Rate-Splitting Multiple Access for Non-Orthogonal Unicast Multicast: An Experimental Study

Non-orthogonal unicast multicast (NOUM) is a variant of multi-antenna multi-user communications where the users desire a shared message (multicast) in addition to their respective unique messages (unicast). The multicast rate is capped in many emerging NOUM applications, such as live-event broadcasting, location-based services and vehicular communications. Given this constraint, we experimentally show that when the user channels are highly correlated, Rate-Splitting Multiple Access (RSMA)-based NOUM can meet the multicast rate while supporting a larger unicast sum rate than conventional multi-user linear precoding (MULP)-based NOUM.

Multi-functional OFDM Signal Design for Integrated Sensing, Communications, and Power Transfer

The wireless domain is witnessing a flourishing of integrated systems, e.g. (a) integrated sensing and communications, and (b) simultaneous wireless information and power transfer, due to their potential to use resources (spectrum, power) judiciously. Inspired by this trend, we investigate integrated sensing, communications and powering (ISCAP), through the design of a wideband OFDM signal to power a sensor while simultaneously performing target-sensing and communication. To characterize the ISCAP performance region, we assume symbols with non-zero mean asymmetric Gaussian distribution (i.e., the input distribution), and optimize its mean and variance at each subcarrier to maximize the harvested power, subject to constraints on the achievable rate (communications) and the average side-to-peak-lobe difference (sensing). The resulting input distribution, through simulations, achieves a larger performance region than that of (i) a symmetric complex Gaussian input distribution with identical mean and variance for the real and imaginary parts, (ii) a zero-mean symmetric complex Gaussian input distribution, and (iii) the superposed power-splitting communication and sensing signal (the coexisting solution). In particular, the optimized input distribution balances the three functions by exhibiting the following features: (a) symbols in subcarriers with strong communication channels have high variance to satisfy the rate constraint, while the other symbols are dominated by the mean, forming a relatively uniform sum of mean and variance across subcarriers for sensing; (b) with looser communication and sensing constraints, large absolute means appear on subcarriers with stronger powering channels for higher harvested power. As a final note, the results highlight the great potential of the co-designed ISCAP system for further efficiency enhancement.

A Prototype Implementation of Rate Splitting Multiple Access using Software-Defined Radios

For interference-limited multi-user communications, many papers have demonstrated, in theory, the effectiveness of Rate Splitting Multiple Access (RSMA) in suppressing the interference and achieving better outcomes (w.r.t spectral efficiency, fairness etc.) than the conventional Space Division Multiple Access (SDMA) used in present-day standards. However, an experimental demonstration of RSMA's benefits is missing in the literature. In this paper, we address this gap by realizing an RSMA prototype using software-defined radios. For the two-user multiple-input single-output (MISO) scenario, we measure the throughput performance of SDMA and RSMA in nine different scenarios that vary in terms of the channel pathloss and spatial correlation experienced by the users. Emulating perfect channel state information (CSI) at the transmitter through unquantized CSI feedback, we observe that RSMA achieves a higher sum throughput (upto 57%) and more fairness than SDMA when the user channels have high spatial correlation. Similarly, emulating imperfect CSI through quantized feedback, RSMA - along with the above trend - also experiences a smaller sum throughput loss, relative to the unquantized case (37%, on average, as opposed to 44% for SDMA). These outcomes are consistent with theoretical predictions, and demonstrate the feasibility and potential of RSMA for next generation wireless networks (e.g., 6G).

Input Distribution Optimization in OFDM Dual-Function Radar-Communication Systems

Orthogonal frequency division multiplexing (OFDM) has been widely adopted in dual-function radar-communication (DFRC) systems, where radar and communications are performed simultaneously with a common signal. However, with random communication symbols (CS) in DFRC, the transmit signal has a random ambiguity function that affects the radar's range-velocity estimation performance, whose influence is remained uncovered. Hence, this paper focuses on minimizing the outlier probability (OP) -- the probability of incorrectly estimating a target's range-velocity bin -- in OFDM DFRC w.r.t the CS probability distribution (i.e., the \emph{input distribution}). Conditioned on the CSs, the OP only depends on the CS magnitudes. Hence, we consider the following two schemes for the above optimization: CSs with (1) constant magnitude (phase shift keying input), and (2) random magnitude (Gaussian input). For (1), the problem reduces to the familiar power allocation design across OFDM's subcarriers and symbols, with uniform power allocation across subcarriers and a \emph{windowed} power allocation across symbols being near-optimal. For (2), the mean and variance of the Gaussian distribution at each subcarrier is optimized, with an additional communication constraint to avoid the zero-variance solution where no CSs are carried. We observe that subcarriers with strong communication channels feature strong variance (i.e., favour communications) while the others are characterized by a strong mean (favouring radar). However, the overall power allocation (i.e., the sum of mean and variance) across the OFDM subcarriers and symbols is similar to (1). Simulations show that CSs with random magnitudes degrade the sensing performance, but can be compensated significantly with the proposed input distribution optimization.

Sensing using Coded Communications Signals

A key challenge for a common waveform for Integrated Sensing and Communications (ISAC) - widely seen as an attractive proposition to achieve high performance for both functionalities, while efficiently utilizing available resources -- lies in leveraging information-bearing channel-coded communications signals (c.c.s) for sensing. In this paper, we investigate the sensing performance of c.c.s in (multi-user) interference-limited operation, and show that it is limited by sidelobes in the range-Doppler map, whose form depends on whether the c.c.s modulates a single-carrier or OFDM waveform. While uncoded communications signals -- comprising a block of $N$ i.i.d zero-mean symbols -- give rise to asymptotically (i.e., as $N \rightarrow \infty$) zero sidelobes due to the law of large numbers, it is not obvious that the same holds for c.c.s, as structured channel coding schemes (e.g., linear block codes) induce dependence across codeword symbols. In this paper, we show that c.c.s also give rise to asymptotically zero sidelobes -- for both single-carrier and OFDM waveforms -- by deriving upper bounds for the tail probabilities of the sidelobe magnitudes that decay as $\exp( - O($code rate $\times$ block length$))$. This implies that for any code rate, c.c.s are effective sensing signals that are robust to multi-user interference at sufficiently large block lengths, with negligible difference in performance based on whether they modulate a single-carrier or OFDM waveform. We verify the latter implication through simulations, where we observe the sensing performance (characterized by the detection and false-alarm probabilities) of a QPSK-modulated c.c.s (code rate = 120/1024, block length = 1024 symbols) to match that of a comparable interference-free FMCW waveform even at high interference levels (signal-to-interference ratio of -11dB), for both single-carrier and OFDM waveforms.