Coded caching leverages the differences in user cache memories to achieve gains that scale with the total cache size, alleviating network congestion due to high-quality content requests. Additionally, distributing transmitters over a wide area can mitigate the adverse effects of path loss. In this work, we consider a partially connected network where the channel between distributed transmitters (helpers) and users is modeled as a distributed multiple-input-multiple-output (MIMO) Gaussian broadcast channel. We propose a novel delivery scheme consisting of two phases: partitioning and transmission. In the partitioning phase, users with identical cache profiles are partitioned into the minimum number of sets, such that users within each set can successfully decode their desired message from a joint transmission enabled by MIMO precoding. To optimally partition the users, we employ the branch and bound method. In the transmission phase, each partition is treated as a single entity, and codewords are multicast to partitions with distinct cache profiles. The proposed delivery scheme is applicable to any partially connected network, and while the partitioning is optimal, the overall delivery scheme, including transmission, is heuristic. Interestingly, simulation results show that its performance closely approximates that of the fully connected optimal solution.

Coded Caching is a technology that promises to reduce cacheable traffic by turning stored content at the users to multicast opportunities. In wireless channels, though, users experience different rates causing each message to be communicated with the group’s worst-user’s rate, which in turn impacts significantly the achieved performance. In this work we propose an adaptive quality transmission framework specifically designed for coded caching multicast communications, which uses superposition coding to overcome channel degradation. Our scheme combines coded caching, superposition coding, and scalable source coding, while keeping the caching oblivious to future channel rates and delivered file quality. The proposed framework covers all possible channel rate and quality configurations, while we further propose algorithms that can optimise the served quality. An interesting outcome of our work is that a modest quality reduction at the degraded users can counter the effects of significant channel degradation. For example, in a 100-user system with normalized cache size 1/10 at each user, if 10 users experience channel degradation of 60% compared to the rate of the non-degraded users, we show that our transmission strategy leads to a$\thicksim 85\%$quality at the degraded users and perfect quality at the non-degraded users.

This work focuses on the efficient delivery of content over the single antenna degraded broadcast channel with user caching through the adaptation of the content quality at the users. We design a delivery scheme which combines superposition coding, multicasting, and scalable video coding, while keeping the caching scheme oblivious to channel qualities. By lowering the quality at users who experience channel degradation we are able to satisfy user demands in a time efficient manner. In addition, superposition coding allows us to treat users with higher channel rates without subjecting them to a delay penalty due to their degraded counterparts' channels. An interesting outcome of this work is that a modest reduction in the quality of the degraded users can counter the effects of a significant channel degradation. For example, in a 100-user channel with normalised cache size 1/10 at each user, if 10 users experience channel degradation of 60% compared to the rate of the non-degraded users, we show that our transmission strategy leads to a ~ 85% quality at the degraded users and perfect quality at the non-degraded users.

This work presents a new way of exploiting non-uniform file popularity in coded caching networks. Focusing on a fully-connected fully-interfering wireless setting with multiple cache-enabled transmitters and receivers, we show how non-uniform file popularity can be used very efficiently to accelerate the impact of transmitter-side data redundancy on receiver-side coded caching. This approach is motivated by the recent discovery that, under any realistic file-size constraint, having content appear in multiple transmitters can in fact dramatically boost the speed-up factor attributed to coded caching. We formulate an optimization problem that exploits file popularity to optimize the placement of files at the transmitters. Consequently, we propose a search algorithm that solves the problem at hand while reducing the variable search space significantly. We also prove an analytical performance upper bound, which is in fact met by our algorithm in the regime of many receivers. Our work reflects the benefits of allocating higher cache redundancy to more popular files, but also reflects a law of diminishing returns where for example very popular files may in fact benefit from minimum redundancy. In the end, this work reveals that in the context of coded caching, employing multiple transmitters can be a catalyst in fully exploiting file popularity, as it avoids various asymmetry complications that appear when file popularity is used to alter the receiver-side cache placement.

Multi-antenna cache-aided wireless networks were thought to suffer from a severe feedback bottleneck, since achieving the maximal Degrees-of-Freedom (DoF) performance required feedback from all served users for the known transmission schemes. These feedback costs match the caching gains and thus scale with the number of users. In the context of the$L$-antenna Multiple-Input Single Output broadcast channel with$K$receivers, each having normalized cache size$\gamma $, we pair a fundamentally novel algorithm together with a new information-theoretic converse and identify the optimal tradeoff between feedback costs and DoF performance, by showing that having channel state information from only$C< L$served users implies an optimal one-shot linear DoF of$C+K\gamma $. As a side consequence of this, we also now understand that the well known DoF performance$L+K\gamma $is in fact exactly optimal. In practice, the above means that we are able to disentangle caching gains from feedback costs, thus achieving unbounded caching gains at the mere feedback cost of the multiplexing gain. This further solidifies the role of caching in boosting multi-antenna systems; caching now can provide unbounded DoF gains over multi-antenna downlink systems, at no additional feedback costs. The above results are extended to also include the corresponding multiple transmitter scenario with caches at both ends.

The work focuses on optimizing coded caching under asynchronous demands. We consider a single-stream setting where users are allowed to request content at arbitrary time-slots. Aiming to minimize the total system delay required to serve all users, i.e. from the moment of the first request to the delivery of the last bit of requested information, we design a pair of placement and delivery algorithms and show that the achievable performance is within a multiplicative factor of 2 from the optimal, under the assumption of uncoded placement, and within a multiplicative factor of 4.02 in the general placement case. Interesting characteristics of our algorithms are that i) a placement phase agnostic to the users' arrival times is adequate to provide a near-optimal delay, and ii) the proposed delivery algorithm requires low complexity and, at the same time, requires no non-causal information. Further, we show that systems are able to withstand some degree of asynchronicity without an increase in the delay compared to an equivalent synchronous setting. Finally, we highlight an interesting connection between coded caching under asynchronous demands and coded caching in wireless environments under uneven channel strengths.

We consider cache-aided wireless communication scenarios where each user requests both a file from an a-priori generated cacheable library (referred to as `content'), and an uncacheable `non-content' message generated at the start of the communication session. This scenario is easily found in real-world wireless networks, where the two types of traffic coexist and share limited radio resources. We focus our investigation on single-transmitter wireless networks with cache-aided receivers, where the wireless channel is modelled by a degraded Gaussian broadcast channel (GBC). For this setting, we study the (normalized) delay-rate trade-off, which characterizes the content delivery time and non-content communication rates that can be achieved simultaneously. We propose a scheme based on the separation principle, which isolates the coded caching problem from the physical layer transmission problem, and prove its information-theoretic order optimality up to a multiplicative factor of 2.01. A key insight emerging from our scheme is that substantial amounts of non-content traffic can be communicated while maintaining the minimum content delivery time, achieved in the absence of non-content messages; compliments of `topological holes' arising from asymmetries in wireless channel gains.

This work considers the cache-aided multiple-input single-output broadcast channel (MISO BC) where an L-antenna transmitter serves K receiving users, each assisted by one of ΛKγ, all existing coded caching schemes suffer substantially reduced caching or multiplexing gains. Our work provides a novel coded caching scheme that achieves the exact best known, near optimal, DoF L+Kγ, and does so even if L > Kγ, thus covering an important hole in identifying the optimal performance for the multi-antenna shared-cache problem. Therefore, our work reveals that shared-cache systems with many transmit antennas can also enjoy both full multiplexing gains (L) as well as full caching gains (Kγ) despite the sharing of the caches. A side benefit of this scheme is its applicability in multi-antenna settings with dedicated users caches, where it can offer the advantage of reducing the subpacketization without sacrificing the DoF performance.

Within the context of coded caching, the work reveals the interesting connection between having multiple transmitters and having heterogeneity in the cache sizes of the receivers. Our work effectively shows that having multiple transmit antennas - while providing full multiplexing gains - can also simultaneously completely remove the performance penalties that are typically associated to cache-size unevenness. Focusing on the multiple-input single-output Broadcast Channel, the work first identifies the performance limits of the extreme case where cache-aided users coincide with users that do not have caches, and then expands the analysis to the case where both user groups are cache-aided but with heterogeneous cache-sizes. In the first case, the main contribution is a new algorithm that employs perfect matchings on a bipartite graph to offer full multiplexing as well as full coded-caching gains to both cache-aided as well as cache-less users. An interesting conclusion is that, starting from a single-stream centralized coded caching setting with normalized cache size -y, then adding L antennas allows for the addition of up to approximately L/γ extra cache-less users, at no added delay costs. Similarly surprising is the finding that, beginning with a single-antenna hybrid system (with both cache-less and cache-aided users), then adding L - 1 antennas to the transmitter, as well as endowing the cache-less users with a cumulative normalized cache size Γ2, increases the Degrees of Freedom by a multiplicative factor of up to Γ2+ L.

In this work, we study a large linear interference network with an equal number of transmitters and receivers, where each transmitter is connected to two subsequent receivers. Each transmitter has individual access to a backhaul link (fetching the equivalent of MTfiles), while each receiver can cache a fraction γ of the library. We explore the tradeoff between the communication rate, backhaul load, and caching storage by designing algorithms that can harness the benefits of cooperative transmission in partially connected networks, while exploiting the advantages of multicast transmissions attributed to user caching. We show that receiver caching and fetching content from the backhaul are two resources that can simultaneously increase the delivery performance in synergistic ways. Specifically, an interesting outcome of this work is that user caching of a fraction γ of the library can increase the per-user Degrees of Freedom (puDoF) by γ. Further, the results reveal significant savings in the backhaul load, even in the small cache size region. For example, the puDoF achieved using the pair (MT= 8,γ = 0) can also be achieved with the pairs (MT= 4,γ = 0.035) and (MT= 2,γ = 0.1), showing that small caches can provide significant savings in the backhaul load.

The work considers a Coded MapReduce setting where computing nodes of different processing capabilities coexist. Motivated by scenarios where the mapping phase is performed by nodes of heterogeneous computing capabilities, we explore the setting with K1nodes that can each map a fraction γ1∈ [1/K,1] of the dataset, and K2nodes that can each map a smaller fraction γ21. For the standard wireless (single-antenna) device-to-device channel or its equivalent wired network with network-coding capabilities at the nodes, we propose a solution of assigning data to the nodes and a method of communicating intermediate values during the shuffling phase, that can be applied to any MapReduce problem and which entirely removes the affects of heterogeneity. The surprising outcome of this work is that the shuffling-phase delay is reduced by a factor of K1γ1+K2γ2, matching the performance of the corresponding homogeneous setting, thus revealing for the first time that heterogeneity during the mapping phase does not inherently deteriorate the overall performance.

In the context of coded caching in the K-user BC, our work reveals the surprising fact that having multiple (L) transmitting antennas, dramatically ameliorates the longstanding subpacketization bottleneck of coded caching by reducing the required subpacketization to approximately its Lth root, thus boosting the actual DoF by a multiplicative factor of up to L. In asymptotic terms, this reveals that as long as L scales with the theoretical caching gain, then the full cumulative (multiplexing + full caching) gains are achieved with constant subpacketization. This is the first time, in any known setting, that unbounded caching gains appear under finite file-size constraints. The achieved caching gains here are up to L times higher than any caching gains previously experienced in any single- or multiantenna fully-connected setting, thus offering a multiplicative mitigation to a subpacketization problem that was previously known to hard-bound caching gains to small constants. The proposed scheme is practical and it works for all values of K, L and all cache sizes. The scheme's gains show in practice: e.g. for K=100, when L=1 the theoretical caching gain of G=10, under the original coded caching algorithm, would have needed subpacketization , while if extra transmitting antennas were added, the subpacketization was previously known to match or exceed S1. Now for L=5, our scheme offers the theoretical (unconstrained) cumulative DoF dI = L+G = 5 +10 = 15, with subpacketization SL=\binomK/LG/L=\binom100/510/5=190. The scheme's performance, given, subpacketization sL=\binomK/LG/L, is within a factor of 2 from the optimal linear sum-DoF. The gains stemming from this work come by a virtual decomposition of the fully connected cache-aided channel into parallel ones, which significantly reduces the required subpacketization

In the context of the K-user MISO broadcast channel with cache-aided receivers, recent multi-antenna coded-caching techniques have sought to complement the traditional multiplexing gains associated to multiple (L) antennas, with the (potentially unbounded) caching gains (G) associated to coded caching. To date, all known existing efforts to combine the two gains, either resulted in a maximum known DoF L + G that required though CSIT on all (L + G) users served at a time (i.e., that induced potentially unbounded CSIT costs that matched the DoF gains), or resulted in a much compromised DoF where multiplexing gains came at the expense of bounded or vanishing caching gains. We present here a new multi-antenna coded caching algorithm that introduces a new XOR generation structure which completely untangles caching gains from CSIT, delivering the desired sum-DoF of L + G but with a much reduced CSIT cost of only L channel vectors at a time (L × L CSIT matrix). This means that for the first time in multi-antenna coded caching, one can achieve full multiplexing gains and unbounded caching gains, at the mere CSIT cost associated to achieving the multiplexing gains. In the end, the result solidifies the role of coded caching as a method for reducing feedback requirements in multi-antenna environments.

This work explores a distributed computing setting where K nodes are assigned fractions (subtasks) of a computational task in order to perform the computation in parallel. In this setting, a well-known main bottleneck has been the internode communication cost required to parallelize the task, because unlike the computational cost which could keep decreasing as K increases, the communication cost remains approximately constant, thus bounding the total speedup gains associated to having more computing nodes. This bottleneck was substantially ameliorated by the recent introduction of coded techniques in the context of MapReduce which allowed each node - at the computational cost of having to preprocess approximately t times more subtasks - to reduce its communication cost by approximately t times. In reality though, the associated speed up gains were severely limited by the requirement that larger t and K necessited that the original task be divided into an extremely large number of subtasks. In this work we show how node cooperation, along with a novel assignment of tasks, can help to dramatically ameliorate this limitation. The result applies to wired as well as wireless distributed computing and it is based on the idea of having groups of nodes compute identical mapping tasks and then employing a here-proposed novel D2D coded caching algorithm. In this context, the new approach here manages to achieve a virtual decomposition of the fully connected D2D setting into parallel ones, which significantly reduces the required subpacketization.

The work identifies the performance limits of the multiple-input single-output broadcast channel where cache-aided users coincide with users that do not have caches. The main contribution is a new algorithm that employs perfect matchings on a bipartite graph to offer full multiplexing as well as full coded-caching gains to both cache-aided as well as cache-less users. This performance is shown to be within a factor of at most 3 from the optimal, under the assumption of linear one-shot schemes. An interesting outcome is the following: starting from a single-stream centralized coded caching setting with normalized cache size γ, then every addition of an extra transmit antenna allows for the addition of approximately 1/γ extra cache-less users, at no added delay costs. For example, starting from a single-stream coded caching setting with γ = 1/100, every addition of a transmit antenna, allows for serving approximately an additional 100 more cache-less users, without any increase in the overall delay. Finally the work reveals the role of multiple antennas in removing the penalties typically associated to cache-size unevenness, as it shows that the performance in the presence of both cache-less and cache-aided users, matches the optimal (under uncoded cache placement) performance of the corresponding symmetric case where the same cumulative cache volume is split evenly across all users.

In the context of coded caching in the K-user broadcast channel, our work reveals the surprising fact that having multiple (L) transmitting antennas, dramatically ameliorates the long-standing subpacketization bottleneck of coded caching by reducing the required subpacketization to approximately its Lth root, thus boosting the actual DoF by a multiplicative factor of up to L. In asymptotic terms, this reveals that as long as L scales with the theoretical caching gain, then the full cumulative (multiplexing + full caching) gains are achieved with constant subpacketization. This is the first time, in any known setting, that unbounded caching gains appear under finite file-size constraints. The achieved caching gains here are up to L times higher than any caching gains previously experienced in any single- or multi-antenna fully connected setting, thus offering a multiplicative mitigation to a subpacketization problem that was previously known to hard-bound caching gains to small constants. The proposed scheme manages for the first time to virtually decompose the fully connected cache-aided channel into L parallel channels. The scheme is practical; it works for all the values of K and L and all cache sizes, and its gains show in practice: e.g., for K = 100, when L = 1 the theoretical caching gain of G = 10, under the original coded caching algorithm, would have needed subpacketization S1= (K;G) = (100;10) > 1013, while if extra transmitting antennas were added, the subpacketization was previously known to match or exceed S1. Now for L = 5, our scheme offers the theoretical (unconstrained) cumulative DoF dL= L + G = 5 + 10 = 15, with subpacketization SL= (K/L;G/L) = (100/5;10/5) = 190. The work extends to the multi-server and cache-aided IC settings, while the scheme's performance, given subpacketization SL= (K/L;G/L), is within a factor of 2 from the optimal linear sum-DoF.

This work explores cache-aided interference management in the absence of channel state information at the transmitters (CSIT), focusing on the setting with K transmitter/receiver pairs endowed with caches, where each receiver k is connected to transmitter k via a direct link with normalized capacity 1, and to any other transmitter via a cross link with normalized capacity τ ≤ 1. In this setting, we explore how a combination of pre-caching at transmitters and receivers, together with interference enhancement techniques, can a) partially counter the lack of CSIT, and b) render the network self-sufficient, in the sense that the transmitters need not receive additional data after pre-caching. Toward this we present new schemes that blindly harness topology and transmitter-and-receiver caching, to create separate streams, each serving many receivers at a time. Key to the approach here is a combination of rate-splitting, interference enhancement and coded caching.