PCI) is a critical parameter in wireless networks, enabling user equipment (UE) to uniquely identify cells and mitigate inter-cell interference during communication. However, the proliferation of base stations in modern networks has complicated the proper assignment of PCIs, as it requires avoiding multiple types of PCI conflicts, such as PCI collision, mod-3 collision, and confusion. Existing methods generally focus on addressing single PCI conflicts and involve reassigning PCIs for all cells, which can lead to significant data exchange overhead in large-scale networks. To address these challenges, this paper tackles the PCI planning problem by introducing a PCI reassignment budget as a constraint to minimize overall network interference. A penalty-based double-loop PCI planning algorithm is proposed, where the outer loop optimizes penalty parameters derived from the constraints, and the inner loop combines a block coordinate descent (BCD) approach with a proposed concave-convex procedure to optimize PCI assignments. Simulation results demonstrate the effectiveness of the proposed method in significantly reducing network interference compared to existing approaches.

This paper addresses the joint beamforming and data stream allocation (DSA) optimization problem for non-coherent joint transmission (NCJT). A critical yet neglected issue in NCJT beamforming is the tightly related DSA, which involves determining the number of streams transmitted from access points (APs) to their serving user equipments (UEs) according to the channel quality, so that the weighted sum-rate (WSR) can be maximized. However, since the integer number of streams directly determines the dimensions of beamformers, the joint optimization problem is mixed-integer and nonconvex with tightly coupled decision variables, making it NP-hard. To solve this problem, we first fix the DSA variables and propose a distributed and reduced weighted minimum mean square error (WMMSE) beamforming algorithm, called distributed RWMMSE, by leveraging the low-dimensional subspace property of the beamformer obtained by the traditional WMMSE. The distributed RWMMSE achieves the same WSR as the traditional WMMSE but with significantly lower computational complexity (scaling linearly with the number of AP antennas) and reduced interaction cost. Building on this, a joint beamforming and DSA optimization algorithm, named RWMMSE-LSA, is proposed by decoupling the decision variables through introduced stream indicator matrices. The RWMMSE-LSA optimizes beamformers and DSA via the distributed RWMMSE and linear programming, respectively, both of which have closed-form solutions. Simulations validate substantial performance gain of our proposed algorithms over existing alternatives in computational and interaction costs.

The rapid development of wireless communications has driven the need for careful optimization of network parameters to improve network performance and reduce operational cost. Traditional methods, however, struggle with the vast number of tunable parameters and lack scalability in diverse network scenarios. To address these challenges, this paper introduces an optimization-inspired bipartite graph neural network (Bi-GNN) approach for scalable network optimization. Our approach leverages the bipartite structure of network topologies, and incorporates a message-passing mechanism by unfolding the Zeroth-Order Block Coordinate Projected Gradient Descent (ZO-BCPGD) algorithm, which ensures not only high-performance optimization but also manageable computational demand. We demonstrate the permutation and dimensionality equivariance property of the Bi-GNN, which significantly enhances the model’s generalizability across various network structures and sizes. Furthermore, we theoretically analyze the expressive power and generalization ability of the Bi-GNN, demonstrating its adeptness at complex network optimization tasks. The training process, parallel execution, and practical implementation techniques are also discussed to ensure the model’s applicability in real-world scenarios. Numerical results verify that the Bi-GNN outperforms existing methods in both coverage ratios and computational cost. Furthermore, our approach exhibits robust scalability across various network scenarios, making it a versatile tool for optimizing a wide range of wireless networks.

Configuring intelligent surface (IS) or passive antenna array without any channel knowledge, namely blind beamforming, is a frontier research topic in the wireless communication field. Existing methods in the previous literature for blind beamforming include the RFocus and the CSM, the effectiveness of which has been demonstrated on hardware prototypes. However, this paper points out a subtle issue with these blind beamforming algorithms: the RFocus and the CSM may fail to work in the non-line-of-sight (NLoS) channel case. To address this issue, we suggest a grouping strategy that enables adaptive blind beamforming. Specifically, the reflective elements (REs) of the IS are divided into three groups; each group is configured randomly to obtain a dataset of random samples. We then extract the statistical feature of the wireless environment from the random samples, thereby coordinating phase shifts of the IS without channel acquisition. The RE grouping plays a critical role in guaranteeing performance gain in the NLoS case. In particular, if we place all the REs in the same group, the proposed algorithm would reduce to the RFocus and the CSM. We validate the advantage of the proposed blind beamforming algorithm in the real-world networks at 3.5 GHz aside from simulations.

This paper investigates group-level multi-user precoding schemes for multi-antenna multi-subcarrier systems. Existing literature often presupposes an individual precoder for each tone, uniformly applying standard narrowband precoding techniques to each subcarrier. Such solutions, however, are unfeasible in practice due to unrealistic hardware cost and computational complexity. Motivated by realistic industrial protocol that allocates one common precoder for multiple subcarriers, this paper investigates a more viable sum-rate maximization paradigm employing group-level precoding. We first introduce two innovative schemes for assessing spectral efficiency of subcarrier blocks, that takes into account the fluctuating signal-to-interference-and-noise-ratios (SINRs) across all tones. Our formulation shows that these group-level precoding strategies present highly nonconvex challenges. To address these difficulties, we adopt successive convex approximation (SCA) methodology, which resolves the original nonconvex challenge via iteratively convexifying subproblems. Moreover, we devise low-complexity methods utilizing gradient projection, obviating the necessity for numerical solvers. Numerical experiments affirm the convergence and efficacy of our proposed algorithms. Notably, our 5G link-level simulations reveal that, compared to traditional methods, group-level precoding not only ensures a more uniform distribution of SINRs across subcarriers but also significantly improves throughput performance.

In this paper, we investigate a self-sensing intelligent reflecting surface (IRS) aided millimeter wave (mmWave) integrated sensing and communication (ISAC) system. Unlike the conventional purely passive IRS, the self-sensing IRS can effectively reduce the path loss of sensing-related links, thus rendering it advantageous in ISAC systems. Aiming to jointly improve the channel estimation (CE) and target/scatterer/user sensing performance in the considered system, we propose a two-phase transmission scheme, where the coarse and refined CE/sensing results are respectively obtained in the first and second phases. Particularly, in each phase, an angle-based sensing turbo variational Bayesian inference (AS-TVBI) algorithm, which combines the VBI, messaging passing and expectation-maximization (EM) methods, is devised to solve the considered joint sensing and CE problem. The proposed algorithm incorporates the partial overlapping structured (POS) sparsity between the sensing and communication channels to improve the performance. Simulation results are provided to verify the superiority of the proposed algorithm.

In this paper, we investigate a self-sensing intelligent reflecting surface (IRS) aided millimeter wave (mmWave) integrated sensing and communication (ISAC) system. Unlike the conventional purely passive IRS, the self-sensing IRS can effectively reduce the path loss of sensing-related links, thus rendering it advantageous in ISAC systems. Aiming to jointly sense the target/scatterer/user positions as well as estimate the sensing and communication (SAC) channels in the considered system, we propose a two-phase transmission scheme, where the coarse and refined sensing/channel estimation (CE) results are respectively obtained in the first phase (using scanning-based IRS reflection coefficients) and second phase (using optimized IRS reflection coefficients). For each phase, an angle-based sensing turbo variational Bayesian inference (AS-TVBI) algorithm, which combines the VBI, messaging passing and expectation-maximization (EM) methods, is developed to solve the considered joint location sensing and CE problem. The proposed algorithm effectively exploits the partial overlapping structured (POS) sparsity and 2-dimensional (2D) block sparsity inherent in the SAC channels to enhance the overall performance. Based on the estimation results from the first phase, we formulate a Cramér-Rao bound (CRB) minimization problem for optimizing IRS reflection coefficients, and through proper reformulations, a low-complexity manifold-based optimization algorithm is proposed to solve this problem. Simulation results are provided to verify the superiority of the proposed transmission scheme and associated algorithms.

Network optimization plays a crucial role in wireless communications. However, the optimization of contemporary 5G networks is challenging due to its black-box nature and huge searching space. To address these challenges, this paper introduces efficient zeroth-order (ZO) algorithms and a parallel framework for optimizing large-scale networks. By leveraging the gradient-based searching strategy, the proposed algorithms, namely ZO projected gradient descent (ZO-PGD) and ZO block coordinate projected gradient descent (ZO-BCPGD), can significantly improve optimization quality and computational efficiency. Both algorithms guarantee a convergence towards stationary points under mild conditions, eliminating inherent errors in traditional ZO methods. We further propose a parallel framework for optimizing the network parameters in a simultaneous manner. By partitioning the entire network into manageable subnetworks, the original network optimization problem is reformulated as a consensus optimization problem and tackled in parallel using the penalty dual decomposition (PDD) method. We have also designed a tailored size-constrained grid clustering algorithm for network partitioning to ensure load balance among parallel working nodes. The efficacy of our proposed schemes is verified by extensive numerical results. Our ZO algorithms outperform existing methods in both computational efficiency and solution quality. Moreover, the parallel framework significantly reduces execution time without sacrificing network performance.

This paper investigates a novel deep learning framework for the general nonconvex quality-of-service (QoS)-constrained beamforming design problems in multi-user systems. While existing deep learning-based approaches have shown great success for various power allocation and beamforming design problems, most of the considered problems are equipped with simple constraints (e.g., power budget constraints), which can be satisfied by a simple projection operation. However, it is still a challenge to tackle the more complicated QoS constraints, in which the beamformers and the wireless channels are commonly coupled. To fill this gap, this paper proposes an augmented Lagrangian based penalty-dual training algorithm, which trains two individual neural networks for inferring the beamformers and the corresponding Lagrange multipliers alternatingly. Furthermore, we apply the proposed penalty-dual learning framework to optimize the energy-efficient unicast beamformers and the power-minimized multicast beamformers, respectively. The neural network architectures are judiciously designed based on the solution structures of the two problems. Simulation results on the two applications demonstrate that the proposed penalty-dual approach outperforms state-of-the-art learning approaches and optimization-based algorithms in terms of the constraint violation and the computational time, respectively.

Conventional policy for configuring an intelligent reflecting surface (IRS) typically requires channel state information (CSI), thus incurring substantial overhead costs and facing incompatibility with the current network protocols. This paper proposes a blind beamforming strategy in the absence of CSI, aiming to boost the minimum signal-to-noise ratio (SNR) among all the receiver positions, namely the coverage enhancement. Although some existing works already consider the IRS-assisted coverage enhancement without CSI, they assume certain position-channel models through which the channels can be recovered from the geographic locations. In contrast, our approach solely relies on the received signal power data, not assuming any position-channel model. We examine the achievability and converse of the proposed blind beamforming method. If the IRS has N reflective elements and there are U receiver positions, then our method guarantees the minimum SNR of$\Omega (N^{2}/U)$—which is fairly close to the upper bound$O(N+N^{2}\sqrt {\ln (NU)}/\sqrt [{4}]{U})$. Aside from the simulation results, we justify the practical use of blind beamforming in a field test at 2.6 GHz. According to the real-world experiment, the proposed blind beamforming method boosts the minimum SNR across seven random positions in a conference room by 18.22 dB, while the position-based method yields a boost of 12.08 dB.

This paper investigates the problem of activity detection and channel estimation in cooperative multi-cell massive access systems with temporally correlated activity, where all access points (APs) are connected to a central unit via fronthaul links. We propose to perform user-centric AP cooperation for computation burden alleviation and introduce a generalized sliding-window detection strategy for fully exploiting the temporal correlation in activity. By establishing the probabilistic model associated with the factor graph representation, we propose a scalable Dynamic Compressed Sensing-based Multiple Measurement Vector Generalized Approximate Message Passing (DCS-MMV-GAMP) algorithm from the perspective of Bayesian inference. Therein, the activity likelihood is refined by performing standard message passing among the activities in the spatial-temporal domain and GAMP is employed for efficient channel estimation. Furthermore, we develop two schemes of quantize-and-forward (QF) and detect-and-forward (DF) based on DCS-MMV-GAMP for the finite-fronthaul-capacity scenario, which are extensively evaluated under various system limits. Numerical results verify the significant superiority of the proposed approach over the benchmarks. Moreover, it is revealed that QF can usually realize superior performance when the antenna number is small, whereas DF shifts to be preferable with limited fronthaul capacity if the large-scale antenna arrays are equipped.

Neural Architecture Search (NAS) is a silver bullet in alleviating time consumption and human effort for deep neural network design. It is however challenging to search for good architectures with low consumption. In this paper, we propose a novel NAS framework to address the differentiable neural architecture search problem by inspecting the bi-level problem formulation from scratch. Combined with the Zeroth-Order (ZO) gradient descent technique and implicit gradients, the proposed algorithm can not only reduce search time for suitable architectures than existing works but maintain the final accuracy simultaneously. Experimental results show the efficacy of our proposed ZO-based NAS approach.

Channel acquisition is a major challenge faced by the conventional beamforming methods when dealing with multiple intelligent reflecting surfaces (IRSs), because the number of unknown channels grows exponentially with the number of IRSs. This work proposes to sidestep channel estimation and to configure the IRSs blindly based on the statistical information which is extracted from a set of random samples of the received signal power. The proposed blind beamforming method has provable performance in terms of the signal-to-noise ratio (SNR) boost. For instance, it yields a quartic SNR boost of$\Theta(N^{4})$for a double-IRS system under certain condition, where$N$is the number of reflected elements of each IRS. We remark that the above$\Theta(N^{4})$result is more sophisticated than the existing ones about the double-IRS system in the literature. Furthermore, we numerically demonstrate the advantage of the proposed blind beamforming method through prototype tests with multiple IRSs.

One of the main focuses in distributed learning is communication efficiency, since model aggregation at each round of training can consist of millions to billions of parameters. Several model compression methods, such as gradient quantization and sparsification, have been proposed to improve the communication efficiency of model aggregation. However, the information-theoretic minimum communication cost for a given distortion of gradient estimators is still unknown. In this paper, we study the fundamental limit of communication cost of model aggregation in distributed learning from a rate-distortion perspective. By formulating the model aggregation as a vector Gaussian CEO problem, we derive the rate region bound and sum-rate-distortion function for the model aggregation problem, which reveals the minimum communication rate at a particular gradient distortion upper bound. We also analyze the communication cost at each iteration and total communication cost based on the sum-rate-distortion function with the gradient statistics of real-world datasets. It is found that the communication gain by exploiting the correlation between worker nodes is significant for SignSGD, and a high distortion of gradient estimator can achieve low total communication cost in gradient compression.

Coded distributed computing (CDC) can alleviate the communication load in distributed computing systems by leveraging coding opportunities via redundant computation. While the optimal computation-communication tradeoff has been well studied for homogeneous systems, it remains largely unknown for heterogeneous systems where workers have different computation capabilities. This paper characterizes the upper and lower bounds of the optimal communication load as two linear programming problems for a general heterogeneous CDC system using the MapReduce framework. Our achievable scheme first designs a parametric data shuffling strategy for any given mapping strategy, and then jointly optimizes the mapping strategy and the data shuffling strategy to obtain the upper bound. The parametric data shuffling strategy allows adjusting the size of the multicast message intended for each worker set, so that it can largely decrease the number of unicast messages and hence increase the communication efficiency. Numerical results show that our achievable communication load is lower than those achieved in existing works. Our lower bound is established by unifying an improved cut-set bound and a peeling method. The obtained upper and lower bounds degenerate to the existing result in homogeneous systems, and coincide with each other when the system is approximately homogeneous or grouped homogeneous.

This paper studies caching in (K + L - 1) × K partially connected wireless linear networks, where each of the K receivers locally communicates with L out of the K +L-1 transmitters, and caches are at all nodes. The goal is to design caching and delivery schemes to reduce the transmission latency, by using normalized delivery time (NDT) as the performance metric. For small transmitter cache size (any L transmitters can collectively store the database just once), we propose a cyclic caching strategy so that each of every L consecutive transmitters caches a distinct part of each file; the delivery strategy exploits coded multicasting and interference alignment by introducing virtual receivers. The obtained NDT is within a multiplicative gap of 2 to the optimum in the entire cache size region, and optimal in certain region. For large transmitter cache size (any L transmitters can collectively store the database for multiple copies), we propose a modified caching strategy so that every bit is repeatedly cached at consecutive transmitters; the delivery strategy exploits self-interference cancellation and interference neutralization. By combining these schemes, the NDT is optimal in a larger region. We also extend our results to linear networks with heterogeneous receiver connectivity and partially connected circular networks.

This paper studies the computation-communication tradeoff in a heterogeneous MapReduce computing system where each distributed node is equipped with different computation capability. We first obtain an achievable communication load for any given computation load and any given function assignment at each node. The proposed file allocation strategy has two steps: first, the input files are partitioned into disjoint batches, each with possibly different size and computed by a distinct node; then, each node computes additional files from its non-computed files according to its redundant computation capability. In the Shuffle phase, coded multicasting opportunities are exploited thanks to the repetitive file allocation among different nodes. Based on this scheme, we further propose the computation-aware and the shuffle-aware function assignments. We prove that, by using proper function assignments, our achievable communication load for any given computation load is within a constant multiplicative gap to the optimum in an equivalent homogeneous system with the same average computation load. Numerical results show that our scheme with shuffle-aware function assignment achieves better computation- communication tradeoff than existing works in some cases.

Today's mobile data traffic is dominated by content-oriented traffic. Caching popular contents at the network edge can alleviate network congestion and reduce content delivery latency. This paper provides a comprehensive and unified study of caching and delivery techniques in wireless radio access networks (RANs) with caches at all edge nodes (ENs) and user equipments (UEs). Three cache-aided RAN architectures are considered: RANs without fronthaul, with dedicated fronthaul, and with wireless fronthaul. It first reviews in a tutorial nature how caching facilitates interference management in these networks by enabling interference cancelation (IC), zero-forcing (ZF), and interference alignment (IA). Then, two new delivery schemes are presented. One is for RANs with dedicated fronthaul, which considers centralized cache placement at the ENs but both centralized and decentralized placement at the UEs. This scheme combines IA, ZF, and IC together with soft-transfer fronthauling. The other is for RANs with wireless fronthaul, which considers decentralized cache placement at all nodes. It leverages the broadcast nature of wireless fronthaul to fetch not only uncached but also cached contents to boost transmission cooperation among the ENs. The numerical results show that both schemes outperform existing results for a wide range of system parameters, thanks to the various caching gains obtained opportunistically.

This paper aims to characterize the synergy of distributed caching and wireless fronthaul in a fog radio access network (Fog-RAN) where all edge nodes (ENs) and user equipments (UEs) have a local cache and store contents independently at random. The network operates in two phases, a file-splitting based decentralized cache placement phase and a fronthaul-aided content delivery phase. We adopt normalized delivery time (NDT) to characterize the asymptotic latency performance with respect to cache size and fronthaul capacity. Both an achievable upper bound and a theoretical lower bound of NDT are obtained, and their multiplicative gap is within 12. In the proposed delivery scheme, we utilize the fronthaul link, by exploiting coded multicasting, to fetch both non-cached and cached contents to boost EN cooperation in the access link. In particular, to fetch contents already cached at ENs, an additional layer of coded multicasting is added on the coded messages desired by UEs in the fronthaul link. Our analysis shows that the proposed delivery scheme can balance the delivery latency between the fronthaul link and access link, and is approximately optimum under decentralized caching.

Cache-aided communication is emerging as a new topic in wireless networks. Previous works have shown that caching in interference networks can change the interference topology by changing the information flow and hence facilitate advanced interference management. This paper studies the gain of caching in partially connected interference networks where each receiver can only communicate with a subset of transmitters. The performance is characterized by an information- theoretic metric, normalized delivery time (NDT). We obtain an order-optimal NDT for the (K+L-1)×K partially connected linear interference network with any number of receivers K, any receiver connectivity L≤K, and with caches equipped at all transmitters and receivers. The cache placement phase adopts a file splitting strategy tailor- made for the partial receiver connectivity. Via the aid of virtual receivers, the proposed delivery strategy exploits coded multicasting gain by XOR combining and transmitter coordination gain by interference alignment. In the special case when L is a divisor of K, our NDT results are directly applicable to K×K partially connected circular interference networks.

This paper studies the fundamental tradeoff between storage and latency in a general wireless interference network with caches equipped at all transmitters and receivers. The tradeoff is characterized by an information-theoretic metric, normalized delivery time (NDT), which is the worst case delivery time of the actual traffic load at a transmission rate specified by degrees of freedom of a given channel. We obtain both an achievable upper bound and a theoretical lower bound of the minimum NDT for any number of transmitters, any number of receivers, and any feasible cache size tuple. We show that the achievable NDT is exactly optimal in certain cache size regions, and is within a bounded multiplicative gap to the theoretical lower bound in other regions. In the achievability analysis, we first propose a novel cooperative transmitter/receiver coded caching strategy. It offers the freedom to adjust file splitting ratios for NDT minimization. We then propose a delivery strategy that transforms the considered interference network into a new class of cooperative X-multicast channels. It leverages local caching gain, coded multicasting gain, and transmitter cooperation gain (via interference alignment and interference neutralization) opportunistically. Finally, the achievable NDT is obtained by solving a linear programming problem. This paper reveals that with caching at both transmitter and receiver sides, the network can benefit simultaneously from traffic load reduction and transmission rate enhancement, thereby effectively reducing the content delivery latency.

Caching is an effective technique to improve user perceived experience for content delivery in wireless networks. Wireless caching differs from traditional web caching in that it can exploit the broadcast nature of wireless medium and hence, opportunistically change the network topologies. This paper studies a cache-aided MIMO interference network with three transmitters each equipped with M antennas and three receivers each with N antennas. With caching at both the transmitter and receiver sides, the network is changed to hybrid forms of MIMO broadcast channel, MIMO X channel, and MIMO multicast channels. We analyze the degrees of freedom (DoF) of these new channel models using practical interference management schemes. Based on the collective use of these DoF results, we then obtain an achievable normalized delivery time (NDT) of the network, an information-theoretic metric that evaluates the worst-case delivery time at given cache sizes. The obtained NDT is for arbitrary M, N, and any feasible cache sizes. It is shown to be optimal in certain cases and within a multiplicative gap of 3 from the optimum in other cases. The extension to the network with arbitrary number of transmitters and receivers is also discussed.

Caching is an effective technique to improve user perceived experience for massive content delivery in wireless networks. An essential problem in cache-aided wireless networks is to find what and how much gain can be achieved by caching. This paper provides a study of the fundamental storage-latency tradeoff for a cache-aided MIMO interference network with 3 transmitters and 3 receivers and each node equipped with antennas. By using a newly proposed novel file splitting and caching strategy, the network topology during the content delivery phase is turned opportunistically to MIMO X channel, MIMO broadcast channel, MIMO multicast channel, or a hybrid form of these channels. Linear-precoding based interference management schemes such as interference alignment and neutralization over finite symbol extension are designed for these channels. We characterize the storage-latency tradeoff by fractional delivery time (FDT), a metric to evaluate the worst-case delivery time of the actual traffic load at a rate specified by the degrees of freedom (DoF) of the considered channel. The achievable FDT of our proposed scheme decreases piecewise linearly with the normalized cache sizes and is inversely proportional to the number of antennas. It is also shown that the achievable FDT is optimal at certain cache size regions and is within a multiplicative gap of 2 from the optimum at other regions.

This paper studies the storage-latency tradeoff in the 3 × 3 wireless interference network with caches equipped at all transmitters and receivers. The tradeoff is characterized by the so-called fractional delivery time (FDT) at given normalized transmitter and receiver cache sizes. We first propose a generic cooperative transmitter/receiver caching strategy with adjustable file splitting ratios. Based on this caching strategy, we then design the delivery phase carefully to turn the considered interference channel opportunistically into broadcast channel, multicast channel, X channel, or a hybrid form of these channels. After that, we obtain an achievable upper bound of the minimum FDT by solving a linear programming problem of the file splitting ratios. The achievable FDT is a convex and piece-wise linear decreasing function of the cache sizes. Receiver local caching gain, coded multicasting gain, and transmitter cooperation gain (interference alignment and interference neutralization) are leveraged in different cache size regions.