Power Distribution for an OFDM-Based Dual-Function Radar-Communication Sensor

Abstract

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 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 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, which highlights the importance of considering the effect of CSs' random magnitudes on DFRC signal design.

Min-Max Latency Optimization Based on Sensed Position State Information in Internet of Vehicles

“…where κ = √ N t N v is the array gain factor, p k,i represents the transmit power for the kth vehicle, and z C (t) ∼ CN 0, σ 2 C is the complex Gaussian noise [19], [23].…”

“…where κ = √ N t N r is the array gain factor, τ k,i = 2d k,i /c represents the delay between echo signal and transmit signal, and z k,i (t) ∼ CN 0, σ 2 R 1 Nr denotes the complex Gaussian noise [19], [23].…”

Performance Analysis of Data Transmission for Joint Radar and Communication Systems

“…In [13], a method of utilizing an orthogonal frequency division multiplexing (OFDM) signal to operate the functions of radar and communication systems is applied to the problem of radar and communication coexistence. e similar strategy can also found in [14,15,20]. Embed communication information into the radar waveform so that radar and communication functions can be realized by one common signal.…”

Joint Uplink Power Allocation Method in Wireless Communication and Positioning Integrated System

“…Because there is mutual interference between C-Users and P-Users in the MS-NOMA signal, the optimal water-filling over the sub-carriers algorithm, which is usually used in the Orthogonal Frequency Division Multiplexing (OFDM) power allocation problem, cannot be used to solve our problem [24]. In [25], considering the energy efficiency of users, a joint power and bandwidth allocation method with clone immune algorithm is proposed.…”

“…s f + m f p G m p f + m f p sin 2 πfDT p df = ,n T c sin c 2 f + m f p − n f c T c sin c 2 f T p sin 2 πfDT p df G + m f p − n f c T c sin 2 f T p df ≈ T c T p D 2 N n=1 h c,n 2 P c,n sin 2 π m − n G × (Gm−n+1) (Gm−n−1) sin c 2 f + m f p − n f c T c sin 2 f T p df ≈ T p D 2 N n=1 h c,n 2 P c,n sin 2 n G π(24) …”