Millimeter wave (mmWave) signals experience orders-of-magnitude more pathloss than the microwave signals currently used in most wireless applications and all cellular systems. MmWave systems must therefore leverage large antenna arrays, made possible by the decrease in wavelength, to combat pathloss with beamforming gain. Beamforming with multiple data streams, known as precoding, can be used to further improve mmWave spectral efficiency. Both beamforming and precoding are done digitally at baseband in traditional multi-antenna systems. The high cost and power consumption of mixed-signal devices in mmWave systems, however, make analog processing in the RF domain more attractive. This hardware limitation restricts the feasible set of precoders and combiners that can be applied by practical mmWave transceivers. In this paper, we consider transmit precoding and receiver combining in mmWave systems with large antenna arrays. We exploit the spatial structure of mmWave channels to formulate the precoding/combining problem as a sparse reconstruction problem. Using the principle of basis pursuit, we develop algorithms that accurately approximate optimal unconstrained precoders and combiners such that they can be implemented in low-cost RF hardware. We present numerical results on the performance of the proposed algorithms and show that they allow mmWave systems to approach their unconstrained performance limits, even when transceiver hardware constraints are considered.

Spatially Sparse Precoding in Millimeter Wave MIMO Systems

Driven by the visions of Internet of Things and 5G communications, recent years have seen a paradigm shift in mobile computing, from the centralized mobile cloud computing toward mobile edge computing (MEC). The main feature of MEC is to push mobile computing, network control and storage to the network edges (e.g., base stations and access points) so as to enable computation-intensive and latency-critical applications at the resource-limited mobile devices. MEC promises dramatic reduction in latency and mobile energy consumption, tackling the key challenges for materializing 5G vision. The promised gains of MEC have motivated extensive efforts in both academia and industry on developing the technology. A main thrust of MEC research is to seamlessly merge the two disciplines of wireless communications and mobile computing, resulting in a wide-range of new designs ranging from techniques for computation offloading to network architectures. This paper provides a comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management. We also discuss a set of issues, challenges, and future research directions for MEC research, including MEC system deployment, cache-enabled MEC, mobility management for MEC, green MEC, as well as privacy-aware MEC. Advancements in these directions will facilitate the transformation of MEC from theory to practice. Finally, we introduce recent standardization efforts on MEC as well as some typical MEC application scenarios.

A Survey on Mobile Edge Computing: The Communication Perspective

Millimeter-wave (mmW) frequencies between 30 and 300 GHz are a new frontier for cellular communication that offers the promise of orders of magnitude greater bandwidths combined with further gains via beamforming and spatial multiplexing from multielement antenna arrays. This paper surveys measurements and capacity studies to assess this technology with a focus on small cell deployments in urban environments. The conclusions are extremely encouraging; measurements in New York City at 28 and 73 GHz demonstrate that, even in an urban canyon environment, significant non-line-of-sight (NLOS) outdoor, street-level coverage is possible up to approximately 200 m from a potential low-power microcell or picocell base station. In addition, based on statistical channel models from these measurements, it is shown that mmW systems can offer more than an order of magnitude increase in capacity over current state-of-the-art 4G cellular networks at current cell densities. Cellular systems, however, will need to be significantly redesigned to fully achieve these gains. Specifically, the requirement of highly directional and adaptive transmissions, directional isolation between links, and significant possibilities of outage have strong implications on multiple access, channel structure, synchronization, and receiver design. To address these challenges, the paper discusses how various technologies including adaptive beamforming, multihop relaying, heterogeneous network architectures, and carrier aggregation can be leveraged in the mmW context.

Millimeter-Wave Cellular Wireless Networks: Potentials and Challenges

Driven by the visions of Internet of Things and 5G communications, recent years have seen a paradigm shift in mobile computing, from the centralized Mobile Cloud Computing towards Mobile Edge Computing (MEC). The main feature of MEC is to push mobile computing, network control and storage to the network edges (e.g., base stations and access points) so as to enable computation-intensive and latency-critical applications at the resource-limited mobile devices. MEC promises dramatic reduction in latency and mobile energy consumption, tackling the key challenges for materializing 5G vision. The promised gains of MEC have motivated extensive efforts in both academia and industry on developing the technology. A main thrust of MEC research is to seamlessly merge the two disciplines of wireless communications and mobile computing, resulting in a wide-range of new designs ranging from techniques for computation offloading to network architectures. This paper provides a comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management. We also present a research outlook consisting of a set of promising directions for MEC research, including MEC system deployment, cache-enabled MEC, mobility management for MEC, green MEC, as well as privacy-aware MEC. Advancements in these directions will facilitate the transformation of MEC from theory to practice. Finally, we introduce recent standardization efforts on MEC as well as some typical MEC application scenarios.

This article explores network densification as the key mechanism for wireless evolution over the next decade. Network densification includes densification over space (e.g, dense deployment of small cells) and frequency (utilizing larger portions of radio spectrum in diverse bands). Large-scale cost-effective spatial densification is facilitated by self-organizing networks and intercell interference management. Full benefits of network densification can be realized only if it is complemented by backhaul densification, and advanced receivers capable of interference cancellation.

Network densification: the dominant theme for wireless evolution into 5G

Decision metrics used to decode wireless communication payloads are combined for successive frames to improve decoding of the later received frames. A bitwise payload difference between successive frames is encoded in the same manner the payloads are encoded. Decision metrics determined for the earlier received frame are combined with the encoded payload difference to generate adjusted decision metrics. The adjusted decision metrics are combined with decision metrics determined for the later received frame. The combined decision metrics are decoded to generate a payload for the later received frame. If the decoding is not successful the combined decision metrics are carried forward and the process is repeated based on the payload difference between the following frames.

Combining decision metrics for decoding based on payload difference

Methods, systems, apparatuses, and devices are described for wireless communication. In one example, a sequence may be determined based on at least one of: an operator identifier associated with an operator using a spectrum or a clear channel assessment (CCA) slot index associated with the operator using the spectrum. At least one channel based on the determined sequence may be used to communicate over the spectrum.

Sequence generation for shared spectrum

Methods, systems, and devices are described for scheduling transmissions for multiple subframes in a single scheduling operation. Scheduling information is provided in a multi-subframe scheduling information transmission for a set of subframes. Differences in characteristics for subframes under the multi-subframe information are determined, and one or more properties for communication during the one or more subframes may be adjusted based on subframe characteristics. Such multi-subframe scheduling may allow for reduced overhead for scheduling uplink or downlink transmissions.

Methods and apparatus for multi-subframe scheduling

Certain aspects of the present disclosure relate to techniques that may help address issues in wireless communications systems that utilize unlicensed radio frequency spectrum bands. For example, the techniques presented herein may be used in systems where frames transmitted in licensed and/or un-licensed component carriers are not synchronous.

Techniques for enabling asynchronous communications using unlicensed radio frequency spectrum

Systems and methodologies are described that effectuate or facilitate detecting a cell (serving or neighboring cell) in multichannel wireless communication environments. In accordance with various aspects set forth herein, systems and/or methods are provided that receive signals from multiple cells and identify candidate cells based at least in part on the received signals, compare signal metrics for each of the candidate cells, select signal metrics associated with each of the candidate cells, and compare signal metrics to identify proximate base stations located within a candidate cell.

Tao Luo

Papers

Combining decision metrics for decoding based on payload difference

Sequence generation for shared spectrum

Methods and apparatus for multi-subframe scheduling

Techniques for enabling asynchronous communications using unlicensed radio frequency spectrum

Neighboring cell search for mobile communication systems