Ambient radio frequency (RF) energy harvesting (EH) technology is key to realize self-sustainable, always-on, low-power, massive Internet of Things networks. Typically, rigid (non-adaptable to channel fluctuations) multi-antenna receive architectures
are proposed to support reliable EH operation. Herein, we introduce a dynamic RF combining architecture for ambient RF EH use cases, and exemplify the attainable performance gains via three simple mechanisms, namely, brute force (BF), sequential testing (ST) and codebook based (CB). Among the proposed mechanisms, BF demands the highest power consumption, while CB requires the highest-resolution phase shifters, thus tipping the scales in favor of ST. Finally, we show that the performance gains of ST over a rigid RF combining scheme increase with the number of receive antennas and energy transmitters deployment density.
With the increasing number of wireless communication systems and the demand for bandwidth, the wireless medium has become a congested and contested environment. Operating under such an environment brings several challenges, especially for military co
mmunication systems, which need to guarantee reliable communication while avoiding interfering with other friendly or neutral systems and denying the enemy systems of service. In this work, we investigate a novel application of Rate-Splitting Multiple Access(RSMA) for joint communications and jamming with a Multi-Carrier(MC) waveform in a multiantenna Cognitive Radio(CR) system. RSMA is a robust multiple access scheme for downlink multi-antenna wireless networks. RSMA relies on multi-antenna Rate-Splitting (RS) at the transmitter and Successive Interference Cancellation (SIC) at the receivers. Our aim is to simultaneously communicate with Secondary Users(SUs) and jam Adversarial Users(AUs) to disrupt their communications while limiting the interference to Primary Users(PUs) in a setting where all users perform broadband communications by MC waveforms in their respective networks. We consider the practical setting of imperfect CSI at transmitter(CSIT) for the SUs and PUs, and statistical CSIT for AUs. We formulate a problem to obtain optimal precoders which maximize the mutual information under interference and jamming power constraints. We propose an Alternating Optimization-Alternating Direction Method of Multipliers(AOADMM) based algorithm for solving the resulting non-convex problem. We perform an analysis based on Karush-Kuhn-Tucker conditions to determine the optimal jamming and interference power thresholds that guarantee the feasibility of problem and propose a practical algorithm to calculate the interference power threshold. By simulations, we show that RSMA achieves a higher sum-rate than Space Division Multiple Access(SDMA).
Rate-splitting multiple access (RSMA) is a promising technique for downlink multi-antenna communications owning to its capability of enhancing the system performance in a wide range of network loads, user deployments and channel state information at
the transmitter (CSIT) inaccuracies. In this paper, we investigate the achievable rate performance of RSMA in a multi-user multiple-input single-output (MU-MISO) network where only slow-varying statistical channel state information (CSI) is available at the transmitter. RSMA-based statistical beamforming and the split of the common stream is optimized with the objective of maximizing the minimum user rate subject to a sum power budget of the transmitter. Two statistical CSIT scenarios are investigated, namely the Rayleigh fading channels with only spatial correlations known at the transmitter, and the uniform linear array (ULA) deployment with only channel amplitudes and mean of phase known at the transmitter. Numerical results demonstrate the explicit max min fairness (MMF) rate gain of RSMA over space division multiple access (SDMA) in both scenarios. Moreover, we demonstrate that RSMA is more robust to the inaccuracy of statistical CSIT.
Dual-Functional Radar-Communication (DFRC) system is an essential and promising technique for beyond 5G. In this work, we propose a powerful and unified multi-antenna DFRC transmission framework, where an additional radar sequence is transmitted apar
t from communication streams to enhance radar beampattern matching capability, and Rate-Splitting Multiple Access (RSMA) is adopted to better manage the interference. RSMA relies on multi-antenna Rate-Splitting (RS) with Successive Interference Cancellation (SIC) receivers, and the split and encoding of messages into common and private streams. We design the message split and the precoders of the radar sequence and communication streams to jointly maximize the Weighted Sum Rate (WSR) and minimize the radar beampattern approximation Mean Square Error (MSE) subject to the per antenna power constraint. An iterative algorithm based on Alternating Direction Method of Multipliers (ADMM) is developed to solve the problem. Numerical results first show that RSMA-assisted DFRC achieves a better tradeoff between WSR and beampattern approximation than Space-Division Multiple Access (SDMA)-assisted DFRC with or without radar sequence, and other simpler radar-communication strategies using orthogonal resources. We also show that the RSMA-assisted DFRC frameworks with and without radar sequence achieve the same tradeoff performance. This is because that the common stream is better exploited in the proposed framework. The common stream of RSMA fulfils the triple function of managing interference among communication users, managing interference between communication and radar, and beampattern approximation. Therefore, by enabling RSMA in DFRC, the system performance is enhanced while the system architecture is simplified since there is no need to use additional radar sequence and SIC. We conclude that RSMA is a more powerful multiple access for DFRC.
Rate-Splitting Multiple Access (RSMA) is a flexible and robust multiple access scheme for downlink multi-antenna wireless networks. RSMA relies on multi-antenna Rate-Splitting (RS) at the transmitter and Successive Interference Cancellation (SIC) at
the receivers. In this work, we study the performance of RSMA under the practical important setup of imperfect Channel State Information at Transmitter (CSIT) originating from user mobility and latency in the network. First, we derive a lower bound on the ergodic sum-rate of RSMA for an arbitrary number of transmit antennas, number of users, user speeds and transmit power. Then, we study the power allocation between common and private streams and obtain a closed-form solution for the optimal power allocation that maximizes the obtained lower bound. The proposed power allocation greatly reduces precoder design complexity for RSMA. By Link-Level Simulations (LLS), we demonstrate that RSMA with the proposed power allocation is robust to degrading effects of user mobility and has significantly higher performance compared to conventional multi-user (massive) Multiple-Input Multiple-Output (MIMO) strategies. The work has important practical significance as results demonstrate that, in contrast to conventional multi-user (massive) MIMO whose performance collapse under mobility, RSMA can maintain reliable multi-user connectivity in mobile deployments.
In order to further exploit the potential of joint multi-antenna radar-communication (RadCom) system, we propose two transmission techniques respectively based on separated and shared antenna deployments. Both techniques are designed to maximize the
weighted sum rate (WSR) and the probing power at targets location under average power constraints at the antennas such that the system can simultaneously communicate with downlink users and detect the target within the same frequency band. Based on a Weighted Minimized Mean Square Errors (WMMSE) method, the separated deployment transmission is designed via semidefinite programming (SDP) while the shared deployment problem is solved by majorization-minimization (MM) algorithm. Numerical results show that the shared deployment outperforms the separated deployment in radar beamforming. The tradeoffs between WSR and probing power at target are compared among both proposed transmissions and two practically simpler dual-function implementations i.e., time division and frequency division. Results show that although the separated deployment enables spectrum sharing, it experiences a performance loss compared with frequency division, while the shared deployment outperforms both and surpasses time division in certain conditions.
Wireless power transfer (WPT) is an emerging paradigm that will enable using wireless to its full potential in future networks, not only to convey information but also to deliver energy. Such networks will enable trillions of future low-power devices
to sense, compute, connect, and energize anywhere, anytime, and on the move. The design of such future networks brings new challenges and opportunities for signal processing, machine learning, sensing, and computing so as to make the best use of the RF radiations, spectrum, and network infrastructure in providing cost-effective and real-time power supplies to wireless devices and enable wireless-powered applications. In this paper, we first review recent signal processing techniques to make WPT and wireless information and power transfer as efficient as possible. Topics include power amplifier and energy harvester nonlinearities, active and passive beamforming, intelligent reflecting surfaces, receive combining with multi-antenna harvester, modulation, coding, waveform, massive MIMO, channel acquisition, transmit diversity, multi-user power region characterization, coordinated multipoint, and distributed antenna systems. Then, we overview two different design methodologies: the model and optimize approach relying on analytical system models, modern convex optimization, and communication theory, and the learning approach based on data-driven end-to-end learning and physics-based learning. We discuss the pros and cons of each approach, especially when accounting for various nonlinearities in wireless-powered networks, and identify interesting emerging opportunities for the approaches to complement each other. Finally, we identify new emerging wireless technologies where WPT may play a key role -- wireless-powered mobile edge computing and wireless-powered sensing -- arguing WPT, communication, computation, and sensing must be jointly designed.
Reconfigurable intelligent surfaces (RISs) are an emerging technology for future wireless communication. The vast majority of recent research on RIS has focused on system level optimizations. However, developing straightforward and tractable electrom
agnetic models that are suitable for RIS aided communication modeling remains an open issue. In this paper, we address this issue and derive communication models by using rigorous scattering parameter network analysis. We also propose new RIS architectures based on group and fully connected reconfigurable impedance networks that can adjust not only the phases but also the magnitudes of the impinging waves, which are more general and more efficient than conventional single connected reconfigurable impedance network that only adjusts the phases of the impinging waves. In addition, the scaling law of the received signal power of an RIS aided system with reconfigurable impedance networks is also derived. Compared with the single connected reconfigurable impedance network, our group and fully connected reconfigurable impedance network can increase the received signal power by up to 62%, or maintain the same received signal power with a number of RIS elements reduced by up to 21%. We also investigate the proposed architecture in deployments with distance-dependent pathloss and Rician fading channel, and show that the proposed group and fully connected reconfigurable impedance networks outperform the single connected case by up to 34% and 48%, respectively.
In this paper, we study the multiple-input and multiple-output (MIMO) wireless power transfer (WPT) system so as to enhance the output DC power of the rectennas. To that end, we revisit the rectenna nonlinearity considering multiple receive antennas.
Two combining schemes for multiple rectennas at the receiver, DC and RF combinings, are modeled and analyzed. For DC combining, we optimize the transmit beamforming, adaptive to the channel state information (CSI), so as to maximize the total output DC power. For RF combining, we compute a closed-form solution of the optimal transmit and receive beamforming. In addition, we propose a practical RF combining circuit using RF phase shifter and RF power combiner and also optimize the analog receive beamforming adaptive to CSI. We also analytically derive the scaling laws of the output DC power as a function of the number of transmit and receive antennas. Those scaling laws confirm the benefits of using multiple antennas at the transmitter or receiver. They also highlight that RF combining significantly outperforms DC combining since it leverages the rectenna nonlinearity more efficiently. Two types of performance evaluations, based on the nonlinear rectenna model and based on realistic and accurate rectenna circuit simulations, are provided. The evaluations demonstrate that the output DC power can be linearly increased by using multiple rectennas at the receiver and that the relative gain of RF combining versus DC combining in terms of the output DC power level is very significant, of the order of 240% in a one-transmit antenna ten-receive antenna setup.
Rate-Splitting Multiple Access (RSMA), relying on multi-antenna Rate-Splitting (RS) techniques, has emerged as a powerful strategy for multi-user multi-antenna systems. In this paper, RSMA is introduced as a unified multiple access for multi-antenna
radar-communication (RadCom) system, where the base station has a dual communication and radar capability to simultaneously communicate with downlink users and probe detection signals to azimuth angles of interests. Using RS, messages are split into common and private parts, then encoded into common and private streams before being precoded and transmitted. We design the message split and the precoders for this RadCom system such that the Weighted Sum Rate (WSR) is maximized and the transmit beampattern is approximated to the desired radar beampattern under an average transmit power constraint at each antenna. We then propose a framework based on Alternating Direction Method of Multipliers (ADMM) to solve the complicated non-convex optimization problem. Results highlight the benefits of RSMA to unify RadCom transmissions and to manage the interference among radar and communications, over the conventional Space-Division Multiple Access (SDMA) technique.
