Joint Location and Transmit Power Optimization for NOMA-UAV Networks via Updating Decoding Order
Summary (2 min read)
Introduction
- Recently, unmanned aerial vehicles (UAVs) have been widely used as carrying platforms of base stations in wireless communications [1], [2].
- A resource allocation algorithm for NOMA networks was proposed by Chang et al. to improve the secrecy energy efficiency [9].
A. System Model
- Consider a NOMA-UAV network with one UAV and K ground users.
- The superimposed information is transmitted from the UAV to the users via NOMA.
- Θi = arcsin ( H di ) represents the elevation angle between the UAV and Ui.
- According to an extensive survey for UAV channel modeling [20], when the UAV is located high enough (e.g., 120m), the LoS probability is approximate to 1.
- Ui needs to decode the messages from Ui+1 to UK and removes them from the superposed signal.
B. Problem Formulation
- Thus, approximation will be used in the next section.
- Different from the fixed SIC order [14], the decoding order is updated after each iteration according the ranking of channel gains in this letter, with the stronger user decoded later.
- The problem (11) is difficult to solve due to its nonconvexity.
- Thus, the authors propose a scheme to optimize the location and power alternately via successive convex optimization.
B. Location Optimization
- 4 C. Iterative Algorithm Based on Section III-A and Section III-B, (11) can be solved iteratively using Algorithm 1.
- In Step 3, the decoding order is updated according to the optimized UAV location.
- Algorithm 1 Iterative Algorithm for (11) Initialization:.
- Set the geometric center of users as the starting location L0 = ∑K i=1 qi/K. Similarly, the authors have R(Pr+1,Lr) ≤ R(Pr+1,Lr+1). (32) Step 3 in Algorithm 1 can always adjust the current decoding order in each iteration, and the sum rate will not decrease.
D. Analysis of the Last Decoding User
- The last decoding user is the closest one to the UAV and last decoded via SIC.
- The last decoding user is determined when the UAV location is initialized, and will not change during iterations, which is proved in Proposition 2. (33) Proposition 2: The last decoding user is not changed during iterations and the optimal UAV location is getting closer to this user with increasing transmit power.
- In order to increase the sum rate, the UAV location will approach U1.
- The user U1 always has the best channel condition and the last decoding user is not changed.
- The authors can observe that the increase of power has a much greater influence on R1 from (37).
IV. SIMULATION RESULTS AND DISCUSSION
- From the result, the authors can observe that the optimal UAV location becomes closer to the last decoding order when Psum increases, which is consistent with the conclusion from Proposition 2.
- The result shows that the sum rate increases when the transmit power of the UAV is higher.
- Average sum rate comparison of the proposed scheme and the scheme in [14].
- Furthermore, the sum rate decreases as the rate threshold increases.
- The result shows PC can effectively improve the rate performance for both schemes.
V. CONCLUSIONS
- The authors have jointly optimized the UAV location and transmit power in NOMA-UAV networks via updating decoding order, which can be divided into two sub-problems.
- The non-convex sub-problems are approximated into convex ones, and an iterative algorithm is proposed to optimize the location and power alternately via successive convex optimization.
- In addition, the closer performance of the algorithm has been further analyzed.
- Finally, simulation results have been shown to verify the effectiveness of the proposed scheme over benchmarks.
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"Joint Location and Transmit Power O..." refers background in this paper
...INTRODUCTION RECENTLY, unmanned aerial vehicles (UAVs) have been widely used as carrying platforms of base stations in wireless communications [1], [2]....
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"Joint Location and Transmit Power O..." refers background in this paper
...INTRODUCTION RECENTLY, unmanned aerial vehicles (UAVs) have been widely used as carrying platforms of base stations in wireless communications [1], [2]....
[...]
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Frequently Asked Questions (9)
Q2. how can i optimize a ground receiver?
To maximize the sum rate of ground users via jointly optimizing L and P based on (7) and (8), the optimization problem can be formulated asmax L,P ∑ i∈K log2 (1 + SINRi) (10a)s.t. SINRi ≥ γi, (10b) 0 < P1 ≤ · · · ≤ Pi ≤ · · · ≤ PK , (10c)∑Ki=1 Pi ≤ Psum.
Q3. What is the transmit power of the UAV?
The received signal at Ui is given bysi = hi ∑Kj=1 zj + ni, i ∈ K, (1)where hi represents the channel coefficient from the UAV to Ui, and ni denotes the additive white Gaussian noise (AWGN) at Ui. zj is the message for Uj with |zj |2 = ajPsum = Pj , where Psum is the sum transmit power of UAV, aj is the power coefficient of Uj , and Pj is the transmit power for Uj .
Q4. Why does the sum rate increase when the threshold is lower?
This is because that the lower threshold provides more degree of freedom for the power allocation and location selection, and thus the UAV can allocate more transmit power for the interference-free (last decoding) user, which leads to higher throughput.
Q5. How many users are in the proposed scheme?
The authors assume all the users are randomly deployed in a square area of 400 × 400 m2, and the authors set η = (1, 1, 1) bit/s/Hz for all the schemes.
Q6. What is the probability of UAV-to-ground links?
The distance between the UAV and Ui can be expressed asdi = √ H2 + ∥qi − L∥2. (2)The probability of UAV-to-ground links dominated by lineof-sight (LoS) can be expressed asPLoSi = 11 + a0 exp(−b0(θi − a0)) , (3)where a0 and b0 denote the environment constants.
Q7. What is the objective value of r?
combining (31) with (32), the authors prove the objective value of (11) is non-decreasing after each iteration, and is upper bounded by a finite value.
Q8. what is the snr of the uav?
the authors fix the UAV location and (11) becomesmax P ∑ i∈K log2 (1 + SINRi) (12a)s.t. Pi i−1∑ k=1 Pk + σ2 |hi|2 ≥ γi, i ∈ K\\{1}, (12b)(11c), (11d), (11e). (12c)(12c) is convex. (12b) is non-convex and its left-hand-side can be replaced by Ri.
Q9. What is the way to prove that the last decoding user is the closest to the U?
(33)Proposition 2: The last decoding user is not changed during iterations and the optimal UAV location is getting closer to this user with increasing transmit power.