Communications of the ACM (CACM for short, not the best sounding acronym around) is the ACM’s flagship magazine. Started in 1957, CACM is handy for keeping up to date on current research being carried out across all topics of computer science and realworld applications. CACM has had an illustrious past with many influential pieces of work and debates started within its pages. These include Hoare’s presentation of the Quicksort algorithm; Rivest, Shamir and Adleman’s description of the first publickey cryptosystem RSA; and Dijkstra’s famous letter against the use of GOTO. In addition to the print edition, which is released monthly, there is a fantastic website (http://cacm.acm. org/) that showcases not only the most recent edition but all previous CACM articles as well, readable online as well as downloadable as a PDF. In addition, the website lets you browse for articles by subject, a handy feature if you want to focus on a particular topic. CACM is really essential reading. Pretty much guaranteed to contain content that is interesting to anyone, it keeps tabs on the latest in computer science. It is a valuable asset for us students, who tend to delve deep into a particular area of CS and forget everything that is happening around us. — Daniel Gooch U ndergraduate research is like a box of chocolates: You never know what kind of project you will get. That being said, there are still a few things you should know to get the most out of the experience.

Communications of the ACM

Radio access technologies for cellular mobile communications are typically characterized by multiple access schemes, e.g., frequency division multiple access (FDMA), time division multiple access (TDMA), code division multiple access (CDMA), and OFDMA. In the 4th generation (4G) mobile communication systems such as Long-Term Evolution (LTE) (Au et al., Uplink contention based SCMA for 5G radio access. Globecom Workshops (GC Wkshps), 2014. doi:10.1109/GLOCOMW.2014.7063547) and LTE-Advanced (Baracca et al., IEEE Trans. Commun., 2011. doi:10.1109/TCOMM.2011.121410.090252; Barry et al., Digital Communication, Kluwer, Dordrecht, 2004), standardized by the 3rd Generation Partnership Project (3GPP), orthogonal multiple access based on OFDMA or single carrier (SC)-FDMA is adopted. Orthogonal multiple access was a reasonable choice for achieving good system-level throughput performance with simple single-user detection. However, considering the trend in 5G, achieving significant gains in capacity and system throughput performance is a high priority requirement in view of the recent exponential increase in the volume of mobile traffic. In addition the proposed system should be able to support enhanced delay-sensitive high-volume services such as video streaming and cloud computing. Another high-level target of 5G is reduced cost, higher energy efficiency and robustness against emergencies.

Non-Orthogonal Multiple Access (NOMA) for cellular future radio access

The spatial features of emitted wireless signals are the basis of location distinction and determination for wireless indoor localization. Available in mainstream wireless signal measurements, the Received Signal Strength Indicator (RSSI) has been adopted in vast indoor localization systems. However, it suffers from dramatic performance degradation in complex situations due to multipath fading and temporal dynamics. Break-through techniques resort to finer-grained wireless channel measurement than RSSI. Different from RSSI, the PHY layer power feature, channel response, is able to discriminate multipath characteristics, and thus holds the potential for the convergence of accurate and pervasive indoor localization. Channel State Information (CSI, reflecting channel response in 802.11 a/g/n) has attracted many research efforts and some pioneer works have demonstrated submeter or even centimeter-level accuracy. In this article, we survey this new trend of channel response in localization. The differences between CSI and RSSI are highlighted with respect to network layering, time resolution, frequency resolution, stability, and accessibility. Furthermore, we investigate a large body of recent works and classify them overall into three categories according to how to use CSI. For each category, we emphasize the basic principles and address future directions of research in this new and largely open area.

From RSSI to CSI: Indoor Localization via Channel Response, A survey on indoor localization using PHY-layer information

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The ambitious high data-rate applications in the envisioned future beyond fifth-generation (B5G) wireless networks require new solutions, including the advent of more advanced architectures than the ones already used in 5G networks, and the coalition of different communications schemes and technologies to enable these applications requirements. Among the candidate communications schemes for future wireless networks are non-orthogonal multiple access (NOMA) schemes that allow serving more than one user in the same resource block by multiplexing users in other domains than frequency or time. In this way, NOMA schemes tend to offer several advantages over orthogonal multiple access (OMA) schemes such as improved user fairness and spectral efficiency, higher cell-edge throughput, massive connectivity support, and low transmission latency. With these merits, NOMA-enabled transmission schemes are being increasingly looked at as promising multiple access schemes for future wireless networks. When the power domain is used to multiplex the users, it is referred to as the power domain NOMA (PD-NOMA). In this paper, we survey the integration of PD-NOMA with the enabling communications schemes and technologies that are expected to meet the various requirements of B5G networks. In particular, this paper surveys the different rate optimization scenarios studied in the literature when PD-NOMA is combined with one or more of the candidate schemes and technologies for B5G networks including multiple-input-single-output (MISO), multiple-input-multiple-output (MIMO), massive-MIMO (mMIMO), advanced antenna architectures, higher frequency millimeter-wave (mmWave) and terahertz (THz) communications, advanced coordinated multi-point (CoMP) transmission and reception schemes, cooperative communications, cognitive radio (CR), visible light communications (VLC), unmanned aerial vehicle (UAV) assisted communications and others. The considered system models, the optimization methods utilized to maximize the achievable rates, and the main lessons learnt on the optimization and the performance of these NOMA-enabled schemes and technologies are discussed in detail along with the future research directions for these combined schemes. Moreover, the role of machine learning in optimizing these NOMA-enabled technologies is addressed.

A Survey of Rate-Optimal Power Domain NOMA With Enabling Technologies of Future Wireless Networks

To overcome the difficulties of charging the wireless sensors in the wild with conventional energy supply, more and more researchers have focused on the sensor networks with renewable generations. Considering the uncertainty of the renewable generations, an effective energy management strategy is necessary for the sensors. In this paper, we propose a novel energy management algorithm based on the reinforcement learning. By utilizing deep deterministic policy gradient (DDPG), the proposed algorithm is applicable for the continuous states and realizes the continuous energy management. We also propose a state normalization algorithm to help the neural network initialize and learn. With only one day’s real solar data and the simulative channel data for training, the proposed algorithm shows excellent performance in the validation with about 800 days length of real solar data. Compared with the state-of-the-art algorithms, the proposed algorithm achieves better performance in terms of long-term average net bit rate.

Deep Deterministic Policy Gradient (DDPG)-Based Energy Harvesting Wireless Communications

Non-orthogonal multiple access (NOMA) has been considered as a significant candidate technique for the next generation wireless communication to support high throughput and massive connectivity. It allows different users to be multiplexed on one channel through applying superposition coding at the transmitter and successive interference cancellation (SIC) at the receiver. To fully utilize the benefit of the NOMA technique, the key problem is how to optimally allocate resources, such as power and channels, to users to maximize the system performance. There have been some existing works on the power allocation for the single-carrier NOMA system. However, how to optimally assign channels in the multi-carrier NOMA system is still unclear. In this paper, we propose a deep reinforcement learning framework to allocate resources to users in a near optimal way. Specifically, we exploit an attention-based neural network (ANN) to perform the channel assignment. Simulation results show that the proposed framework can achieve better system performance, compared with the state-of-the-art approaches.

Joint Power Allocation and Channel Assignment for NOMA With Deep Reinforcement Learning

In this paper, we propose a contact-free breath tracking system, BreathTrack, to track the status of breath using the off-the-shelf WiFi devices. BreathTrack exploits the phase variation of the channel state information (CSI) to track human breath. To resolve the phase distortions introduced by the hardware imperfection of the commodity WiFi chips, BreathTrack utilizes both the hardware and software correction methods. The time-invariant PLL phase offset is calibrated by the hardware correction using cables and splitters, while the time-varying carrier frequency offset, sampling frequency offset and packet detection delay are removed by the software corrections using the phase difference between the CSI at the receiver antennas and that at the reference antenna connected from the transmitter. Moreover, BreathTrack utilizes the sparse recovery method to find the dominant path in the multipath indoor environment and derive the corresponding complex attenuation coefficient. Then, the phase variation of the complex attenuation coefficient is utilized to extract the detailed breath status and the breath rate. Extensive experiments are conducted to show that BreathTrack could estimate the breath rate with the median accuracy of over 99% in most scenarios, and could track the detailed status of breath directly using the raw phase variation.

BreathTrack: Tracking Indoor Human Breath Status via Commodity WiFi

Error concealment techniques are very important for video communication since compressed video sequences may be corrupted or lost when transmitted over error-prone networks. In this paper, we propose a novel two-stage error concealment scheme for erroneously received video sequences. In the first stage, we propose a novel spatio-temporal boundary matching algorithm (STBMA) to reconstruct the lost motion vectors (MV). A well defined cost function is introduced which exploits both spatial and temporal smoothness properties of video signals. By minimizing the cost function, the MV of each lost macroblock (MB) is recovered and the corresponding reference MB in the reference frame is obtained using this MV. In the second stage, instead of directly copying the reference MB as the final recovered pixel values, we use a novel partial differential equation (PDE) based algorithm to refine the reconstruction. We minimize, in a weighted manner, the difference between the gradient field of the reconstructed MB in current frame and that of the reference MB in the reference frame under given boundary condition. A weighting factor is used to control the regulation level according to the local blockiness degree. With this algorithm, the annoying blocking artifacts are effectively reduced while the structures of the reference MB are well preserved. Compared with the error concealment feature implemented in the H.264 reference software, our algorithm is able to achieve significantly higher PSNR as well as better visual quality.

Video Error Concealment Using Spatio-Temporal Boundary Matching and Partial Differential Equation

Indoor human sensing, recognition, and detection, as key enablers of building smart environments, such as smart home, smart retail, and smart museum, have gained tremendous attention in recent years. Compared with traditional vision-based and wearable sensor-based solutions, radio-frequency (RF)-based approaches are more desirable with the contactless and nonline-of-sight nature. Among all RF-based approaches, WiFi-based approaches have been the focus of many researchers because of the ubiquitous availability and cost efficiency. In this article, we present a survey of recent advances in WiFi vision problems, i.e., sensing, recognition, and detection by utilizing the channel state information (CSI) of the commodity WiFi devices. We focus on nine key applications of smart environments, including WiFi imaging, vital sign monitoring, human identification, gesture recognition, gait recognition, daily activity recognition, fall detection, human detection, and indoor positioning. Such a survey can help readers have an overall understanding of sensing, recognition, and detection with commodity WiFi, and thus expedite the development of smart environments.

Yang Hu

Papers

Deep Deterministic Policy Gradient (DDPG)-Based Energy Harvesting Wireless Communications

Joint Power Allocation and Channel Assignment for NOMA With Deep Reinforcement Learning

BreathTrack: Tracking Indoor Human Breath Status via Commodity WiFi

Video Error Concealment Using Spatio-Temporal Boundary Matching and Partial Differential Equation

WiFi Vision: Sensing, Recognition, and Detection With Commodity MIMO-OFDM WiFi