Sum-Rate Maximization Schemes for &lt;inline-formula&gt; &lt;tex-math notation="LaTeX"&gt;$K$&lt;/tex-math&gt;&lt;/inline-formula&gt;-User MISO Interference Channels With a Cognitive Relay

Shin, Hun Min; Lee, Sang-Rim; Kong, Han-Bae; Lee, Inkyu

doi:10.1109/twc.2015.2454496

Cited by 4 publications

(4 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(29) obtained by applying MM in step 2 has a ascent property and converges to a finite value [28]. Also, the sequence of the objective values of (24), the design problem, has the same property and converges to a finite value [28]. The interested reader may see [28] for the conditions in which MM and AO converge to stationary points.…”

Section: The Proposed Methods For Max-min Throughput Optimization In ...mentioning

confidence: 99%

“…Next, by using matrix inversion lemma and Sylvester's determinant property i.e., det(I + AB) = det(I + BA) and by substituting ( 15) in ( 14), ( 14) can be rewritten as [24] R…”

Section: B Uplink Phasementioning

confidence: 99%

“…Similar to the problem in (24), the problem in (47) is non-convex due to the coupled design variables in the objective function and in the constraints C 3 and C 4 . Therefore, we employ AO with partitioning [τ ⋮ Q, S, S].…”

Section: A Sum Throughput Maximizationmentioning

confidence: 99%

“…Fig. 4 shows the values of the max-min throughput, i.e., equivalently the objective function of (24), versus the number of outer iterations (see Table I), for different numbers of antennas at the HAP and the users (M). As expected, the max-min throughput increases at each iteration.…”

Section: A Convergence Of the Proposed Algorithmmentioning

confidence: 99%

See 3 more Smart Citations

Throughput Maximization for Multi-Cluster WPCN: User Cooperation, Non-linearity, and Channel Uncertainty

Rezaei¹,

Masjedi²,

Naghsh³

2021

Preprint

View full text Add to dashboard Cite

In this paper, we study a general multi-cluster wireless powered communication network (WPCN) with user cooperation under harvest-then-transmit (HTT) protocol where the hybrid access point (HAP) as well as each user is equipped with multiple antennas. In the downlink phase of HTT, the HAP employs beamforming to transfer energy to the users. In the uplink phase, users in each cluster transmit their signals to the HAP and to their cluster heads (CHs). Afterward, the CHs first relay the signals of their cluster users and then transmit their own information signals to the HAP. The aim is to design the energy beamforming (EB) matrix, transmit covariance matrices of the users and time allocations among energy transfer and cooperation phases in order to optimize the max-min and sum throughputs of the network. The corresponding maximization problems are non-convex and NP-hard in general. We devise iterative algorithm based on alternating optimization (AO) and then the minorization-maximization (MM) technique is used to deal with the non-convex sub-problems with respect to (w.r.t.) the EB and covariance matrices in each iteration. We recast the resulting sub-problems as a convex second order cone programming (SOCP) and quadratic constraint quadratic programming (QCQP) for the maxmin and sum throughput maximization problems, respectively. We also consider imperfect channel state information (CSI) at the HAP and CHs and non-linearity in energy harvesting (EH) circuits. Numerical examples show that the proposed cooperative method can effectively improve the achievable throughput in the multi-cluster wireless powered communication under various setups.

show abstract

Section: The Proposed Methods For Max-min Throughput Optimization In ...mentioning

confidence: 99%

“…Next, by using matrix inversion lemma and Sylvester's determinant property i.e., det(I + AB) = det(I + BA) and by substituting ( 15) in ( 14), ( 14) can be rewritten as [24] R…”

Section: B Uplink Phasementioning

confidence: 99%

Section: A Sum Throughput Maximizationmentioning

confidence: 99%

Section: A Convergence Of the Proposed Algorithmmentioning

confidence: 99%

See 2 more Smart Citations