Avik Sengupta

5G massive MIMO deployments up to 64T64R (64 Transmit and 64 Receive) antenna arrays are gaining traction, in part due to the smaller size of such arrays suitable for mid-band spectrum (2–5 GHz). A scheduling algorithm that can aggressively pair many UEs (User Equipment) on the same time-frequency resources is essential to fully derive the benefits of such an array. In this paper, an uplink scheduler design is proposed based on RME (Recursive Maximal Expansion) that ensures contiguous resource allocation and power constraint for each UE, while maximizing MU (Multi-User) MIMO (Multiple-Input Multiple-Output) gains by selecting and pairing UE groups based on the sum-PF (Proportional Fair) metric. We show, using realistic system level simulations, that up to 100% average cell-throughput gains can be achieved by MU-MIMO over SU (Single-User) MIMO in dense urban micro cell deployment scenarios presenting an attractive proposition for massive MIMO array deployments. It is observed that power control parameters play a critical role and should be optimized considering scheduler behavior, especially for cell-edge throughput improvement.

Fog radio access network (F-RAN) architectures can leverage both cloud processing and edge caching for content delivery to the users. To this end, F-RAN utilizes caches at the edge nodes (ENs) and fronthaul links connecting a cloud processor to ENs. Assuming time-invariant content popularity, existing information-theoretic analyses of content delivery in F-RANs rely on offline caching with separate content placement and delivery phases. In contrast, this paper focuses on the scenario in which the set of popular content is time-varying, hence necessitating the online replenishment of the ENs' caches along with the delivery of the requested files. The analysis is centered on the characterization of the long-term normalized delivery time (NDT), which captures the temporal dependence of the coding latencies accrued across multiple time slots in the high signal-to-noise ratio regime. Online edge caching and delivery schemes are investigated for both serial and pipelined transmission modes across fronthaul and edge segments. Analytical results demonstrate that, in the presence of a time-varying content popularity, the rate of fronthaul links sets a fundamental limit on the long-term NDT of F-RAN system. Analytical results are further verified by numerical simulation, yielding important design insights.

Caching at the network edge has emerged as a viable solution for alleviating the severe capacity crunch in modern content centric wireless networks by leveraging network load-balancing in the form of localized content storage and delivery. In this paper, we consider a cache-aided network, where the cache storage phase is assisted by a central server and users can demand multiple files at each transmission interval. To service these demands, we consider two delivery models: (1) centralized content delivery, where user demands at each transmission interval are serviced by the central server via multicast transmissions; and (2) device-to-device assisted distributed delivery, where users multicast to each other in order to service file demands. For such cache-aided networks, we present new results on the fundamental cache storage versus transmission rate tradeoff. Specifically, we develop a new technique for characterizing information theoretic lower bounds on the storage-rate tradeoff and show that the new lower bounds are strictly tighter than cut-set bounds from literature. Furthermore, using the new lower bounds, we improve the constant factor approximation of the optimal storage-rate tradeoff for cache-aided systems under both delivery models.

In a Fog Radio Access Network (F-RAN), content delivery is carried out using both edge caching and cloud processing. A key design question for F-RANs hence concerns the optimal use of edge and cloud resources. In this work, this problem is addressed from an information theoretic viewpoint by investigating the fundamental limits of the normalized delivery time (NDT) metric, which captures the high signal- to-noise ratio (SNR) worst-case latency for delivering any requested content to the users. Specifically, unlike prior work, the NDT perfor- mance of an F-RAN is studied under pipelined fronthaul-edge transmission, whereby edge nodes are capable of simultaneously receiving fronthaul messages from the cloud on fronthaul links while transmitting to the mobile users over the wireless edge channel. Lower and upper bounds on the NDT are derived that yield insights into the trade-off between cache storage capacity, fronthaul capacity and delivery latency and on the impact of fronthaul-edge pipelining.

Conventional spectrum sharing approaches assume that an unlicensed user accessing a licensed spectrum band autonomously operates by sensing (or detecting) the presence of the primary user, which is licensed to use the given band, with minimal coordination from the primary user. However, in this case, incorrect sensing decisions and resource allocation on the unlicensed secondary users' (SU) part can lead to undesired degradation of the primary users' performance. Therefore, there has been a recent paradigm shift to enable cooperative spectrum sharing, in which interaction is allowed between the primary and the secondary network for efficient spectrum utilization while limiting the interference experienced by a primary user. In this paper, we consider a cooperative spectrum sharing architecture in which the SU is assumed to be a cellular network. We then propose a novel fractional-frequency-reuse-based dynamic resource allocation (DRA) algorithm at the secondary (cellular) network. The joint DRA optimization involves simultaneous spectrum allocation across different base stations and then across users within a base station. It is an NP-hard problem and is prohibitively complex to solve for a large cellular network. Therefore, to reduce the complexity of the joint problem and offer a tractable solution, we formulate layered scheduling algorithms for cross-layer resource allocation. Furthermore, the feedback overhead between the layers is kept minimal. Finally, using simulations, we compare our proposed DRA scheme with other resource allocation schemes for sum system throughput, as well as for fairness in allocation. We also present the result for the case when the available primary spectrum varies over time and locations of SUs.

In this paper, we propose a novel design of concatenated Space-Time Block Codes (STBCs) using Systematic Redundant Residue Number System (RRNS) codes. The link between residues and complex signal constellations is optimized by incorporating the "Direct Mapping", "Prior probability based Distance Aware Mapping" and "Indirect Mapping" schemes. We analytically quantify the upper bounds on the codeword and bit error probabilities of Systematic RRNS-STBC and characterize the achievable coding and diversity gains assuming maximum likelihood decoding (MLD). We propose an Adaptive M-ary Demapping scheme for ML decoding, which exploits the RRNS code structure to decrease complexity and provide further performance improvement over the Distance Aware Mapping scheme. It is shown that Indirect Mapping, when used for RRNS coding schemes can offer more efficient transmission and performance than naive M-ary mapping. It is also seen that Systematic RRNS-STBC codes provide superior performance compared to Non-systematic RRNS-STBC, for the same code parameters, owing to more efficient binary to residue mapping.